{"id":127,"date":"2026-03-03T20:18:06","date_gmt":"2026-03-03T20:18:06","guid":{"rendered":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/?post_type=chapter&#038;p=127"},"modified":"2026-03-03T20:31:14","modified_gmt":"2026-03-03T20:31:14","slug":"polygenic-risks-of-addictions","status":"publish","type":"chapter","link":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/chapter\/polygenic-risks-of-addictions\/","title":{"raw":"Polygenic Risks of Addictions","rendered":"Polygenic Risks of Addictions"},"content":{"raw":"<div class=\"flex flex-col text-sm pb-25\"><article class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" data-turn-id=\"3a739a8f-cea2-4dd5-9078-314a444f686c\" data-testid=\"conversation-turn-82\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\r\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm\/main:[--thread-content-margin:--spacing(6)] @w-lg\/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\r\n<div class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\">\r\n<div class=\"flex max-w-full flex-col grow\">\r\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"8c1c2e57-c234-41ad-a31f-1f650379e720\" data-message-model-slug=\"gpt-5-2-thinking\">\r\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\r\n<div class=\"markdown prose dark:prose-invert w-full wrap-break-word light markdown-new-styling\">\r\n<h3 data-start=\"0\" data-end=\"22\">Reading Objectives<\/h3>\r\n<p data-start=\"24\" data-end=\"73\">By the end of this module, you should be able to:<\/p>\r\n\r\n<ol data-start=\"75\" data-end=\"1378\">\r\n \t<li data-start=\"75\" data-end=\"326\">\r\n<p data-start=\"78\" data-end=\"326\"><strong data-start=\"78\" data-end=\"125\">Understand Molecular Genetics Fundamentals:<\/strong> Grasp the basic structure and function of DNA, genes, and genomes, and recognize the role of genetic variations such as Single Nucleotide Polymorphisms (SNPs) in influencing behaviors and addiction.<\/p>\r\n<\/li>\r\n \t<li data-start=\"327\" data-end=\"568\">\r\n<p data-start=\"330\" data-end=\"568\"><strong data-start=\"330\" data-end=\"381\">Explain Genome-Wide Association Studies (GWAS):<\/strong> Describe the methodology and purpose of GWAS, including study designs like case-control and cohort studies, and understand how GWAS identify genetic variants associated with addiction.<\/p>\r\n<\/li>\r\n \t<li data-start=\"569\" data-end=\"799\">\r\n<p data-start=\"572\" data-end=\"799\"><strong data-start=\"572\" data-end=\"610\">Comprehend Polygenic Scores (PGS):<\/strong> Understand how PGS are derived from GWAS data, their applications in predicting addiction risk, and the limitations associated with their use, especially concerning population diversity.<\/p>\r\n<\/li>\r\n \t<li data-start=\"800\" data-end=\"1167\">\r\n<p data-start=\"803\" data-end=\"1167\"><strong data-start=\"803\" data-end=\"871\">Identify and Address Ethical Considerations in Genetic Research:<\/strong> Recognize the importance of data privacy, informed consent, and the responsible use of genetic data to prevent scientific racism and ensure equitable research practices. Implement best practices for using population descriptors and appreciate the significance of genetic diversity in research.<\/p>\r\n<\/li>\r\n \t<li data-start=\"1168\" data-end=\"1378\">\r\n<p data-start=\"1171\" data-end=\"1378\"><strong data-start=\"1171\" data-end=\"1224\">Explore the ABCD Study\u2019s Genetic Data Collection:<\/strong> Learn how the ABCD Study collects, analyzes, and utilizes genetic data to investigate the interplay between genetics, brain development, and addiction.<\/p>\r\n<\/li>\r\n<\/ol>\r\n\r\n<hr data-start=\"1380\" data-end=\"1383\" \/>\r\n\r\n<h3 data-start=\"1385\" data-end=\"1398\">Key Terms<\/h3>\r\n<ul data-start=\"1400\" data-end=\"2667\">\r\n \t<li data-start=\"1400\" data-end=\"1617\">\r\n<p data-start=\"1402\" data-end=\"1617\"><strong data-start=\"1402\" data-end=\"1434\">DNA (Deoxyribonucleic Acid):<\/strong> The molecule that carries genetic information in all living organisms, structured as a double helix composed of four bases: adenine (A), thymine (T), cytosine (C), and guanine (G).<\/p>\r\n<\/li>\r\n \t<li data-start=\"1618\" data-end=\"1746\">\r\n<p data-start=\"1620\" data-end=\"1746\"><strong data-start=\"1620\" data-end=\"1629\">Gene:<\/strong> A specific segment of DNA that encodes instructions for building proteins, serving as the basic units of heredity.<\/p>\r\n<\/li>\r\n \t<li data-start=\"1747\" data-end=\"1873\">\r\n<p data-start=\"1749\" data-end=\"1873\"><strong data-start=\"1749\" data-end=\"1760\">Genome:<\/strong> The complete set of genetic material within an organism, encompassing all of its genes and non-coding regions.<\/p>\r\n<\/li>\r\n \t<li data-start=\"1874\" data-end=\"2124\">\r\n<p data-start=\"1876\" data-end=\"2124\"><strong data-start=\"1876\" data-end=\"1917\">Single Nucleotide Polymorphism (SNP):<\/strong> The most common type of genetic variation among people, involving a change of a single nucleotide in the DNA sequence, occurring approximately once every 300 bases (Genetic Science Learning Center, 2016).<\/p>\r\n<\/li>\r\n \t<li data-start=\"2125\" data-end=\"2384\">\r\n<p data-start=\"2127\" data-end=\"2384\"><strong data-start=\"2127\" data-end=\"2168\">Genome-Wide Association Study (GWAS):<\/strong> A research approach that involves scanning entire genomes of many individuals to identify genetic variants, particularly SNPs, associated with specific traits or diseases, such as addiction (Baurley et al., 2016).<\/p>\r\n<\/li>\r\n \t<li data-start=\"2385\" data-end=\"2667\">\r\n<p data-start=\"2387\" data-end=\"2667\"><strong data-start=\"2387\" data-end=\"2413\">Polygenic Score (PGS):<\/strong> A quantitative index that aggregates the tiny effects of many genetic variants (typically SNPs) identified in GWAS to summarize inherited liability for a trait. A PGS shifts group-level probabilities; it does not determine outcomes for any one person.<\/p>\r\n<\/li>\r\n<\/ul>\r\n\r\n<hr data-start=\"2669\" data-end=\"2672\" \/>\r\n\r\n<h2 data-start=\"2674\" data-end=\"2692\">I. Introduction<\/h2>\r\n<p data-start=\"2694\" data-end=\"3360\">In 2003, the Human Genome Project achieved a near-complete sequence of the human genome after 13 years of relentless effort and a staggering investment of around \u00a32 billion. Just two decades later, advancements in sequencing technologies have accelerated this process exponentially. By 2022, the Wellcome Sanger Institute showcased the power of modern sequencing by producing a human genome every 12 minutes\u2014a stark contrast to the painstaking pace of the past (Wellcome Sanger Institute, 2022). Today, entire genomes can be sequenced in less than an hour, revolutionizing our understanding of genetics and opening new frontiers in biology, medicine, and healthcare.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<div class=\"z-0 flex min-h-[46px] justify-start\"><\/div>\r\n<div class=\"mt-3 w-full empty:hidden\">\r\n<div class=\"text-center\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/article><\/div>\r\n<div class=\"pointer-events-none h-px w-px absolute bottom-0\" aria-hidden=\"true\" data-edge=\"true\">\r\n\r\n[caption id=\"attachment_128\" align=\"aligncenter\" width=\"672\"]<img class=\"wp-image-128 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS.jpg\" alt=\"Infographic showing the DNA sequencing workflow from DNA extraction to sequencing and computational analysis\" width=\"672\" height=\"378\" \/> Figure 1. DNA sequencing workflow. Courtesy: National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH), 2023. Public domain. Source: genome.gov (DNA Sequencing Fact Sheet).[\/caption]\r\n\r\nIn the previous module, we developed an intuition for heritability and foundational methodologies in behavioral genetics. We now turn to the molecular structures that underlie heritability. Whereas an atom is the smallest unit of an element (carbon, hydrogen, oxygen, etc.) that keeps its chemical identity, molecules are two or more atoms held together by chemical bonds (water, carbon dioxide, etc.). Our focus is the biomolecules of genetics: DNA, RNA, and proteins.\r\n\r\nIn this module, we explore molecular genetics, genome-wide association studies (GWAS), and polygenic scores (PGS) to understand how these technologies help scientists uncover complex relationships between genes and behavior, particularly in the context of addiction.\r\n\r\nAdditionally, we consider ethical challenges and responsible approaches to behavioral genetics in addiction science.\r\n<h2>II. Molecular Genetics<\/h2>\r\nUnderstanding a few molecular genetics basics helps us interpret how DNA variation can relate to behavior, including addiction. This section introduces DNA, genes, chromosomes, the genome, and common genetic variation called single-nucleotide polymorphisms (SNPs).\r\n<h3>DNA and Genes<\/h3>\r\nDNA (deoxyribonucleic acid) carries the biological instructions for life. It consists of two complementary strands twisted into a double helix, like a spiral ladder. The sugar-phosphate backbones form the sides, and paired bases form the rungs. DNA uses four bases: A (adenine), T (thymine), C (cytosine), and G (guanine). A pairs with T, and C pairs with G. The order of the bases, or sequence, functions like biological \u201ctext\u201d that cells read to build proteins, which perform most life functions.\r\n\r\nWhen a cell needs a protein, it copies a specific stretch of DNA into RNA (ribonucleic acid). RNA is usually single-stranded and uses U (uracil) instead of T. The short-lived copy, messenger RNA (mRNA), is then read in three-base codons to assemble a protein.\r\n\r\nA gene is a stretch of DNA whose sequence is transcribed into RNA, typically to make a protein. Genes are passed from parents to offspring and influence traits such as eye color and disease susceptibility. A chromosome is a long, tightly packaged DNA molecule containing many genes, wrapped around proteins. Chromosomes are located in the cell nucleus in most cells. Humans have 23 pairs of chromosomes (46 total), with one chromosome in each pair inherited from each parent. The genome is the complete DNA sequence across all chromosomes in a person, totaling about 3 billion base pairs in humans.\r\n\r\n[caption id=\"attachment_129\" align=\"aligncenter\" width=\"624\"]<img class=\"wp-image-129 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA.png\" alt=\"Here\u2019s a clean, ready-to-paste image-generation prompt that matches the \u201cConstructing a PGS\u201d steps (select SNPs + LD pruning, assign GWAS weights, compute weighted sum, standardize to z-score\/percentile).&#096;&#096;&#096;text Create a high-clarity, textbook-ready vector infographic (16:9 landscape, white background, 4K resolution) titled: \u201cHow a Polygenic Score (PGS) is Built\u201d Design style: - Clean, minimal, modern scientific infographic - Thick, consistent line icons, high contrast, large readable labels - Use a restrained palette (navy\/blue + gray + one accent), no gradients, no clutter - All text must be legible when viewed on a lecture slide Layout: A left-to-right 5-step pipeline with rounded rectangles and arrows between steps. Each step has (1) a simple icon, (2) a bold step label, and (3) 1\u20132 short explanatory lines. Step 1 (Input): \u201cGWAS summary statistics\u201d - Icon: Manhattan-style bars or a simple bar chart - Text: \u201cEffect sizes (\u03b2) and p-values from a GWAS\u201d Step 2: \u201cSelect SNPs + LD clumping\/pruning\u201d - Icon: a cluster of dots with some removed, or linked nodes with a few crossed out - Text: \u201cChoose SNPs by p-value threshold (strict or more inclusive)\u201d - Text: \u201cRemove highly correlated SNPs (LD pruning) to avoid double-counting\u201d Step 3: \u201cAssign weights (\u03b2) and read genotypes (0\/1\/2)\u201d - Icon: a small table with columns labeled \u201cGenotype (0,1,2)\u201d and \u201cWeight (\u03b2)\u201d - Text: \u201cWeight each SNP using GWAS effect size (\u03b2 or log odds ratio)\u201d - Text: \u201cCount risk alleles per SNP: 0, 1, or 2\u201d Step 4: \u201cCompute the raw PGS (weighted sum)\u201d - Icon: calculator or summation symbol - Show the formula prominently inside the box: \u201cPGS\u1d62 = \u03a3\u2c7c (\u03b2\u2c7c \u00d7 G\u1d62\u2c7c)\u201d - Small note under formula: \u201cAdd up many tiny genetic nudges\u201d Step 5 (Output): \u201cStandardize for interpretation\u201d - Icon: a speedometer gauge + a small bell curve - Text: \u201cConvert to z-score (standard deviations) or percentile\u201d - Text: \u201cExample: 90th percentile = higher than 90% of the sample\u201d Footer (single line, centered): \u201cFrom many small SNP effects to one standardized score: PGS = \u03a3(allele count \u00d7 GWAS weight)\u201d Constraints: - No extra panels, no distracting background patterns - No logos, no trademarks - Prioritize clarity and readability over decorative detail \" width=\"624\" height=\"338\" \/> Figure 2. Gene\u2013DNA\u2013chromosome relationship and protein production. <strong>Illustration by Laura Olivares Bold\u00fa \/ Wellcome Connecting Science (<a href=\"https:\/\/www.yourgenome.org\/theme\/what-is-a-chromosome\/\">YourGenome<\/a>), CC BY 4.0.<\/strong>[\/caption]\r\n<h3>Gene Expression<\/h3>\r\nGene expression is the process by which information from a gene is used to create a functional product, usually a protein. Not all genes are active at all times. Gene regulation controls when and how much of a protein is made, ensuring that proteins are produced as needed by the cell.\r\n<h3>Genotype vs Phenotype<\/h3>\r\nThe genotype is the genetic makeup of an individual, meaning the specific set of genes they carry. The phenotype is the observable traits or characteristics that result from the interaction of the genotype with the environment. For example, a gene may influence a person's tendency toward impulsivity, which, combined with environmental factors, can affect behavior.