Genome-Wide Association Study (GWAS)

A genome-wide association study, or GWAS, is a method for identifying variations in DNA that may contribute to the development of a particular trait, such as a disease. A GWAS relies on identifying statistical correlations between many, often thousands of, DNA markers and a particular trait. Scientists employ GWASs to try to identify the genetic contributions to complex traits, such as common human diseases. Complex traits are ones that scientists suspect are the result of multiple genes and environmental inputs acting together, in contrast to simple, Mendelian disorders that result primarily from the disturbance of a single gene. The genetic variants identified through a GWAS typically account for only a small proportion of the expected genetic contribution to a complex trait, which scientists refer to as the missing heritability problem. Since 2006, scientists have conducted thousands of GWASs aimed at identifying the genetic contributions to complex traits and have identified many thousands of genetic variations that correlate with those traits, although as of 2025, because of the missing heritability problem and other limitations, the concrete contributions of GWASs to medicine have so far been modest.

1. Background and Context
2. Origins of the GWAS
3. How Scientists Use a GWAS
4. The Case of Missing Heritability and Other Problems
5. Legacy and Impact as of 2025

Background and Context

The GWAS approach differs from previous methods of identifying genes that use linkage analysis. Linkage analysis starts with assembling family pedigrees, showing which family members inherited a particular trait going back generations. Then, the researcher looks for genetic markers that family members consistently inherited along with that trait. Starting in the mid-1970s, the genetic markers that scientists typically used for linkage analysis were restriction fragment-length polymorphisms, or RFLPs. A RFLP is a variation in the length of DNA fragments that result when scientists apply a particular DNA-cutting enzyme to a sample of DNA. If individuals always inherit a particular RFLP along with the trait, then that suggests to researchers that the gene underlying that trait may be near to, and therefore linked with, that RFLP. Some of the first genes that scientists tied to specific traits using linkage analysis were ones involved in rare, Mendelian diseases like Huntington’s disease, which causes nerve cells in the brain to decay over time, sickle cell anemia, an inherited blood cell disorder that causes red blood cells to become misshapen and leads to intense pain, and cystic fibrosis, which causes thick, sticky mucus to collect in the body’s ducts and airways, causing breathing difficulties among other problems.

When scientists sought to understand the genetic contributions to more common, complex diseases like heart disease, schizophrenia, and diabetes, they realized that relying on linkage analysis would not work, and they developed the GWAS approach to address that problem. Unlike traits that arise from single genes, most common traits, including common diseases, result from complex interactions between multiple genes and the environment. Moreover, the traits themselves are not discrete, meaning that they are neither clearly present nor clearly absent, but rather sometimes only partially present. Those factors, together, make it much more challenging to home in on particular genes that may be contributing to complex traits. With a GWAS, researchers attempt to circumvent those challenges by relying on newer technologies and statistical approaches that can detect associations between particular traits and thousands of genetic markers spread across the entire genome in thousands of individuals.

Origins of the GWAS

A 1996 article in the journal Science by researchers Neil Risch, then in the Genetics Department at Stanford University in Palo Alto, California, and Kathleen Merikangas, then in the Departments of Epidemiology and Psychiatry at Yale University School of Medicine in New Haven, Connecticut, was among the first to articulate the scientific reasoning behind a GWAS. In that article, Risch and Merikangas note that a number of previous studies have reportedly identified genes that might play a role in complex diseases, but they point out that researchers have been able to replicate very few of those findings. They argue in their paper that the modest effect of any one gene on the phenotype of a complex trait likely explains the contradictory and inconclusive results of prior studies. To identify such genes of modest effect in complex diseases, they call for an alternative to linkage analysis. The alternative they came up with is genome-wide association studies. Through a mathematical analysis, they show that an association study that analyzes one million genetic markers from a sample of unrelated individuals could be more powerful, statistically, than a linkage analysis that analyzes a few hundred markers. It took ten years before scientists were able to implement Risch and Merikangas’s approach.

At least four additional developments in biomedicine were necessary before scientists could conduct a GWAS, and the first was the creation of DNA sequencing technologies that permit scientists to identify DNA markers called single nucleotide polymorphisms, or SNPs, located throughout a person’s genome. A SNP, pronounced snip, is a one-letter change in a DNA sequence that occurs approximately once every one thousand base-pairs. The necessary DNA sequencing technology emerged in the 1990s out of the Human Genome Project, the massive, international effort to sequence the entire human genome, consisting of more than 3 billion bases, for the first time. By the end of the Human Genome Project in 2003, it was much more feasible to cheaply sequence DNA than it was before it began.