\r\n<h3>Alleles: Different Versions of a Gene<\/h3>\r\nGenes often exist in slightly different forms known as alleles. Each individual inherits two alleles for every gene, one from each parent. While many alleles have little or no influence on observable traits, some can lead to differences in characteristics, such as eye color, or predispositions to certain behaviors or diseases. For example, a gene that influences eye color may have one allele for blue eyes and another for brown eyes.\r\n\r\nThe combination of alleles you carry can influence how genes are expressed and, in some cases, how you respond to your environment, medications, and other external factors. Additionally, Single Nucleotide Polymorphisms (SNPs) represent a type of allelic variation where a single nucleotide differs between alleles. Allelic variation is therefore an important concept in understanding genetic diversity and how traits and health outcomes vary between individuals and across populations.\r\n\r\n[caption id=\"attachment_131\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-131 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-1024x683.png\" alt=\"Two blue homologous chromosomes with matching colored bands connected to labels for example traits: lactose tolerance, taste sensitivity (PTC), caffeine metabolism, handedness, and ear lobe attachment.\" width=\"1024\" height=\"683\" \/> Figure 3. Example of traits mapped to corresponding regions on homologous chromosomes, illustrating that the same loci appear in the same positions on both chromosome copies. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.[\/caption]\r\n\r\n<\/div>\r\n<h3>Genotype and Phenotype<\/h3>\r\nThe term genotype refers to one\u2019s genetic makeup (the specific alleles one carries), whereas phenotype refers to observable characteristics or outcomes (which result from the interaction of genotype with environment). For instance, a person might have a genetic allele that increases impulsivity (genotype), but whether that manifests in behavior (phenotype) could depend on upbringing, stress, and other environmental factors.\r\n\r\nIt\u2019s crucial to remember that for complex traits like addiction vulnerability, there is no single \u201caddiction gene.\u201d Instead, many genes each make small contributions. In other words, such traits are polygenic (influenced by multiple genes).\r\n<h3>Single-Nucleotide Polymorphisms (SNPs)<\/h3>\r\nOne common type of genetic variation is the single nucleotide polymorphism (SNP), essentially a single \u201cletter\u201d difference in the DNA sequence between individuals. For example, at a particular position in the genome one person might have an A, while another has a G. Each SNP is like a single-character typo or variation in a very long book.\r\n\r\nSNPs occur roughly once in every 300 base pairs on average, meaning there are millions of SNP differences between any two people. Most SNPs have no effect on health, but some can influence how genes function or how proteins are made. As such, SNPs can alter how a person responds to a drug, or their susceptibility to a health condition, including possibly their predisposition to addiction.\r\n\r\nBecause SNPs are so abundant and spread throughout the genome, they serve as useful markers for researchers. Modern genotyping technologies, such as SNP microarrays, can test hundreds of thousands of SNPs in a person\u2019s DNA quickly and cost-effectively. This allows scientists to create a genome-wide \u201cfingerprint\u201d of genetic variants for each individual in a study. These data feed the analyses you\u2019ll see next.\r\n\r\nA recommended quick resource to learn more about SNPs can be found at <a href=\"https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/\"><em>Making SNPs Make Sense<\/em><\/a> from the Genetics Science Learning Center (2016).\r\n\r\n[caption id=\"attachment_130\" align=\"aligncenter\" width=\"683\"]<img class=\"wp-image-130 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-683x1024.png\" alt=\"Two-panel diagram showing how a DNA coding region (gene) makes a protein, and how a single-nucleotide polymorphism (SNP) in the gene can change the genotype and potentially alter the protein.\" width=\"683\" height=\"1024\" \/> Figure 4. A gene\u2019s DNA sequence encodes a protein; a SNP (single-letter DNA change) can create a variant genotype and may alter the resulting protein. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.[\/caption]\r\n<h3>Genome-Wide Association Studies (GWAS)<\/h3>\r\nHow do scientists go from raw DNA data to discovering which genetic variants might increase the risk of disease, or show associations with phenotypes like addiction? The answer is often through genome-wide association studies, commonly abbreviated as GWAS (pronounced \u201cGEE-wahs\u201d).\r\n\r\nA GWAS is a systematic scan of the entire genome, comparing many people to see whether specific genetic variants (usually SNPs) are associated with a particular trait or disease. For a visual overview, see the cartoon explainer of GWAS developed by the Broad Institute (2017).\r\n\r\nIn a GWAS, researchers examine hundreds of thousands, or even millions, of SNPs across the genome in a large group of individuals. The goal is to identify SNPs that are statistically more frequent in people with the trait of interest (for example, nicotine addiction) compared to people without the trait.\r\n\r\nIf a particular SNP is significantly more common in the cases (those with the addiction) than in the controls (those without), that SNP is flagged as potentially associated with the trait.\r\n\r\n[caption id=\"attachment_132\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-132 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-1024x576.jpg\" alt=\"Diagram illustrating a genome-wide association study (GWAS), comparing people with a disease vs without a disease across multiple SNPs, showing one SNP associated with disease.\" width=\"1024\" height=\"576\" \/> Figure 5. Genome-wide association study (GWAS) concept: compare SNP frequencies in individuals with and without a disease to identify variants associated with disease risk. Image Credit: <a href=\"https:\/\/www.genome.gov\/about-genomics\/fact-sheets\/Genome-Wide-Association-Studies-Fact-Sheet\">NHGRI 2020<\/a>.[\/caption]\r\n<h3>The Process of GWAS<\/h3>\r\nAt the outset, researchers must define the phenotype, or trait, they are studying. In addiction research, a phenotype might be a clinical diagnosis, such as opioid use disorder defined by diagnostic criteria, or a quantitative measure, such as the number of cigarettes smoked per day.\r\n\r\nOnce the phenotype is defined and DNA is collected, usually through blood or saliva samples, genotyping is performed using SNP arrays or sequencing technologies. This process yields each individual\u2019s genotype at hundreds of thousands or even millions of SNPs. Researchers then conduct a statistical test for each SNP to evaluate whether variation at that location is associated with the trait of interest.\r\n\r\nBecause so many SNPs are tested, researchers apply a very stringent standard for statistical significance. This high threshold helps distinguish true associations from results that could arise by random chance. Any SNP that surpasses this cutoff is considered genome-wide significant and treated as a strong candidate for a real association.\r\n\r\nReplication is essential. To be confident that a finding is not a statistical fluke or a quirk of a single dataset, significant SNP\u2013trait associations should be confirmed in an independent sample. Replication strengthens confidence that the association reflects a genuine biological signal.\r\n<h3>Interpreting GWAS Results<\/h3>\r\n<p data-start=\"31\" data-end=\"399\">GWAS results are commonly visualized using a Manhattan plot, named for its resemblance to a city skyline. Each dot represents a SNP, plotted by its genomic position along the x-axis (typically chromosomes 1\u201322) and by the strength of its association with the trait on the y-axis (often shown as \u2212log10(p-value), where higher values mean stronger statistical evidence).<\/p>\r\n<p data-start=\"401\" data-end=\"881\">Most points appear near the bottom of the plot, indicating little or no association. Occasionally, tall \u201cskyscraper\u201d peaks rise above a horizontal threshold line, marking variants (or regions) that reach genome-wide significance. These peaks highlight genomic regions where variants are statistically associated with the trait. Because true associations are rare and effect sizes are usually small, only a limited number of regions typically stand out, even in very large studies.<\/p>\r\n\r\n\r\n[caption id=\"attachment_137\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-137 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-1024x399.png\" alt=\"Manhattan plot of a genome wide association study: colored points grouped by chromosomes 1 through 22 on the x axis, and negative log10(p) on the y axis. Most points form a low band, with several tall peaks indicating stronger associations.\" width=\"1024\" height=\"399\" \/> Figure 6. Manhattan plot showing genome association with microcirculation (GWAS example). Source figure from Ikram et al. (2010), PLOS Genetics; shared via Wikimedia Commons under CC BY 2.5.[\/caption]\r\n<h3>Interpreting a Manhattan Plot for Externalizing Behaviors<\/h3>\r\n<p data-start=\"1116\" data-end=\"1457\">Figure 6 provides a concrete example of how to read a Manhattan plot. Each dot is a single DNA variant. The x-axis shows where that variant sits in the genome, organized by chromosome. The y-axis shows how strong the statistical evidence is that the variant is associated with the phenotype being studied. Higher dots mean stronger evidence.<\/p>\r\n<p data-start=\"1459\" data-end=\"1973\">The dashed horizontal line represents the genome-wide significance threshold. Peaks that cross this line suggest genomic regions where many nearby variants show strong signals, often because they are correlated with each other (they tend to be inherited together). In practice, researchers do not stop at \u201ca tall peak.\u201d They check whether the signal replicates in an independent sample, and they follow up to identify which genes or biological pathways might plausibly connect that genomic region to the phenotype.<\/p>\r\n<p data-start=\"1975\" data-end=\"2250\"><strong data-start=\"1975\" data-end=\"2008\">Key takeaway (from Figure 6).<\/strong> Manhattan plots typically show a \u201cmany dots, few peaks\u201d pattern, reflecting that complex traits are influenced by many variants with small effects, plus a smaller number of variants or regions that clear a very strict significance threshold.<\/p>\r\n\r\n<h3 data-start=\"2252\" data-end=\"2307\">Connecting this back to externalizing and addiction<\/h3>\r\n<p data-start=\"2309\" data-end=\"2742\">Although Figure 6 is a GWAS example for microcirculation (not a behavioral trait), Manhattan plots for behavioral phenotypes use the same logic and look visually similar. In GWAS of externalizing-related traits (such as impulsivity, rule-breaking, and early substance use), researchers also look for peaks above the genome-wide threshold, interpret them as associated genomic regions, and require replication to confirm the findings.<\/p>\r\n<p data-start=\"2744\" data-end=\"3228\">Externalizing is widely understood as <strong data-start=\"2782\" data-end=\"2795\">polygenic<\/strong>, meaning there is no single \u201cgene for\u201d addiction or risk-taking. Instead, many small genetic influences are spread across the genome. Individually, each effect is tiny. Together, they can shift the odds at the group level. When these small effects are combined into a <strong data-start=\"3064\" data-end=\"3089\">polygenic score (PGS)<\/strong>, the score can capture some portion of variation in externalizing in large samples, but it does not predict destiny for any single person.<\/p>\r\n<p data-start=\"2744\" data-end=\"3228\">Figure 6 shows how to read the plot; for a domain-specific externalizing example, see Manhattan plots reported in large externalizing GWAS papers (for example, Karlsson et al., 2021).<\/p>\r\n\r\n<h3>Notable Findings from GWAS of Addiction<\/h3>\r\nRecent large-scale GWAS have moved from mixed early results to several replicated discoveries that clarify substance-specific signals and shared vulnerability.\r\n<ul>\r\n \t<li><strong>Alcohol Use Disorder (AUD).<\/strong> Meta-analyses repeatedly identify variants in alcohol-metabolizing enzymes, especially <strong>ADH1B<\/strong>, and also <strong>ADH1C<\/strong> and <strong>ALDH2<\/strong> (Zaso et al., 2019). Certain <strong>ADH1B<\/strong> alleles (common in some East Asian groups) are protective because they speed acetaldehyde buildup, producing an aversive flushing response (Cho et al., 2023).<\/li>\r\n \t<li><strong>Opioid Use Disorder (OUD).<\/strong> A coding SNP in <strong>OPRM1<\/strong> (the \u03bc-opioid receptor) shows a robust association with opioid dependence and replicates across cohorts (Zhou et al., 2020). This is biologically plausible because <strong>OPRM1<\/strong> is the receptor targeted by opioids like heroin and oxycodone (Gaddis et al., 2022). The finding underscores how receptor biology can shape individual risk.<\/li>\r\n \t<li><strong>Shared vulnerability across substances.<\/strong> A 2023 mega-analysis of more than 1 million individuals identified 19 independent markers for a cross-substance addiction risk factor spanning alcohol, nicotine, cannabis, and opioids (Hatoum et al., 2023). Many signals map to genes involved in dopamine signaling, highlighting shared reward circuitry. Higher genetic liability for addiction also correlates with elevated risk for several mental-health and medical conditions, suggesting overlapping genetic architecture.<\/li>\r\n<\/ul>\r\nThese examples illustrate that GWAS can point to both specific genes (like <strong>OPRM1<\/strong> or <strong>ADH1B<\/strong>) and broader biological systems (like dopamine regulation) as relevant to addiction. Each associated SNP is a clue. For example, it may affect receptor function or drug metabolism, which can influence responses to substances.\r\n<h3>Limitations and Challenges of GWAS<\/h3>\r\nGWAS are powerful discovery tools, but their findings come with important caveats that affect interpretation and use.\r\n<ul>\r\n \t<li><strong>Small effects and polygenicity.<\/strong> Most associated SNPs shift risk by only a few percent, and complex traits like addiction are influenced by thousands of variants. Even large studies often explain only a small share of total risk, so GWAS signals are best viewed as small pieces of a much larger puzzle.<\/li>\r\n \t<li><strong>Population stratification.<\/strong> If cases and controls differ in ancestry, allele-frequency differences can create false associations unrelated to the trait. Statistical corrections (for example, principal components) help, but residual bias can remain. This is one reason diverse, well-matched samples matter.<\/li>\r\n \t<li><strong>\u201cMissing heritability\u201d and LD tagging.<\/strong> A significant SNP often tags a broader region because neighboring variants are inherited together (linkage disequilibrium). Identifying the causal change usually requires fine-mapping, sequencing, and additional study designs.<\/li>\r\n \t<li><strong>Need for functional follow-up.<\/strong> Association does not reveal mechanism. A variant might alter a protein, change gene expression, or affect regulation in specific tissues or developmental windows. Post-GWAS functional genomics is essential to translate hits into biology.<\/li>\r\n \t<li><strong>Winner\u2019s curse and replication.<\/strong> First reports tend to overestimate effect sizes, and some early findings fail to replicate. Independent replication and meta-analysis are necessary to confirm signals and obtain more accurate effect estimates.<\/li>\r\n<\/ul>\r\n<h2>Polygenic Scores (PGS)<\/h2>\r\nA polygenic score (PGS) (or polygenic risk score \u2013 PGS) collapses information from many tiny genetic effects into a single number that estimates a person\u2019s inherited predisposition to a trait (e.g., addiction risk). In plain terms, it\u2019s a \u201cgenetic risk tally\u201d much like a credit score condenses your financial history into one number that shifts your likelihood of loan approval. A high PGS does not guarantee an outcome, and a low PGS does not prevent it. It changes probabilities.\r\n<h3>Constructing a PGS<\/h3>\r\n<ul>\r\n \t<li><strong>Select SNPs (which DNA sites to include).<\/strong> Researchers start from GWAS results and decide which single-nucleotide polymorphisms (SNPs) to use. One strategy includes only SNPs that pass a very strict p-value cutoff (i.e., results unlikely to be due to chance). Another includes many more \u201csub-threshold\u201d SNPs because lots of very small signals can help prediction when combined. Researchers also remove highly correlated SNPs (called LD pruning) so the score does not double-count the same signal.<\/li>\r\n \t<li><strong>Assign a weight to each SNP (how strongly it relates to the trait).<\/strong> Each included SNP gets a weight based on its effect size from the GWAS, typically a regression beta or a (log) odds ratio. In plain language, this number tells you how much carrying one copy of the risk allele nudges the trait up or down. Positive weights push risk higher, negative weights push it lower.<\/li>\r\n \t<li><strong>Calculate the person\u2019s score (sum the weighted alleles).<\/strong> For each SNP, count how many risk alleles the person has, 0, 1, or 2, and multiply by that SNP\u2019s weight. Then add those products across all selected SNPs: <strong>PGS = \u03a3 (allele count \u00d7 weight)<\/strong>. Conceptually, this is just \u201cadd up all the tiny nudges.\u201d If the GWAS reported odds ratios, analysts usually take logarithms first so the math becomes simple addition rather than multiplication.<\/li>\r\n \t<li><strong>Standardize the score (make it interpretable).<\/strong> After computing raw PGS values for everyone in a dataset, researchers typically standardize them, for example by converting to a z-score (how many standard deviations above or below the group average) or to a percentile. In plain terms, a 90th-percentile PGS means your score is higher than 90% of people in that sample.<\/li>\r\n<\/ul>\r\n&nbsp;\r\n\r\n[caption id=\"attachment_139\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-139 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-1024x683.png\" alt=\"Five-step left-to-right flowchart showing how a polygenic score (PGS) is built: GWAS summary statistics \u2192 LD clumping\/pruning \u2192 apply GWAS effect-size weights (\u03b2) to genotypes (0\/1\/2) \u2192 sum weighted SNP effects \u2192 standardize into a z-score or percentile; formula shown as PGS\u1d62 = \u03a3 \u03b2\u2c7c\u00b7G\u1d62\u2c7c.\" width=\"1024\" height=\"683\" \/> Figure 7. Workflow for constructing a polygenic score (PGS) from GWAS effect sizes by selecting SNPs, weighting genotypes, summing across variants, and standardizing the resulting score for interpretation. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.[\/caption]\r\n<h3>What a PGS Tells Us (and Does Not)<\/h3>\r\n<ul>\r\n \t<li><strong>Probabilities, not certainties.<\/strong> A higher PGS means a higher likelihood, on average in large groups, of the trait. It is not a guarantee for any one person. Like a credit score, it shifts odds, and behavior and context still matter.<\/li>\r\n \t<li><strong>Context matters.<\/strong> Environment and choices can amplify or buffer genetic liability. For example, a person with a high PGS for alcohol problems who never drinks will not express that risk, while high stress or easy access could increase risk for someone who does drink.<\/li>\r\n<\/ul>\r\n[caption id=\"attachment_142\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-142 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-1024x683.png\" alt=\"Synthetic illustration of substance use outcomes across polygenic score (PGS) groups\" width=\"1024\" height=\"683\" \/> Figure 8. Created by the author using synthetic (simulated) data for educational purposes. Conceptually informed by findings from Karlsson Linn\u00e9r et al. (2021), Nature Neuroscience, but does not reproduce or depict original study data or figures. Released under a Creative Commons Attribution (CC BY 4.0) license. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.[\/caption]\r\n\r\nFigure 8 illustrates this principle using a <strong data-start=\"1004\" data-end=\"1032\">synthetic (hypothetical)<\/strong> example of substance use outcomes across groups of individuals binned by their PGS. In this illustration, average rates of alcohol use disorder (AUD), illicit drug use, and opioid use increase as we move from the lowest-scoring 20% to the highest 20%. The key point is the <strong data-start=\"1306\" data-end=\"1335\">shape of the relationship<\/strong>, not the exact numbers: the increases are gradual, not absolute. Many people with high scores never develop problems, and some with lower scores still do.\r\n\r\nThis matches what real studies typically find, including large-scale analyses of externalizing and substance outcomes (Karlsson Linn\u00e9r et al., 2021). The takeaway is that polygenic scores shift group averages. They help explain patterns at the population level, while individual outcomes remain shaped by many other genetic, environmental, and behavioral factors.\r\n\r\n&nbsp;\r\n\r\n[caption id=\"attachment_140\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-140 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-1024x683.png\" alt=\"Vector infographic of a normal bell curve titled \u201cDistribution of Polygenic Scores (PGS) in a Sample,\u201d with axes labeled Density (y-axis) and PGS (z-score) (x-axis). The curve shows vertical lines at the 10th, 50th (dashed), and 90th percentiles, with labels for Lower PGS and Higher PGS and shading that is darker in both tails.\" width=\"1024\" height=\"683\" \/> Figure 9. Normal distribution of polygenic scores in a sample, illustrating relative position using the 10th, 50th (median), and 90th percentiles. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.[\/caption]\r\n\r\nFigure 9 reinforces two key points:\r\n<ul>\r\n \t<li>Being in a \u201chigh\u201d or \u201clow\u201d group is always relative to others in the sample, not an absolute threshold.<\/li>\r\n \t<li>The vast majority of people are clustered around the middle of the distribution, where genetic liability is modest.<\/li>\r\n<\/ul>\r\nTaken together, Figures 8 and 9 show that polygenic scores reflect a continuum of probabilities, not destinies. Higher scores may nudge risk upward on average, but there is no sharp dividing line between \u201caffected\u201d and \u201cunaffected.\u201d\r\n<h2>Common Research Uses of PGS<\/h2>\r\n<ul>\r\n \t<li><strong>Stratifying risk in studies.<\/strong> Researchers can compare outcomes for participants with higher versus lower PGS to see whether trajectories (e.g., earlier initiation or faster escalation) differ.<\/li>\r\n \t<li><strong>Testing gene\u2013environment interplay.<\/strong> PGS provides a single summary of genetic liability that can be interacted with environmental measures (e.g., stress, parental monitoring) to test whether context changes genetic effects.<\/li>\r\n \t<li><strong>Adjusting for inherited propensity.<\/strong> Studies evaluating programs or exposures can include PGS as a covariate to account for baseline genetic differences among participants.<\/li>\r\n \t<li><strong>Exploring shared biology.<\/strong> PGS for one trait (e.g., depression) can be tested against another outcome (e.g., substance use) to probe pleiotropy, meaning shared genetic influences across conditions.<\/li>\r\n<\/ul>\r\n<h2>Limitations of PGS<\/h2>\r\n<ul>\r\n \t<li><strong>Ancestry transferability.<\/strong> Polygenic scores often do not transfer well across populations with different ancestral backgrounds. If the GWAS that supplied the effect sizes was mostly European-ancestry, the same score can predict poorly for people of East Asian, African, or admixed ancestries because allele frequencies and effect sizes can differ. This is why increasing diversity in genetic research is essential so that PGS are equitable and accurate for all groups.<\/li>\r\n \t<li><strong>Small variance explained.<\/strong> A PGS usually accounts for only a small slice of the overall differences among people (e.g., \u201cabout 5% of variance\u201d in liability). In plain language, most of what makes individuals similar or different on the trait is not captured by the score. Other genes, rare variants, environment, and chance all matter. As a result, prediction for a single person is limited: some people with high scores will not develop problems, and some with low scores will.<\/li>\r\n \t<li><strong>Environment still shapes outcomes.<\/strong> PGS captures genetic propensity, not life context. Two people with the same score can have very different outcomes if one grows up in a supportive, low-risk environment and the other faces high stress or easy access to substances. Put simply, the score nudges probabilities, but environments and choices can amplify, mute, or prevent expression of that liability.<\/li>\r\n \t<li><strong>Risk of misunderstanding or misuse.<\/strong> Without careful explanation, people may interpret a high PGS as destiny (\u201cit\u2019s in my genes, there\u2019s nothing I can do\u201d) or panic about stigmatizing labels. Institutions could also be tempted to use scores inappropriately, hence the importance of legal protections and strong ethics guidance discussed later in the module. Clear communication should emphasize that PGS shifts odds. It does not define an individual.<\/li>\r\n<\/ul>\r\nDespite these caveats, PGS research is valuable because it aggregates many tiny genetic effects into a usable summary, helping scientists study risk patterns and gene\u2013environment interplay. For complex traits like addiction, there is no single \u201cgene for\u201d the outcome. Many small influences add up, and PGS gives us one careful way to quantify that aggregate.\r\n<h2>Ethics and Responsible Use of PGS<\/h2>\r\n<h3>Why genetics research must be handled responsibly<\/h3>\r\nScenario to frame our ethics lens: In 2018, media covered clinics exploring embryo screening for polygenic disease risk. Imagine adding \u201caddiction risk\u201d to that list. Who decides what counts as \u201chigh risk\u201d? Could that label follow a person for life? This section equips you to spot risks and communicate findings responsibly so the science helps people, not harms them.\r\n<h3>1) Data privacy and informed consent<\/h3>\r\nGenetic data are uniquely identifying. Even \u201cde-identified\u201d datasets can sometimes be re-identified when combined with other information. Treat DNA as high-risk personal data: use robust security, least-privilege access, and controlled-access repositories. In ABCD and similar studies, qualified researchers access genetic files through gated systems designed to protect participant confidentiality.\r\n\r\nInformed consent must be specific and clear. Participants (and caregivers) should understand what will be measured, how data will be stored and shared, who may access it, whether results might be reused in future studies, and their right to withdraw. Make these elements explicit whenever genomic data are collected or analyzed.\r\n<h3>2) Avoiding genetic determinism and stigma<\/h3>\r\nCommunicate probabilities, not destinies. GWAS and PGS shift odds at the population level. They do not define any one person. Environment and choices still matter. Scores nudge risk but do not fix outcomes.\r\n\r\nName common misinterpretations up front. High PGS does not equal inevitability. Low PGS does not equal immunity. Warn against institutional misuse and note that legal and ethical guardrails restrict certain uses (e.g., U.S. protections like GINA for employers and health insurers).\r\n\r\nAdopt precise, respectful language.\r\n\r\n<strong>Do say:<\/strong>\r\n<ul>\r\n \t<li>\u201cGenetic liability increases average risk in some contexts.\u201d<\/li>\r\n \t<li>\u201cHeritability is a population statistic.\u201d<\/li>\r\n \t<li>\u201cSupports can buffer risk.\u201d<\/li>\r\n \t<li>\u201cResults may be age-graded and context-dependent.