The second important piece was the development of the International HapMap, or haplotype map, which became available in 2003. A haplotype is a block of SNPs that people tend to inherit together. The HapMap shows what common haplotypes exist in the human population and provides researchers with the location of so-called tag SNPs that identify each haplotype uniquely. Scientists can use the map of haplotypes and the tag SNPs as a short cut to find genetic variants linked to specific traits. Instead of sequencing entire genomes and looking for associations between their trait of interest and every single SNP, of which there are more than ten million, scientists can instead look for associations between the trait and particular haplotypes, identified by specific tag SNPs, of which there are only around 500,000. Once they have located a haplotype of interest, they can then study its sequence in more detail to look for specific DNA sequence variations that may contribute to the trait.

The third necessary piece for a GWAS was the development of commercial DNA microarrays, also called gene chips, which are postage-stamp sized SNP-detection devices. Each chip contains probes for detecting a set number of tag SNPs. The first gene chips to become available, in the late 1990s, could detect over 1,400 SNPs. As of 2025, they can detect nearly one million SNPs. Scientists can apply a person’s DNA to the chip to determine which of the SNPs represented on the chip the person has. Two popular commercial makers of gene chips are Affymetrix and Illumina. Most public and private entities that conduct GWASs, including governments, universities, and commercial direct-to-consumer DNA testing companies, use gene chips made by one of those two companies.

The fourth necessary component for GWASs came in the mid-2000s with the creation of large biobanks that contain cell lines and DNA from thousands of individuals from all over the world. The existence of those biobanks allows researchers to take advantage of massive amounts of DNA information from many people without having to collect those samples themselves. Because GWASs rely on having large numbers of participants in order to achieve statistical significance for any finding, most GWASs that scientists have conducted to date have relied on data stored in one or more of several large biobanks, including the UK Biobank, deCODE Genetics, Estonian Biobank, FinGen, and 23andMe. Those biobanks contain not only the DNA sequences of thousands of individuals’ genomes, but also information about health and other traits of each person who donated their genome. Having those links between genotypes and phenotypes in a database is what makes large GWASs possible.

How Scientists Use a GWAS

Typically, scientists will employ a GWAS in a case-control study approach. That means that scientists will study a large group of people with a particular trait or disease, the cases, and another large group without that trait or disease, the controls. They will then analyze the genomes of both groups looking for genetic markers that are more common in the cases than in the controls. Once they identify those markers, they can look for specific genes near those markers that may be having an impact on the trait of interest. Sometimes, the pinpointing of a nearby gene can give scientists a clue about how the variation in that gene might be contributing to the trait. With that hypothesis in hand, they can then conduct laboratory experiments in animals to see if altering the gene affects the trait. Other times, the SNPs associated with a trait are not located near any genes and so the way they may contribute to the trait remains unknown. In all cases, a GWAS can detect only statistical correlations between DNA variations and particular traits. Because two variables can be correlated without one necessarily causing the other, a GWAS does not, by itself, indicate the genetic causes of traits. It takes more research to identify which of the associations, if any, is a causal one.

Nevertheless, the statistical associations that a GWAS identifies can help scientists make predictions about a person’s traits even without understanding anything about causes by calculating a person’s polygenic risk score, or PRS, based on results from that GWAS. A PRS is a number that represents the sum of the GWAS variants a person has for a trait. In theory, the higher a person’s PRS for a trait, the higher the chances that they will develop that trait. However, because GWASs themselves generally do not identify all of the suspected genetic contributions to a trait, the PRSs derived from them are not one hundred percent reliable. As a result, PRSs for common complex diseases have yet to become a routine part of medical care, although some researchers forecast that they will become part of medical care in the future.

One of the first GWASs was one that Robert Klein, a researcher who studied genetics at Rockefeller University in New York City, New York, and colleagues reported on in 2005. They used a GWAS to investigate the genetic contributions to age-related macular degeneration, a major cause of blindness. They compared the genomes of ninety-six people with the disease to the genomes of fifty people without the disease, looking for associations between the condition and more than 115,000 SNPs. In doing so, they identified a common SNP in a gene called complement factor H, or CFH, that is strongly associated with the disease. Individuals with two copies of that SNP have more than seven times the risk of developing the disease compared to someone without the SNP.