\u201d<\/li>\r\n<\/ul>\r\n<strong>Don\u2019t say:<\/strong>\r\n<ul>\r\n \t<li>\u201cBorn to be addicted.\u201d<\/li>\r\n \t<li>\u201cHigh heritability means environments don\u2019t matter.\u201d<\/li>\r\n \t<li>\u201c50% heritable means half of your behavior is genetic.\u201d<\/li>\r\n \t<li>\u201cHeritability explains between-group differences.\u201d<\/li>\r\n<\/ul>\r\n<h3>3) Equity and diversity in genetic research<\/h3>\r\nWhy representation matters. Many GWAS historically over-sampled European-ancestry participants. As a result, PGS often transfers poorly to other ancestries because allele frequencies and effect sizes can differ, reducing accuracy and fairness. Prioritize diverse cohorts, transparent documentation, and cross-ancestry validation so findings benefit everyone.\r\n\r\nAvoid scientific racism and typological thinking. Race and ethnicity are social constructs and not proxies for genetic ancestry. Use precise genetic markers; justify any population descriptors; be transparent about classification methods; and acknowledge limits to generalizability.\r\n\r\nKeep between-group and within-group inferences separate. Heritability within a group cannot explain differences between groups living in different contexts. Maintain an equity lens: focus on supports and opportunity structures, not \u201cdeficits.\u201d\r\n<h3>4) Responsible use of genetic data (policy and practice)<\/h3>\r\nPurpose-bound use and minimum necessary. Collect and analyze only what you need; avoid repurposing data without consent. Use controlled-access workflows, log decisions, and align with IRB and data-use agreements.\r\n\r\nGuardrails against misuse. Clearly state that GWAS and PGS are research tools and are unsuitable for labeling, tracking, or penalizing individuals (e.g., in schools, workplaces, or insurance). Note that legal protections (e.g., GINA) and evolving policies aim to reduce discrimination, but vigilance and clear communication remain essential.\r\n\r\nLanguage that reduces harm. Carry forward Module 4\u2019s communication principles: use neutral, action-oriented phrasing; avoid deterministic labels; and foreground modifiable supports (mentoring, routines, coping skills, evidence-based prevention).\r\n\r\nClosing note: balanced optimism. Ethical risks are real, but they are manageable. Strong privacy practices, clear consent, diverse study designs, careful communication, and community engagement all improve the science and its impact. Used responsibly, GWAS and PGS can illuminate mechanisms, sharpen study design, and ultimately inform prevention without stigmatizing individuals or communities.\r\n<h2>ABCD Study\u2019s Genetic Data and Resources<\/h2>\r\nThe ABCD Study collects genomic data to illuminate how inherited differences contribute to brain development and addiction-related behaviors across adolescence. Below is how ABCD gathers, processes, and shares genetic data, the design features that make it uniquely powerful, and how researchers are using these resources, alongside a reminder that all work with DNA requires careful ethical stewardship.\r\n<h3>How is DNA collected?<\/h3>\r\n<ul>\r\n \t<li><strong>Saliva (primary method).<\/strong> ABCD obtains DNA primarily via saliva, non-invasive and youth-friendly, then extracts genomic material for downstream analyses.<\/li>\r\n \t<li><strong>Biospecimen note.<\/strong> ABCD collects additional biospecimens for other scientific aims (e.g., exposure assays), but genotyping for genetics is based on saliva-derived DNA.<\/li>\r\n<\/ul>\r\n<h3>How is the genetic information analyzed?<\/h3>\r\n<ul>\r\n \t<li><strong>Genotyping platform.<\/strong> ABCD has used the Axiom Smokescreen array, enabling dense coverage of SNPs relevant to neurodevelopment and substance-use research.<\/li>\r\n \t<li><strong>What variants are studied?<\/strong> Analyses focus on single-nucleotide polymorphisms (SNPs). Post-GWAS tools (e.g., polygenic scores) summarize many tiny effects into a single index of inherited liability.<\/li>\r\n \t<li><strong>Quality control and population structure.<\/strong> Standard QC (e.g., call-rate thresholds, Hardy\u2013Weinberg checks, sex\/relatedness concordance) precedes analyses. Ancestry inference (e.g., principal components) helps limit confounding from population structure.<\/li>\r\n \t<li><strong>Imputation.<\/strong> Genotypes are imputed against reference panels to expand variant coverage beyond the typed SNPs.<\/li>\r\n<\/ul>\r\n<h3>Data availability and access<\/h3>\r\n<ul>\r\n \t<li><strong>Versioned, documented releases.<\/strong> Genetic datasets are distributed with rigorous QC documentation to support reproducibility.<\/li>\r\n \t<li><strong>Controlled access.<\/strong> Qualified investigators obtain de-identified data through controlled-access repositories under data-use agreements, balancing scientific openness with participant privacy and consent.<\/li>\r\n<\/ul>\r\n<h3>Unique features of ABCD genetics<\/h3>\r\n<ul>\r\n \t<li><strong>Population breadth.<\/strong> ABCD enrolls participants from diverse U.S. communities, improving generalizability and enabling tests of portability for GWAS findings and polygenic scores across backgrounds.<\/li>\r\n \t<li><strong>Twin\/sibling design.<\/strong> A substantial twin and sibling subsample allows researchers to combine behavioral genetic designs (A\/C\/E, discordant-twin contrasts) with molecular approaches (GWAS\/PGS) within the same longitudinal cohort, which is powerful for probing gene\u2013environment interplay and developmental timing.<\/li>\r\n<\/ul>\r\n<h3>How researchers use the ABCD genetic data<\/h3>\r\n<ul>\r\n \t<li><strong>Validating biology of the brain.<\/strong> Linking genomic variation to neuroimaging and cognitive phenotypes to test whether previously reported brain-related loci generalize to youth.<\/li>\r\n \t<li><strong>Psychiatric and behavioral liability.<\/strong> Examining how polygenic propensities for psychiatric conditions relate to emerging psychopathology and substance-use trajectories in adolescence (Fan et al., 2023: 160).<\/li>\r\n \t<li><strong>Cross-trait architecture and G\u00d7E.<\/strong> Exploring shared genetic architecture across externalizing, internalizing, and substance phenotypes, and testing how environmental contexts (family, school, neighborhood) tune genetic effects over time.<\/li>\r\n<\/ul>\r\nEthics connection: Access controls, careful population descriptors, and clear consent are integral. See this module\u2019s Ethics and Responsible Use section for guidance on privacy, communication, equity, and responsible interpretation.\r\n\r\n<hr \/>\r\n\r\n<h2>Works Cited<\/h2>\r\nAuchter, A. M., Myers, C. E., Mann, J. B., &amp; Ryan, L. A. (2018). A description of the ABCD organizational structure and the development of a large-scale neurodevelopmental study. <em>Developmental Cognitive Neuroscience, 32<\/em>, 8\u201315. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.04.003\">https:\/\/doi.org\/10.1016\/j.dcn.2018.04.003<\/a>\r\n\r\nBaurley, J. W., Edlund, C. K., Pardamean, C. I., Conti, D. V., &amp; Bergen, A. W. (2016). Smokescreen: A targeted genotyping array for addiction research. <em>Addiction Biology, 21<\/em>(3), 517\u2013525. <a href=\"https:\/\/doi.org\/10.1111\/adb.12345\">https:\/\/doi.org\/10.1111\/adb.12345<\/a>\r\n\r\nBird, K. A., &amp; Carlson, J. (2021). Typological thinking in human genomics research contributes to the production and prominence of scientific racism. Manuscript in preparation.\r\n\r\nCardenas-Iniguez, C., &amp; Robledo Gonzalez, M. (2023). Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research. <em>Nature Neuroscience, 26<\/em>(1), 45\u201360. <a href=\"https:\/\/doi.org\/10.1038\/s41593-022-01234-5\">https:\/\/doi.org\/10.1038\/s41593-022-01234-5<\/a>\r\n\r\nCasey, B. J., Cannonier, T., Conley, M. I., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. <em>Developmental Cognitive Neuroscience, 32<\/em>, 43\u201354. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.06.004\">https:\/\/doi.org\/10.1016\/j.dcn.2018.06.004<\/a>\r\n\r\nCho, Y., Lin, K., Lee, S. H., et al. (2023). Genetic influences on alcohol flushing in East Asian populations. <em>BMC Genomics, 24<\/em>, 638. <a href=\"https:\/\/doi.org\/10.1186\/s12864-023-09721-7\">https:\/\/doi.org\/10.1186\/s12864-023-09721-7<\/a>\r\n\r\nFan, C. C., Loughnan, R., &amp; ABCD Genetic Working Group. (2023). Genotype data and derived genetic instruments of Adolescent Brain Cognitive Development Study\u00ae for better understanding of human brain development. <em>Behavior Genetics, 53<\/em>(1), 31\u201345. <a href=\"https:\/\/doi.org\/10.1007\/s10519-022-10002-3\">https:\/\/doi.org\/10.1007\/s10519-022-10002-3<\/a>\r\n\r\nGaddis, N., Mathur, R., Marks, J., et al. (2022). Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. <em>Scientific Reports, 12<\/em>, 16873. <a href=\"https:\/\/doi.org\/10.1038\/s41598-022-21003-y\">https:\/\/doi.org\/10.1038\/s41598-022-21003-y<\/a>\r\n\r\nGenetic Science Learning Center. (2016, February 1). Making SNPs Make Sense. Retrieved October 10, 2024, from <a href=\"https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/\">https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/<\/a>\r\n\r\nGymrek, M., Willems, T., Mandal, S., et al. (2013). Identifying personal genomes by surname inference. <em>Nature Genetics, 45<\/em>(6), 304\u2013309. <a href=\"https:\/\/doi.org\/10.1038\/ng.2644\">https:\/\/doi.org\/10.1038\/ng.2644<\/a>\r\n\r\nHatoum, A. S., Colbert, S. M. C., Johnson, E. C., et al. (2023). Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. <em>Nature Mental Health, 1<\/em>, 210\u2013223. <a href=\"https:\/\/doi.org\/10.1038\/s44220-023-00034-y\">https:\/\/doi.org\/10.1038\/s44220-023-00034-y<\/a>\r\n\r\nIacono, W. G., Heath, A. C., Hewitt, J. K., Neale, M. C., Banich, M. T., &amp; Luciana, M. (2018). The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design. <em>Developmental Cognitive Neuroscience, 32<\/em>, 30\u201342. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2017.09.001\">https:\/\/doi.org\/10.1016\/j.dcn.2017.09.001<\/a>\r\n\r\nKarlsson Linn\u00e9r, R., Mallard, T. T., Barr, P. B., et al. (2021). Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. <em>Nature Neuroscience, 24<\/em>, 1367\u20131376. <a href=\"https:\/\/doi.org\/10.1038\/s41593-021-00908-3\">https:\/\/doi.org\/10.1038\/s41593-021-00908-3<\/a>\r\n\r\nNational Academies of Sciences, Engineering, and Medicine. (2018). National Academies Press. <a href=\"https:\/\/doi.org\/10.17226\/%5BDOI\">https:\/\/doi.org\/10.17226\/[DOI<\/a>]\r\n\r\nNational Academies of Sciences, Engineering, and Medicine. (2023). <em>Using Population Descriptors in Genetics and Genomics Research<\/em>. National Academies Press. <a href=\"https:\/\/doi.org\/10.17226\/%5BDOI\">https:\/\/doi.org\/10.17226\/[DOI<\/a>]\r\n\r\nPlomin, R., DeFries, J. C., &amp; Fulker, D. W. (1988). <em>Nature and Nurture during Middle Childhood<\/em>. Blackwell.\r\n\r\nPrice, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., &amp; Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. <em>Nature Genetics, 38<\/em>(8), 904\u2013909. <a href=\"https:\/\/doi.org\/10.1038\/ng1764\">https:\/\/doi.org\/10.1038\/ng1764<\/a>\r\n\r\nUban, K. A., Horton, M. K., Jacobus, J., Heyser, C., Thompson, W. K., Tapert, S. F., Madden, P. A. F., &amp; Sowell, E. R. (2018). Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data. <em>Developmental Cognitive Neuroscience, 32<\/em>, 97\u2013106. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.06.005\">https:\/\/doi.org\/10.1016\/j.dcn.2018.06.005<\/a>\r\n\r\nWellcome Sanger Institute. (2022). Human genome sequencing advancements. Retrieved from <a href=\"https:\/\/www.sanger.ac.uk\/\">https:\/\/www.sanger.ac.uk\/<\/a>\r\n\r\nZaso, M. J., Goodhines, P. A., Wall, T. L., &amp; Park, A. (2019). Meta-analysis on associations of alcohol metabolism genes with alcohol use disorder in East Asians. <em>Alcohol and Alcoholism, 54<\/em>(3), 216\u2013224. <a href=\"https:\/\/doi.org\/10.1093\/alcalc\/agz011\">https:\/\/doi.org\/10.1093\/alcalc\/agz011<\/a>\r\n\r\nZhou, H., Rentsch, C. T., Cheng, Z., et al. (2020). Association of OPRM1 functional coding variant with opioid use disorder: A genome-wide association study. <em>JAMA Psychiatry, 77<\/em>(10), 1072\u20131080. <a href=\"https:\/\/doi.org\/10.1001\/jamapsychiatry.2020.1206\">https:\/\/doi.org\/10.1001\/jamapsychiatry.2020.1206<\/a>","rendered":"<div class=\"flex flex-col text-sm pb-25\">\n<article class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" data-turn-id=\"3a739a8f-cea2-4dd5-9078-314a444f686c\" data-testid=\"conversation-turn-82\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm\/main:[--thread-content-margin:--spacing(6)] @w-lg\/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"8c1c2e57-c234-41ad-a31f-1f650379e720\" data-message-model-slug=\"gpt-5-2-thinking\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\n<div class=\"markdown prose dark:prose-invert w-full wrap-break-word light markdown-new-styling\">\n<h3 data-start=\"0\" data-end=\"22\">Reading Objectives<\/h3>\n<p data-start=\"24\" data-end=\"73\">By the end of this module, you should be able to:<\/p>\n<ol data-start=\"75\" data-end=\"1378\">\n<li data-start=\"75\" data-end=\"326\">\n<p data-start=\"78\" data-end=\"326\"><strong data-start=\"78\" data-end=\"125\">Understand Molecular Genetics Fundamentals:<\/strong> Grasp the basic structure and function of DNA, genes, and genomes, and recognize the role of genetic variations such as Single Nucleotide Polymorphisms (SNPs) in influencing behaviors and addiction.<\/p>\n<\/li>\n<li data-start=\"327\" data-end=\"568\">\n<p data-start=\"330\" data-end=\"568\"><strong data-start=\"330\" data-end=\"381\">Explain Genome-Wide Association Studies (GWAS):<\/strong> Describe the methodology and purpose of GWAS, including study designs like case-control and cohort studies, and understand how GWAS identify genetic variants associated with addiction.<\/p>\n<\/li>\n<li data-start=\"569\" data-end=\"799\">\n<p data-start=\"572\" data-end=\"799\"><strong data-start=\"572\" data-end=\"610\">Comprehend Polygenic Scores (PGS):<\/strong> Understand how PGS are derived from GWAS data, their applications in predicting addiction risk, and the limitations associated with their use, especially concerning population diversity.<\/p>\n<\/li>\n<li data-start=\"800\" data-end=\"1167\">\n<p data-start=\"803\" data-end=\"1167\"><strong data-start=\"803\" data-end=\"871\">Identify and Address Ethical Considerations in Genetic Research:<\/strong> Recognize the importance of data privacy, informed consent, and the responsible use of genetic data to prevent scientific racism and ensure equitable research practices. Implement best practices for using population descriptors and appreciate the significance of genetic diversity in research.<\/p>\n<\/li>\n<li data-start=\"1168\" data-end=\"1378\">\n<p data-start=\"1171\" data-end=\"1378\"><strong data-start=\"1171\" data-end=\"1224\">Explore the ABCD Study\u2019s Genetic Data Collection:<\/strong> Learn how the ABCD Study collects, analyzes, and utilizes genetic data to investigate the interplay between genetics, brain development, and addiction.<\/p>\n<\/li>\n<\/ol>\n<hr data-start=\"1380\" data-end=\"1383\" \/>\n<h3 data-start=\"1385\" data-end=\"1398\">Key Terms<\/h3>\n<ul data-start=\"1400\" data-end=\"2667\">\n<li data-start=\"1400\" data-end=\"1617\">\n<p data-start=\"1402\" data-end=\"1617\"><strong data-start=\"1402\" data-end=\"1434\">DNA (Deoxyribonucleic Acid):<\/strong> The molecule that carries genetic information in all living organisms, structured as a double helix composed of four bases: adenine (A), thymine (T), cytosine (C), and guanine (G).<\/p>\n<\/li>\n<li data-start=\"1618\" data-end=\"1746\">\n<p data-start=\"1620\" data-end=\"1746\"><strong data-start=\"1620\" data-end=\"1629\">Gene:<\/strong> A specific segment of DNA that encodes instructions for building proteins, serving as the basic units of heredity.<\/p>\n<\/li>\n<li data-start=\"1747\" data-end=\"1873\">\n<p data-start=\"1749\" data-end=\"1873\"><strong data-start=\"1749\" data-end=\"1760\">Genome:<\/strong> The complete set of genetic material within an organism, encompassing all of its genes and non-coding regions.<\/p>\n<\/li>\n<li data-start=\"1874\" data-end=\"2124\">\n<p data-start=\"1876\" data-end=\"2124\"><strong data-start=\"1876\" data-end=\"1917\">Single Nucleotide Polymorphism (SNP):<\/strong> The most common type of genetic variation among people, involving a change of a single nucleotide in the DNA sequence, occurring approximately once every 300 bases (Genetic Science Learning Center, 2016).<\/p>\n<\/li>\n<li data-start=\"2125\" data-end=\"2384\">\n<p data-start=\"2127\" data-end=\"2384\"><strong data-start=\"2127\" data-end=\"2168\">Genome-Wide Association Study (GWAS):<\/strong> A research approach that involves scanning entire genomes of many individuals to identify genetic variants, particularly SNPs, associated with specific traits or diseases, such as addiction (Baurley et al., 2016).<\/p>\n<\/li>\n<li data-start=\"2385\" data-end=\"2667\">\n<p data-start=\"2387\" data-end=\"2667\"><strong data-start=\"2387\" data-end=\"2413\">Polygenic Score (PGS):<\/strong> A quantitative index that aggregates the tiny effects of many genetic variants (typically SNPs) identified in GWAS to summarize inherited liability for a trait. A PGS shifts group-level probabilities; it does not determine outcomes for any one person.<\/p>\n<\/li>\n<\/ul>\n<hr data-start=\"2669\" data-end=\"2672\" \/>\n<h2 data-start=\"2674\" data-end=\"2692\">I. Introduction<\/h2>\n<p data-start=\"2694\" data-end=\"3360\">In 2003, the Human Genome Project achieved a near-complete sequence of the human genome after 13 years of relentless effort and a staggering investment of around \u00a32 billion. Just two decades later, advancements in sequencing technologies have accelerated this process exponentially. By 2022, the Wellcome Sanger Institute showcased the power of modern sequencing by producing a human genome every 12 minutes\u2014a stark contrast to the painstaking pace of the past (Wellcome Sanger Institute, 2022). Today, entire genomes can be sequenced in less than an hour, revolutionizing our understanding of genetics and opening new frontiers in biology, medicine, and healthcare.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"z-0 flex min-h-[46px] justify-start\"><\/div>\n<div class=\"mt-3 w-full empty:hidden\">\n<div class=\"text-center\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<div class=\"pointer-events-none h-px w-px absolute bottom-0\" aria-hidden=\"true\" data-edge=\"true\">\n<figure id=\"attachment_128\" aria-describedby=\"caption-attachment-128\" style=\"width: 672px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-128 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS.jpg\" alt=\"Infographic showing the DNA sequencing workflow from DNA extraction to sequencing and computational analysis\" width=\"672\" height=\"378\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS.jpg 672w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS-300x169.jpg 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS-65x37.jpg 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS-225x127.jpg 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/GWAS-350x197.jpg 350w\" sizes=\"auto, (max-width: 672px) 100vw, 672px\" \/><figcaption id=\"caption-attachment-128\" class=\"wp-caption-text\">Figure 1. DNA sequencing workflow. Courtesy: National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH), 2023. Public domain. Source: genome.gov (DNA Sequencing Fact Sheet).<\/figcaption><\/figure>\n<p>In the previous module, we developed an intuition for heritability and foundational methodologies in behavioral genetics. We now turn to the molecular structures that underlie heritability. Whereas an atom is the smallest unit of an element (carbon, hydrogen, oxygen, etc.) that keeps its chemical identity, molecules are two or more atoms held together by chemical bonds (water, carbon dioxide, etc.). Our focus is the biomolecules of genetics: DNA, RNA, and proteins.<\/p>\n<p>In this module, we explore molecular genetics, genome-wide association studies (GWAS), and polygenic scores (PGS) to understand how these technologies help scientists uncover complex relationships between genes and behavior, particularly in the context of addiction.<\/p>\n<p>Additionally, we consider ethical challenges and responsible approaches to behavioral genetics in addiction science.<\/p>\n<h2>II. Molecular Genetics<\/h2>\n<p>Understanding a few molecular genetics basics helps us interpret how DNA variation can relate to behavior, including addiction. This section introduces DNA, genes, chromosomes, the genome, and common genetic variation called single-nucleotide polymorphisms (SNPs).<\/p>\n<h3>DNA and Genes<\/h3>\n<p>DNA (deoxyribonucleic acid) carries the biological instructions for life. It consists of two complementary strands twisted into a double helix, like a spiral ladder. The sugar-phosphate backbones form the sides, and paired bases form the rungs. DNA uses four bases: A (adenine), T (thymine), C (cytosine), and G (guanine). A pairs with T, and C pairs with G. The order of the bases, or sequence, functions like biological \u201ctext\u201d that cells read to build proteins, which perform most life functions.<\/p>\n<p>When a cell needs a protein, it copies a specific stretch of DNA into RNA (ribonucleic acid). RNA is usually single-stranded and uses U (uracil) instead of T. The short-lived copy, messenger RNA (mRNA), is then read in three-base codons to assemble a protein.<\/p>\n<p>A gene is a stretch of DNA whose sequence is transcribed into RNA, typically to make a protein. Genes are passed from parents to offspring and influence traits such as eye color and disease susceptibility. A chromosome is a long, tightly packaged DNA molecule containing many genes, wrapped around proteins. Chromosomes are located in the cell nucleus in most cells. Humans have 23 pairs of chromosomes (46 total), with one chromosome in each pair inherited from each parent. The genome is the complete DNA sequence across all chromosomes in a person, totaling about 3 billion base pairs in humans.<\/p>\n<figure id=\"attachment_129\" aria-describedby=\"caption-attachment-129\" style=\"width: 624px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-129 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA.png\" alt=\"Here\u2019s a clean, ready-to-paste image-generation prompt that matches the \u201cConstructing a PGS\u201d steps (select SNPs + LD pruning, assign GWAS weights, compute weighted sum, standardize to z-score\/percentile).&#96;&#96;&#96;text Create a high-clarity, textbook-ready vector infographic (16:9 landscape, white background, 4K resolution) titled: \u201cHow a Polygenic Score (PGS) is Built\u201d Design style: - Clean, minimal, modern scientific infographic - Thick, consistent line icons, high contrast, large readable labels - Use a restrained palette (navy\/blue + gray + one accent), no gradients, no clutter - All text must be legible when viewed on a lecture slide Layout: A left-to-right 5-step pipeline with rounded rectangles and arrows between steps. Each step has (1) a simple icon, (2) a bold step label, and (3) 1\u20132 short explanatory lines. Step 1 (Input): \u201cGWAS summary statistics\u201d - Icon: Manhattan-style bars or a simple bar chart - Text: \u201cEffect sizes (\u03b2) and p-values from a GWAS\u201d Step 2: \u201cSelect SNPs + LD clumping\/pruning\u201d - Icon: a cluster of dots with some removed, or linked nodes with a few crossed out - Text: \u201cChoose SNPs by p-value threshold (strict or more inclusive)\u201d - Text: \u201cRemove highly correlated SNPs (LD pruning) to avoid double-counting\u201d Step 3: \u201cAssign weights (\u03b2) and read genotypes (0\/1\/2)\u201d - Icon: a small table with columns labeled \u201cGenotype (0,1,2)\u201d and \u201cWeight (\u03b2)\u201d - Text: \u201cWeight each SNP using GWAS effect size (\u03b2 or log odds ratio)\u201d - Text: \u201cCount risk alleles per SNP: 0, 1, or 2\u201d Step 4: \u201cCompute the raw PGS (weighted sum)\u201d - Icon: calculator or summation symbol - Show the formula prominently inside the box: \u201cPGS\u1d62 = \u03a3\u2c7c (\u03b2\u2c7c \u00d7 G\u1d62\u2c7c)\u201d - Small note under formula: \u201cAdd up many tiny genetic nudges\u201d Step 5 (Output): \u201cStandardize for interpretation\u201d - Icon: a speedometer gauge + a small bell curve - Text: \u201cConvert to z-score (standard deviations) or percentile\u201d - Text: \u201cExample: 90th percentile = higher than 90% of the sample\u201d Footer (single line, centered): \u201cFrom many small SNP effects to one standardized score: PGS = \u03a3(allele count \u00d7 GWAS weight)\u201d Constraints: - No extra panels, no distracting background patterns - No logos, no trademarks - Prioritize clarity and readability over decorative detail\" width=\"624\" height=\"338\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA.png 624w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA-300x163.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA-65x35.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA-225x122.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/DNA-350x190.png 350w\" sizes=\"auto, (max-width: 624px) 100vw, 624px\" \/><figcaption id=\"caption-attachment-129\" class=\"wp-caption-text\">Figure 2. Gene\u2013DNA\u2013chromosome relationship and protein production. <strong>Illustration by Laura Olivares Bold\u00fa \/ Wellcome Connecting Science (<a href=\"https:\/\/www.yourgenome.org\/theme\/what-is-a-chromosome\/\">YourGenome<\/a>), CC BY 4.0.<\/strong><\/figcaption><\/figure>\n<h3>Gene Expression<\/h3>\n<p>Gene expression is the process by which information from a gene is used to create a functional product, usually a protein. Not all genes are active at all times. Gene regulation controls when and how much of a protein is made, ensuring that proteins are produced as needed by the cell.<\/p>\n<h3>Genotype vs Phenotype<\/h3>\n<p>The genotype is the genetic makeup of an individual, meaning the specific set of genes they carry. The phenotype is the observable traits or characteristics that result from the interaction of the genotype with the environment. For example, a gene may influence a person&#8217;s tendency toward impulsivity, which, combined with environmental factors, can affect behavior.<\/p>\n<h3>Alleles: Different Versions of a Gene<\/h3>\n<p>Genes often exist in slightly different forms known as alleles. Each individual inherits two alleles for every gene, one from each parent. While many alleles have little or no influence on observable traits, some can lead to differences in characteristics, such as eye color, or predispositions to certain behaviors or diseases. For example, a gene that influences eye color may have one allele for blue eyes and another for brown eyes.<\/p>\n<p>The combination of alleles you carry can influence how genes are expressed and, in some cases, how you respond to your environment, medications, and other external factors. Additionally, Single Nucleotide Polymorphisms (SNPs) represent a type of allelic variation where a single nucleotide differs between alleles. Allelic variation is therefore an important concept in understanding genetic diversity and how traits and health outcomes vary between individuals and across populations.<\/p>\n<figure id=\"attachment_131\" aria-describedby=\"caption-attachment-131\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-131 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-1024x683.png\" alt=\"Two blue homologous chromosomes with matching colored bands connected to labels for example traits: lactose tolerance, taste sensitivity (PTC), caffeine metabolism, handedness, and ear lobe attachment.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/alleles.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-131\" class=\"wp-caption-text\">Figure 3. Example of traits mapped to corresponding regions on homologous chromosomes, illustrating that the same loci appear in the same positions on both chromosome copies. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.<\/figcaption><\/figure>\n<\/div>\n<h3>Genotype and Phenotype<\/h3>\n<p>The term genotype refers to one\u2019s genetic makeup (the specific alleles one carries), whereas phenotype refers to observable characteristics or outcomes (which result from the interaction of genotype with environment). For instance, a person might have a genetic allele that increases impulsivity (genotype), but whether that manifests in behavior (phenotype) could depend on upbringing, stress, and other environmental factors.<\/p>\n<p>It\u2019s crucial to remember that for complex traits like addiction vulnerability, there is no single \u201caddiction gene.\u201d Instead, many genes each make small contributions. In other words, such traits are polygenic (influenced by multiple genes).<\/p>\n<h3>Single-Nucleotide Polymorphisms (SNPs)<\/h3>\n<p>One common type of genetic variation is the single nucleotide polymorphism (SNP), essentially a single \u201cletter\u201d difference in the DNA sequence between individuals. For example, at a particular position in the genome one person might have an A, while another has a G. Each SNP is like a single-character typo or variation in a very long book.<\/p>\n<p>SNPs occur roughly once in every 300 base pairs on average, meaning there are millions of SNP differences between any two people. Most SNPs have no effect on health, but some can influence how genes function or how proteins are made. As such, SNPs can alter how a person responds to a drug, or their susceptibility to a health condition, including possibly their predisposition to addiction.<\/p>\n<p>Because SNPs are so abundant and spread throughout the genome, they serve as useful markers for researchers. Modern genotyping technologies, such as SNP microarrays, can test hundreds of thousands of SNPs in a person\u2019s DNA quickly and cost-effectively. This allows scientists to create a genome-wide \u201cfingerprint\u201d of genetic variants for each individual in a study. These data feed the analyses you\u2019ll see next.<\/p>\n<p>A recommended quick resource to learn more about SNPs can be found at <a href=\"https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/\"><em>Making SNPs Make Sense<\/em><\/a> from the Genetics Science Learning Center (2016).<\/p>\n<figure id=\"attachment_130\" aria-describedby=\"caption-attachment-130\" style=\"width: 683px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-130 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-683x1024.png\" alt=\"Two-panel diagram showing how a DNA coding region (gene) makes a protein, and how a single-nucleotide polymorphism (SNP) in the gene can change the genotype and potentially alter the protein.\" width=\"683\" height=\"1024\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-683x1024.png 683w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-200x300.png 200w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-768x1152.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-65x98.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-225x338.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs-350x525.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SNPs.png 1024w\" sizes=\"auto, (max-width: 683px) 100vw, 683px\" \/><figcaption id=\"caption-attachment-130\" class=\"wp-caption-text\">Figure 4. A gene\u2019s DNA sequence encodes a protein; a SNP (single-letter DNA change) can create a variant genotype and may alter the resulting protein. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.<\/figcaption><\/figure>\n<h3>Genome-Wide Association Studies (GWAS)<\/h3>\n<p>How do scientists go from raw DNA data to discovering which genetic variants might increase the risk of disease, or show associations with phenotypes like addiction? The answer is often through genome-wide association studies, commonly abbreviated as GWAS (pronounced \u201cGEE-wahs\u201d).<\/p>\n<p>A GWAS is a systematic scan of the entire genome, comparing many people to see whether specific genetic variants (usually SNPs) are associated with a particular trait or disease. For a visual overview, see the cartoon explainer of GWAS developed by the Broad Institute (2017).<\/p>\n<p>In a GWAS, researchers examine hundreds of thousands, or even millions, of SNPs across the genome in a large group of individuals. The goal is to identify SNPs that are statistically more frequent in people with the trait of interest (for example, nicotine addiction) compared to people without the trait.<\/p>\n<p>If a particular SNP is significantly more common in the cases (those with the addiction) than in the controls (those without), that SNP is flagged as potentially associated with the trait.<\/p>\n<figure id=\"attachment_132\" aria-describedby=\"caption-attachment-132\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-132 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-1024x576.jpg\" alt=\"Diagram illustrating a genome-wide association study (GWAS), comparing people with a disease vs without a disease across multiple SNPs, showing one SNP associated with disease.\" width=\"1024\" height=\"576\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-1024x576.jpg 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-300x169.jpg 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-768x432.jpg 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-1536x864.jpg 1536w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-65x37.jpg 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-225x127.jpg 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI-350x197.jpg 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Gwas_NHGRI.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-132\" class=\"wp-caption-text\">Figure 5. Genome-wide association study (GWAS) concept: compare SNP frequencies in individuals with and without a disease to identify variants associated with disease risk. Image Credit: <a href=\"https:\/\/www.genome.gov\/about-genomics\/fact-sheets\/Genome-Wide-Association-Studies-Fact-Sheet\">NHGRI 2020<\/a>.<\/figcaption><\/figure>\n<h3>The Process of GWAS<\/h3>\n<p>At the outset, researchers must define the phenotype, or trait, they are studying. In addiction research, a phenotype might be a clinical diagnosis, such as opioid use disorder defined by diagnostic criteria, or a quantitative measure, such as the number of cigarettes smoked per day.<\/p>\n<p>Once the phenotype is defined and DNA is collected, usually through blood or saliva samples, genotyping is performed using SNP arrays or sequencing technologies. This process yields each individual\u2019s genotype at hundreds of thousands or even millions of SNPs. Researchers then conduct a statistical test for each SNP to evaluate whether variation at that location is associated with the trait of interest.<\/p>\n<p>Because so many SNPs are tested, researchers apply a very stringent standard for statistical significance. This high threshold helps distinguish true associations from results that could arise by random chance. Any SNP that surpasses this cutoff is considered genome-wide significant and treated as a strong candidate for a real association.<\/p>\n<p>Replication is essential. To be confident that a finding is not a statistical fluke or a quirk of a single dataset, significant SNP\u2013trait associations should be confirmed in an independent sample. Replication strengthens confidence that the association reflects a genuine biological signal.<\/p>\n<h3>Interpreting GWAS Results<\/h3>\n<p data-start=\"31\" data-end=\"399\">GWAS results are commonly visualized using a Manhattan plot, named for its resemblance to a city skyline. Each dot represents a SNP, plotted by its genomic position along the x-axis (typically chromosomes 1\u201322) and by the strength of its association with the trait on the y-axis (often shown as \u2212log10(p-value), where higher values mean stronger statistical evidence).<\/p>\n<p data-start=\"401\" data-end=\"881\">Most points appear near the bottom of the plot, indicating little or no association. Occasionally, tall \u201cskyscraper\u201d peaks rise above a horizontal threshold line, marking variants (or regions) that reach genome-wide significance. These peaks highlight genomic regions where variants are statistically associated with the trait. Because true associations are rare and effect sizes are usually small, only a limited number of regions typically stand out, even in very large studies.<\/p>\n<figure id=\"attachment_137\" aria-describedby=\"caption-attachment-137\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-137 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-1024x399.png\" alt=\"Manhattan plot of a genome wide association study: colored points grouped by chromosomes 1 through 22 on the x axis, and negative log10(p) on the y axis. Most points form a low band, with several tall peaks indicating stronger associations.\" width=\"1024\" height=\"399\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-1024x399.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-300x117.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-768x299.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-65x25.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-225x88.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot-350x136.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Manhattan_Plot.png 1092w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-137\" class=\"wp-caption-text\">Figure 6. Manhattan plot showing genome association with microcirculation (GWAS example). Source figure from Ikram et al. (2010), PLOS Genetics; shared via Wikimedia Commons under CC BY 2.5.<\/figcaption><\/figure>\n<h3>Interpreting a Manhattan Plot for Externalizing Behaviors<\/h3>\n<p data-start=\"1116\" data-end=\"1457\">Figure 6 provides a concrete example of how to read a Manhattan plot. Each dot is a single DNA variant. The x-axis shows where that variant sits in the genome, organized by chromosome. The y-axis shows how strong the statistical evidence is that the variant is associated with the phenotype being studied. Higher dots mean stronger evidence.<\/p>\n<p data-start=\"1459\" data-end=\"1973\">The dashed horizontal line represents the genome-wide significance threshold. Peaks that cross this line suggest genomic regions where many nearby variants show strong signals, often because they are correlated with each other (they tend to be inherited together). In practice, researchers do not stop at \u201ca tall peak.\u201d They check whether the signal replicates in an independent sample, and they follow up to identify which genes or biological pathways might plausibly connect that genomic region to the phenotype.<\/p>\n<p data-start=\"1975\" data-end=\"2250\"><strong data-start=\"1975\" data-end=\"2008\">Key takeaway (from Figure 6).<\/strong> Manhattan plots typically show a \u201cmany dots, few peaks\u201d pattern, reflecting that complex traits are influenced by many variants with small effects, plus a smaller number of variants or regions that clear a very strict significance threshold.<\/p>\n<h3 data-start=\"2252\" data-end=\"2307\">Connecting this back to externalizing and addiction<\/h3>\n<p data-start=\"2309\" data-end=\"2742\">Although Figure 6 is a GWAS example for microcirculation (not a behavioral trait), Manhattan plots for behavioral phenotypes use the same logic and look visually similar. In GWAS of externalizing-related traits (such as impulsivity, rule-breaking, and early substance use), researchers also look for peaks above the genome-wide threshold, interpret them as associated genomic regions, and require replication to confirm the findings.<\/p>\n<p data-start=\"2744\" data-end=\"3228\">Externalizing is widely understood as <strong data-start=\"2782\" data-end=\"2795\">polygenic<\/strong>, meaning there is no single \u201cgene for\u201d addiction or risk-taking. Instead, many small genetic influences are spread across the genome. Individually, each effect is tiny. Together, they can shift the odds at the group level. When these small effects are combined into a <strong data-start=\"3064\" data-end=\"3089\">polygenic score (PGS)<\/strong>, the score can capture some portion of variation in externalizing in large samples, but it does not predict destiny for any single person.<\/p>\n<p data-start=\"2744\" data-end=\"3228\">Figure 6 shows how to read the plot; for a domain-specific externalizing example, see Manhattan plots reported in large externalizing GWAS papers (for example, Karlsson et al., 2021).<\/p>\n<h3>Notable Findings from GWAS of Addiction<\/h3>\n<p>Recent large-scale GWAS have moved from mixed early results to several replicated discoveries that clarify substance-specific signals and shared vulnerability.<\/p>\n<ul>\n<li><strong>Alcohol Use Disorder (AUD).<\/strong> Meta-analyses repeatedly identify variants in alcohol-metabolizing enzymes, especially <strong>ADH1B<\/strong>, and also <strong>ADH1C<\/strong> and <strong>ALDH2<\/strong> (Zaso et al., 2019). Certain <strong>ADH1B<\/strong> alleles (common in some East Asian groups) are protective because they speed acetaldehyde buildup, producing an aversive flushing response (Cho et al., 2023).<\/li>\n<li><strong>Opioid Use Disorder (OUD).<\/strong> A coding SNP in <strong>OPRM1<\/strong> (the \u03bc-opioid receptor) shows a robust association with opioid dependence and replicates across cohorts (Zhou et al., 2020). This is biologically plausible because <strong>OPRM1<\/strong> is the receptor targeted by opioids like heroin and oxycodone (Gaddis et al., 2022). The finding underscores how receptor biology can shape individual risk.<\/li>\n<li><strong>Shared vulnerability across substances.<\/strong> A 2023 mega-analysis of more than 1 million individuals identified 19 independent markers for a cross-substance addiction risk factor spanning alcohol, nicotine, cannabis, and opioids (Hatoum et al., 2023). Many signals map to genes involved in dopamine signaling, highlighting shared reward circuitry. Higher genetic liability for addiction also correlates with elevated risk for several mental-health and medical conditions, suggesting overlapping genetic architecture.<\/li>\n<\/ul>\n<p>These examples illustrate that GWAS can point to both specific genes (like <strong>OPRM1<\/strong> or <strong>ADH1B<\/strong>) and broader biological systems (like dopamine regulation) as relevant to addiction. Each associated SNP is a clue. For example, it may affect receptor function or drug metabolism, which can influence responses to substances.<\/p>\n<h3>Limitations and Challenges of GWAS<\/h3>\n<p>GWAS are powerful discovery tools, but their findings come with important caveats that affect interpretation and use.<\/p>\n<ul>\n<li><strong>Small effects and polygenicity.<\/strong> Most associated SNPs shift risk by only a few percent, and complex traits like addiction are influenced by thousands of variants. Even large studies often explain only a small share of total risk, so GWAS signals are best viewed as small pieces of a much larger puzzle.<\/li>\n<li><strong>Population stratification.<\/strong> If cases and controls differ in ancestry, allele-frequency differences can create false associations unrelated to the trait. Statistical corrections (for example, principal components) help, but residual bias can remain. This is one reason diverse, well-matched samples matter.<\/li>\n<li><strong>\u201cMissing heritability\u201d and LD tagging.<\/strong> A significant SNP often tags a broader region because neighboring variants are inherited together (linkage disequilibrium). Identifying the causal change usually requires fine-mapping, sequencing, and additional study designs.<\/li>\n<li><strong>Need for functional follow-up.<\/strong> Association does not reveal mechanism. A variant might alter a protein, change gene expression, or affect regulation in specific tissues or developmental windows. Post-GWAS functional genomics is essential to translate hits into biology.<\/li>\n<li><strong>Winner\u2019s curse and replication.<\/strong> First reports tend to overestimate effect sizes, and some early findings fail to replicate. Independent replication and meta-analysis are necessary to confirm signals and obtain more accurate effect estimates.<\/li>\n<\/ul>\n<h2>Polygenic Scores (PGS)<\/h2>\n<p>A polygenic score (PGS) (or polygenic risk score \u2013 PGS) collapses information from many tiny genetic effects into a single number that estimates a person\u2019s inherited predisposition to a trait (e.g., addiction risk). In plain terms, it\u2019s a \u201cgenetic risk tally\u201d much like a credit score condenses your financial history into one number that shifts your likelihood of loan approval. A high PGS does not guarantee an outcome, and a low PGS does not prevent it. It changes probabilities.<\/p>\n<h3>Constructing a PGS<\/h3>\n<ul>\n<li><strong>Select SNPs (which DNA sites to include).<\/strong> Researchers start from GWAS results and decide which single-nucleotide polymorphisms (SNPs) to use. One strategy includes only SNPs that pass a very strict p-value cutoff (i.e., results unlikely to be due to chance). Another includes many more \u201csub-threshold\u201d SNPs because lots of very small signals can help prediction when combined. Researchers also remove highly correlated SNPs (called LD pruning) so the score does not double-count the same signal.<\/li>\n<li><strong>Assign a weight to each SNP (how strongly it relates to the trait).<\/strong> Each included SNP gets a weight based on its effect size from the GWAS, typically a regression beta or a (log) odds ratio. In plain language, this number tells you how much carrying one copy of the risk allele nudges the trait up or down. Positive weights push risk higher, negative weights push it lower.<\/li>\n<li><strong>Calculate the person\u2019s score (sum the weighted alleles).<\/strong> For each SNP, count how many risk alleles the person has, 0, 1, or 2, and multiply by that SNP\u2019s weight. Then add those products across all selected SNPs: <strong>PGS = \u03a3 (allele count \u00d7 weight)<\/strong>. Conceptually, this is just \u201cadd up all the tiny nudges.\u201d If the GWAS reported odds ratios, analysts usually take logarithms first so the math becomes simple addition rather than multiplication.<\/li>\n<li><strong>Standardize the score (make it interpretable).<\/strong> After computing raw PGS values for everyone in a dataset, researchers typically standardize them, for example by converting to a z-score (how many standard deviations above or below the group average) or to a percentile. In plain terms, a 90th-percentile PGS means your score is higher than 90% of people in that sample.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<figure id=\"attachment_139\" aria-describedby=\"caption-attachment-139\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-139 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-1024x683.png\" alt=\"Five-step left-to-right flowchart showing how a polygenic score (PGS) is built: GWAS summary statistics \u2192 LD clumping\/pruning \u2192 apply GWAS effect-size weights (\u03b2) to genotypes (0\/1\/2) \u2192 sum weighted SNP effects \u2192 standardize into a z-score or percentile; formula shown as PGS\u1d62 = \u03a3 \u03b2\u2c7c\u00b7G\u1d62\u2c7c.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/buildPGS.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-139\" class=\"wp-caption-text\">Figure 7. Workflow for constructing a polygenic score (PGS) from GWAS effect sizes by selecting SNPs, weighting genotypes, summing across variants, and standardizing the resulting score for interpretation. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.<\/figcaption><\/figure>\n<h3>What a PGS Tells Us (and Does Not)<\/h3>\n<ul>\n<li><strong>Probabilities, not certainties.<\/strong> A higher PGS means a higher likelihood, on average in large groups, of the trait. It is not a guarantee for any one person. Like a credit score, it shifts odds, and behavior and context still matter.<\/li>\n<li><strong>Context matters.<\/strong> Environment and choices can amplify or buffer genetic liability. For example, a person with a high PGS for alcohol problems who never drinks will not express that risk, while high stress or easy access could increase risk for someone who does drink.<\/li>\n<\/ul>\n<figure id=\"attachment_142\" aria-describedby=\"caption-attachment-142\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-142 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-1024x683.png\" alt=\"Synthetic illustration of substance use outcomes across polygenic score (PGS) groups\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/SyntheticPGS.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-142\" class=\"wp-caption-text\">Figure 8. Created by the author using synthetic (simulated) data for educational purposes. Conceptually informed by findings from Karlsson Linn\u00e9r et al. (2021), Nature Neuroscience, but does not reproduce or depict original study data or figures. Released under a Creative Commons Attribution (CC BY 4.0) license. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.<\/figcaption><\/figure>\n<p>Figure 8 illustrates this principle using a <strong data-start=\"1004\" data-end=\"1032\">synthetic (hypothetical)<\/strong> example of substance use outcomes across groups of individuals binned by their PGS. In this illustration, average rates of alcohol use disorder (AUD), illicit drug use, and opioid use increase as we move from the lowest-scoring 20% to the highest 20%. The key point is the <strong data-start=\"1306\" data-end=\"1335\">shape of the relationship<\/strong>, not the exact numbers: the increases are gradual, not absolute. Many people with high scores never develop problems, and some with lower scores still do.<\/p>\n<p>This matches what real studies typically find, including large-scale analyses of externalizing and substance outcomes (Karlsson Linn\u00e9r et al., 2021). The takeaway is that polygenic scores shift group averages. They help explain patterns at the population level, while individual outcomes remain shaped by many other genetic, environmental, and behavioral factors.<\/p>\n<p>&nbsp;<\/p>\n<figure id=\"attachment_140\" aria-describedby=\"caption-attachment-140\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-140 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-1024x683.png\" alt=\"Vector infographic of a normal bell curve titled \u201cDistribution of Polygenic Scores (PGS) in a Sample,\u201d with axes labeled Density (y-axis) and PGS (z-score) (x-axis). The curve shows vertical lines at the 10th, 50th (dashed), and 90th percentiles, with labels for Lower PGS and Higher PGS and shading that is darker in both tails.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/PGS-distribution.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-140\" class=\"wp-caption-text\">Figure 9. Normal distribution of polygenic scores in a sample, illustrating relative position using the 10th, 50th (median), and 90th percentiles. Created by the author. <em data-start=\"3561\" data-end=\"3579\">Production note:<\/em> created using generative AI and edited by the author.<\/figcaption><\/figure>\n<p>Figure 9 reinforces two key points:<\/p>\n<ul>\n<li>Being in a \u201chigh\u201d or \u201clow\u201d group is always relative to others in the sample, not an absolute threshold.<\/li>\n<li>The vast majority of people are clustered around the middle of the distribution, where genetic liability is modest.<\/li>\n<\/ul>\n<p>Taken together, Figures 8 and 9 show that polygenic scores reflect a continuum of probabilities, not destinies. Higher scores may nudge risk upward on average, but there is no sharp dividing line between \u201caffected\u201d and \u201cunaffected.\u201d<\/p>\n<h2>Common Research Uses of PGS<\/h2>\n<ul>\n<li><strong>Stratifying risk in studies.<\/strong> Researchers can compare outcomes for participants with higher versus lower PGS to see whether trajectories (e.g., earlier initiation or faster escalation) differ.<\/li>\n<li><strong>Testing gene\u2013environment interplay.<\/strong> PGS provides a single summary of genetic liability that can be interacted with environmental measures (e.g., stress, parental monitoring) to test whether context changes genetic effects.<\/li>\n<li><strong>Adjusting for inherited propensity.<\/strong> Studies evaluating programs or exposures can include PGS as a covariate to account for baseline genetic differences among participants.<\/li>\n<li><strong>Exploring shared biology.<\/strong> PGS for one trait (e.g., depression) can be tested against another outcome (e.g., substance use) to probe pleiotropy, meaning shared genetic influences across conditions.<\/li>\n<\/ul>\n<h2>Limitations of PGS<\/h2>\n<ul>\n<li><strong>Ancestry transferability.<\/strong> Polygenic scores often do not transfer well across populations with different ancestral backgrounds. If the GWAS that supplied the effect sizes was mostly European-ancestry, the same score can predict poorly for people of East Asian, African, or admixed ancestries because allele frequencies and effect sizes can differ. This is why increasing diversity in genetic research is essential so that PGS are equitable and accurate for all groups.<\/li>\n<li><strong>Small variance explained.<\/strong> A PGS usually accounts for only a small slice of the overall differences among people (e.g., \u201cabout 5% of variance\u201d in liability). In plain language, most of what makes individuals similar or different on the trait is not captured by the score. Other genes, rare variants, environment, and chance all matter. As a result, prediction for a single person is limited: some people with high scores will not develop problems, and some with low scores will.<\/li>\n<li><strong>Environment still shapes outcomes.<\/strong> PGS captures genetic propensity, not life context. Two people with the same score can have very different outcomes if one grows up in a supportive, low-risk environment and the other faces high stress or easy access to substances. Put simply, the score nudges probabilities, but environments and choices can amplify, mute, or prevent expression of that liability.<\/li>\n<li><strong>Risk of misunderstanding or misuse.<\/strong> Without careful explanation, people may interpret a high PGS as destiny (\u201cit\u2019s in my genes, there\u2019s nothing I can do\u201d) or panic about stigmatizing labels. Institutions could also be tempted to use scores inappropriately, hence the importance of legal protections and strong ethics guidance discussed later in the module. Clear communication should emphasize that PGS shifts odds. It does not define an individual.<\/li>\n<\/ul>\n<p>Despite these caveats, PGS research is valuable because it aggregates many tiny genetic effects into a usable summary, helping scientists study risk patterns and gene\u2013environment interplay. For complex traits like addiction, there is no single \u201cgene for\u201d the outcome. Many small influences add up, and PGS gives us one careful way to quantify that aggregate.<\/p>\n<h2>Ethics and Responsible Use of PGS<\/h2>\n<h3>Why genetics research must be handled responsibly<\/h3>\n<p>Scenario to frame our ethics lens: In 2018, media covered clinics exploring embryo screening for polygenic disease risk. Imagine adding \u201caddiction risk\u201d to that list. Who decides what counts as \u201chigh risk\u201d? Could that label follow a person for life? This section equips you to spot risks and communicate findings responsibly so the science helps people, not harms them.<\/p>\n<h3>1) Data privacy and informed consent<\/h3>\n<p>Genetic data are uniquely identifying. Even \u201cde-identified\u201d datasets can sometimes be re-identified when combined with other information. Treat DNA as high-risk personal data: use robust security, least-privilege access, and controlled-access repositories. In ABCD and similar studies, qualified researchers access genetic files through gated systems designed to protect participant confidentiality.<\/p>\n<p>Informed consent must be specific and clear. Participants (and caregivers) should understand what will be measured, how data will be stored and shared, who may access it, whether results might be reused in future studies, and their right to withdraw. Make these elements explicit whenever genomic data are collected or analyzed.<\/p>\n<h3>2) Avoiding genetic determinism and stigma<\/h3>\n<p>Communicate probabilities, not destinies. GWAS and PGS shift odds at the population level. They do not define any one person. Environment and choices still matter. Scores nudge risk but do not fix outcomes.<\/p>\n<p>Name common misinterpretations up front. High PGS does not equal inevitability. Low PGS does not equal immunity. Warn against institutional misuse and note that legal and ethical guardrails restrict certain uses (e.g., U.S. protections like GINA for employers and health insurers).<\/p>\n<p>Adopt precise, respectful language.<\/p>\n<p><strong>Do say:<\/strong><\/p>\n<ul>\n<li>\u201cGenetic liability increases average risk in some contexts.\u201d<\/li>\n<li>\u201cHeritability is a population statistic.\u201d<\/li>\n<li>\u201cSupports can buffer risk.\u201d<\/li>\n<li>\u201cResults may be age-graded and context-dependent.\u201d<\/li>\n<\/ul>\n<p><strong>Don\u2019t say:<\/strong><\/p>\n<ul>\n<li>\u201cBorn to be addicted.\u201d<\/li>\n<li>\u201cHigh heritability means environments don\u2019t matter.\u201d<\/li>\n<li>\u201c50% heritable means half of your behavior is genetic.\u201d<\/li>\n<li>\u201cHeritability explains between-group differences.\u201d<\/li>\n<\/ul>\n<h3>3) Equity and diversity in genetic research<\/h3>\n<p>Why representation matters. Many GWAS historically over-sampled European-ancestry participants. As a result, PGS often transfers poorly to other ancestries because allele frequencies and effect sizes can differ, reducing accuracy and fairness. Prioritize diverse cohorts, transparent documentation, and cross-ancestry validation so findings benefit everyone.<\/p>\n<p>Avoid scientific racism and typological thinking. Race and ethnicity are social constructs and not proxies for genetic ancestry. Use precise genetic markers; justify any population descriptors; be transparent about classification methods; and acknowledge limits to generalizability.<\/p>\n<p>Keep between-group and within-group inferences separate. Heritability within a group cannot explain differences between groups living in different contexts. Maintain an equity lens: focus on supports and opportunity structures, not \u201cdeficits.\u201d<\/p>\n<h3>4) Responsible use of genetic data (policy and practice)<\/h3>\n<p>Purpose-bound use and minimum necessary. Collect and analyze only what you need; avoid repurposing data without consent. Use controlled-access workflows, log decisions, and align with IRB and data-use agreements.<\/p>\n<p>Guardrails against misuse. Clearly state that GWAS and PGS are research tools and are unsuitable for labeling, tracking, or penalizing individuals (e.g., in schools, workplaces, or insurance). Note that legal protections (e.g., GINA) and evolving policies aim to reduce discrimination, but vigilance and clear communication remain essential.<\/p>\n<p>Language that reduces harm. Carry forward Module 4\u2019s communication principles: use neutral, action-oriented phrasing; avoid deterministic labels; and foreground modifiable supports (mentoring, routines, coping skills, evidence-based prevention).<\/p>\n<p>Closing note: balanced optimism. Ethical risks are real, but they are manageable. Strong privacy practices, clear consent, diverse study designs, careful communication, and community engagement all improve the science and its impact. Used responsibly, GWAS and PGS can illuminate mechanisms, sharpen study design, and ultimately inform prevention without stigmatizing individuals or communities.<\/p>\n<h2>ABCD Study\u2019s Genetic Data and Resources<\/h2>\n<p>The ABCD Study collects genomic data to illuminate how inherited differences contribute to brain development and addiction-related behaviors across adolescence. Below is how ABCD gathers, processes, and shares genetic data, the design features that make it uniquely powerful, and how researchers are using these resources, alongside a reminder that all work with DNA requires careful ethical stewardship.<\/p>\n<h3>How is DNA collected?<\/h3>\n<ul>\n<li><strong>Saliva (primary method).<\/strong> ABCD obtains DNA primarily via saliva, non-invasive and youth-friendly, then extracts genomic material for downstream analyses.<\/li>\n<li><strong>Biospecimen note.<\/strong> ABCD collects additional biospecimens for other scientific aims (e.g., exposure assays), but genotyping for genetics is based on saliva-derived DNA.<\/li>\n<\/ul>\n<h3>How is the genetic information analyzed?<\/h3>\n<ul>\n<li><strong>Genotyping platform.<\/strong> ABCD has used the Axiom Smokescreen array, enabling dense coverage of SNPs relevant to neurodevelopment and substance-use research.<\/li>\n<li><strong>What variants are studied?<\/strong> Analyses focus on single-nucleotide polymorphisms (SNPs). Post-GWAS tools (e.g., polygenic scores) summarize many tiny effects into a single index of inherited liability.<\/li>\n<li><strong>Quality control and population structure.<\/strong> Standard QC (e.g., call-rate thresholds, Hardy\u2013Weinberg checks, sex\/relatedness concordance) precedes analyses. Ancestry inference (e.g., principal components) helps limit confounding from population structure.<\/li>\n<li><strong>Imputation.<\/strong> Genotypes are imputed against reference panels to expand variant coverage beyond the typed SNPs.<\/li>\n<\/ul>\n<h3>Data availability and access<\/h3>\n<ul>\n<li><strong>Versioned, documented releases.<\/strong> Genetic datasets are distributed with rigorous QC documentation to support reproducibility.<\/li>\n<li><strong>Controlled access.<\/strong> Qualified investigators obtain de-identified data through controlled-access repositories under data-use agreements, balancing scientific openness with participant privacy and consent.<\/li>\n<\/ul>\n<h3>Unique features of ABCD genetics<\/h3>\n<ul>\n<li><strong>Population breadth.<\/strong> ABCD enrolls participants from diverse U.S. communities, improving generalizability and enabling tests of portability for GWAS findings and polygenic scores across backgrounds.<\/li>\n<li><strong>Twin\/sibling design.<\/strong> A substantial twin and sibling subsample allows researchers to combine behavioral genetic designs (A\/C\/E, discordant-twin contrasts) with molecular approaches (GWAS\/PGS) within the same longitudinal cohort, which is powerful for probing gene\u2013environment interplay and developmental timing.<\/li>\n<\/ul>\n<h3>How researchers use the ABCD genetic data<\/h3>\n<ul>\n<li><strong>Validating biology of the brain.<\/strong> Linking genomic variation to neuroimaging and cognitive phenotypes to test whether previously reported brain-related loci generalize to youth.<\/li>\n<li><strong>Psychiatric and behavioral liability.<\/strong> Examining how polygenic propensities for psychiatric conditions relate to emerging psychopathology and substance-use trajectories in adolescence (Fan et al., 2023: 160).<\/li>\n<li><strong>Cross-trait architecture and G\u00d7E.<\/strong> Exploring shared genetic architecture across externalizing, internalizing, and substance phenotypes, and testing how environmental contexts (family, school, neighborhood) tune genetic effects over time.<\/li>\n<\/ul>\n<p>Ethics connection: Access controls, careful population descriptors, and clear consent are integral. See this module\u2019s Ethics and Responsible Use section for guidance on privacy, communication, equity, and responsible interpretation.<\/p>\n<hr \/>\n<h2>Works Cited<\/h2>\n<p>Auchter, A. M., Myers, C. E., Mann, J. B., &amp; Ryan, L. A. (2018). A description of the ABCD organizational structure and the development of a large-scale neurodevelopmental study. <em>Developmental Cognitive Neuroscience, 32<\/em>, 8\u201315. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.04.003\">https:\/\/doi.org\/10.1016\/j.dcn.2018.04.003<\/a><\/p>\n<p>Baurley, J. W., Edlund, C. K., Pardamean, C. I., Conti, D. V., &amp; Bergen, A. W. (2016). Smokescreen: A targeted genotyping array for addiction research. <em>Addiction Biology, 21<\/em>(3), 517\u2013525. <a href=\"https:\/\/doi.org\/10.1111\/adb.12345\">https:\/\/doi.org\/10.1111\/adb.12345<\/a><\/p>\n<p>Bird, K. A., &amp; Carlson, J. (2021). Typological thinking in human genomics research contributes to the production and prominence of scientific racism. Manuscript in preparation.<\/p>\n<p>Cardenas-Iniguez, C., &amp; Robledo Gonzalez, M. (2023). Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research. <em>Nature Neuroscience, 26<\/em>(1), 45\u201360. <a href=\"https:\/\/doi.org\/10.1038\/s41593-022-01234-5\">https:\/\/doi.org\/10.1038\/s41593-022-01234-5<\/a><\/p>\n<p>Casey, B. J., Cannonier, T., Conley, M. I., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. <em>Developmental Cognitive Neuroscience, 32<\/em>, 43\u201354. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.06.004\">https:\/\/doi.org\/10.1016\/j.dcn.2018.06.004<\/a><\/p>\n<p>Cho, Y., Lin, K., Lee, S. H., et al. (2023). Genetic influences on alcohol flushing in East Asian populations. <em>BMC Genomics, 24<\/em>, 638. <a href=\"https:\/\/doi.org\/10.1186\/s12864-023-09721-7\">https:\/\/doi.org\/10.1186\/s12864-023-09721-7<\/a><\/p>\n<p>Fan, C. C., Loughnan, R., &amp; ABCD Genetic Working Group. (2023). Genotype data and derived genetic instruments of Adolescent Brain Cognitive Development Study\u00ae for better understanding of human brain development. <em>Behavior Genetics, 53<\/em>(1), 31\u201345. <a href=\"https:\/\/doi.org\/10.1007\/s10519-022-10002-3\">https:\/\/doi.org\/10.1007\/s10519-022-10002-3<\/a><\/p>\n<p>Gaddis, N., Mathur, R., Marks, J., et al. (2022). Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. <em>Scientific Reports, 12<\/em>, 16873. <a href=\"https:\/\/doi.org\/10.1038\/s41598-022-21003-y\">https:\/\/doi.org\/10.1038\/s41598-022-21003-y<\/a><\/p>\n<p>Genetic Science Learning Center. (2016, February 1). Making SNPs Make Sense. Retrieved October 10, 2024, from <a href=\"https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/\">https:\/\/learn.genetics.utah.edu\/content\/precision\/snips\/<\/a><\/p>\n<p>Gymrek, M., Willems, T., Mandal, S., et al. (2013). Identifying personal genomes by surname inference. <em>Nature Genetics, 45<\/em>(6), 304\u2013309. <a href=\"https:\/\/doi.org\/10.1038\/ng.2644\">https:\/\/doi.org\/10.1038\/ng.2644<\/a><\/p>\n<p>Hatoum, A. S., Colbert, S. M. C., Johnson, E. C., et al. (2023). Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. <em>Nature Mental Health, 1<\/em>, 210\u2013223. <a href=\"https:\/\/doi.org\/10.1038\/s44220-023-00034-y\">https:\/\/doi.org\/10.1038\/s44220-023-00034-y<\/a><\/p>\n<p>Iacono, W. G., Heath, A. C., Hewitt, J. K., Neale, M. C., Banich, M. T., &amp; Luciana, M. (2018). The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design. <em>Developmental Cognitive Neuroscience, 32<\/em>, 30\u201342. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2017.09.001\">https:\/\/doi.org\/10.1016\/j.dcn.2017.09.001<\/a><\/p>\n<p>Karlsson Linn\u00e9r, R., Mallard, T. T., Barr, P. B., et al. (2021). Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. <em>Nature Neuroscience, 24<\/em>, 1367\u20131376. <a href=\"https:\/\/doi.org\/10.1038\/s41593-021-00908-3\">https:\/\/doi.org\/10.1038\/s41593-021-00908-3<\/a><\/p>\n<p>National Academies of Sciences, Engineering, and Medicine. (2018). National Academies Press. <a href=\"https:\/\/doi.org\/10.17226\/%5BDOI\">https:\/\/doi.org\/10.17226\/[DOI<\/a>]<\/p>\n<p>National Academies of Sciences, Engineering, and Medicine. (2023). <em>Using Population Descriptors in Genetics and Genomics Research<\/em>. National Academies Press. <a href=\"https:\/\/doi.org\/10.17226\/%5BDOI\">https:\/\/doi.org\/10.17226\/[DOI<\/a>]<\/p>\n<p>Plomin, R., DeFries, J. C., &amp; Fulker, D. W. (1988). <em>Nature and Nurture during Middle Childhood<\/em>. Blackwell.<\/p>\n<p>Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., &amp; Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. <em>Nature Genetics, 38<\/em>(8), 904\u2013909. <a href=\"https:\/\/doi.org\/10.1038\/ng1764\">https:\/\/doi.org\/10.1038\/ng1764<\/a><\/p>\n<p>Uban, K. A., Horton, M. K., Jacobus, J., Heyser, C., Thompson, W. K., Tapert, S. F., Madden, P. A. F., &amp; Sowell, E. R. (2018). Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data. <em>Developmental Cognitive Neuroscience, 32<\/em>, 97\u2013106. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.06.005\">https:\/\/doi.org\/10.1016\/j.dcn.2018.06.005<\/a><\/p>\n<p>Wellcome Sanger Institute. (2022). Human genome sequencing advancements. Retrieved from <a href=\"https:\/\/www.sanger.ac.uk\/\">https:\/\/www.sanger.ac.uk\/<\/a><\/p>\n<p>Zaso, M. J., Goodhines, P. A., Wall, T. L., &amp; Park, A. (2019). Meta-analysis on associations of alcohol metabolism genes with alcohol use disorder in East Asians. <em>Alcohol and Alcoholism, 54<\/em>(3), 216\u2013224. <a href=\"https:\/\/doi.org\/10.1093\/alcalc\/agz011\">https:\/\/doi.org\/10.1093\/alcalc\/agz011<\/a><\/p>\n<p>Zhou, H., Rentsch, C. T., Cheng, Z., et al. (2020). Association of OPRM1 functional coding variant with opioid use disorder: A genome-wide association study. <em>JAMA Psychiatry, 77<\/em>(10), 1072\u20131080. <a href=\"https:\/\/doi.org\/10.1001\/jamapsychiatry.2020.1206\">https:\/\/doi.org\/10.1001\/jamapsychiatry.2020.1206<\/a><\/p>\n","protected":false},"author":30,"menu_order":2,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-127","chapter","type-chapter","status-publish","hentry"],"part":27,"_links":{"self":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/127","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/users\/30"}],"version-history":[{"count":7,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/127\/revisions"}],"predecessor-version":[{"id":321,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/127\/revisions\/321"}],"part":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/parts\/27"}],"metadata":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/127\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/media?parent=127"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapter-type?post=127"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/contributor?post=127"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/license?post=127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}