In 2007, The Wellcome Trust Case Control Consortium, a group of over fifty research groups working across the United Kingdom, published the results of a large GWAS that included the genomes of 17,000 people from the British Birth Cohort. The researchers examined the genomes of roughly 2,000 individuals for each of seven major diseases and compared them to 3,000 controls. They identified twenty-four SNPs that were associated with six out of seven of those diseases: one in bipolar disorder, one in coronary artery disease, nine in Crohn’s disease, three in rheumatoid arthritis, seven in type 1 diabetes, and three in type 2 diabetes. The authors call their study a thorough validation of the GWAS approach.

In 2023, according to The NHGRI-EBI Catalog of Human Genome-wide Association Studies, scientists conducted over 45,000 GWASs across 5,000 human traits to investigate thousands of diseases. Some of the largest GWASs to date, involving more than one million people each, have analyzed height, educational attainment, and insomnia.

The Case of Missing Heritability and Other Problems

While GWASs have proved successful at identifying previously unknown genetic contributions to complex traits, including diseases, they have also sparked criticism and controversy. The main criticism is that for any given trait and any given GWAS, the genomic variants identified explain only a small proportion—typically between five and ten percent—of the estimated genetic contribution to the trait. Scientists call that discrepancy the missing heritability problem. Heritability is a technical term that describes the proportion of the observed variation in a trait that can be explained by genetic variation as opposed to environmental variation within a particular population. A classic example of the missing heritability problem is human height. Scientists estimate that the heritability of human height is about eighty percent, which means that genetic variation is responsible for approximately eighty percent of the variability of human height, and environmental factors, such as nutrition, are responsible for the other approximately twenty percent. However, by 2009, GWASs had identified more than forty genetic variants that are associated with height, and yet they explained only about five percent of the variation in the trait, despite the fact that the studies have involved tens of thousands of people. Subsequently, a very large 2022 study led by a team of international researchers identified 12,000 SNPs from more than five million people that still only explained ten to forty percent of human height.

A possible explanation for the missing heritability in GWASs is that the underlying assumptions of the approach are flawed, and one main assumption is that common diseases are attributable, in part, to genetic variants that are present in at least five percent of people in a population. Scientists call that assumption the common disease, common variant hypothesis. If that assumption is wrong, that is, if the genetic variants contributing to a disease phenotype are not common, but rare—present at frequencies below five percent—then a GWAS will not be able to measure them. That is because researchers designed the gene chips for detecting SNPs to measure only common variants—those present at frequencies above five percent. So, for example, in the case of human height, if many of the SNPs influencing human height are each present in only two to four percent of the population, then they will go undetected by the gene chip. In turn, the total number of SNPs that the gene chip does measure and find to be associated with height will underestimate the identified genetic contribution. In other words, it will be a case of missing heritability.

A second major assumption underlying GWASs that scientists have speculated may be flawed is that geneticists have accurate ways of estimating the true heritability of a trait. In many cases, the source for estimates of a trait’s heritability comes from twin studies. Scientists estimate a trait’s heritability by comparing how much more frequently identical twins share a trait compared with fraternal twins. Identical twins develop from a single fertilized egg that subsequently splits to form two embryos and therefore share the same DNA sequence. Fraternal twins develop from two separate fertilized eggs and are no more genetically similar than other non-twin siblings. Comparing identical twins to fraternal twins who grew up in the same family thus provides an estimate of the genetic contribution to a trait. However, that manner of estimating heritability makes the assumption that there is no greater environmental similarity of identical compared with fraternal twins, which may not be the case because studies have shown that people often treat identical twins more similarly than they do fraternal twins, for example. That assumption could end up inflating the estimate of heritability, creating the appearance of missing heritability when compared with a GWAS.

Moreover, when conducting a GWAS, scientists typically assume that each genetic contribution they identify acts independently, such that the total genetic contribution is just the sum of those individual contributions. But scientists know that genes can also interact with each other to produce unpredictable results, a phenomenon called epistasis. The heritability estimates that scientists obtain from twin studies include any epistasis that is contributing to their traits. That fact could also inflate the heritability of twins as compared with GWASs. As of 2025, scientists do not have a definite solution to the problem of missing heritability.

A further limitation of GWASs is that they tend to be biased toward individuals of predominantly European ancestry living in the United States and Europe. A 2019 study that looked at all GWASs conducted from 2005 to 2018 found that around eighty percent of the individuals included in those studies were of European descent. That likely reflects the fact that most large DNA biobanks are located in North America and Europe, and therefore primarily contain data from individuals with European ancestry. That disparity is a problem because the results of a GWAS conducted in one population are less applicable to another population. In other words, what scientists learn about the particular SNPs associated with particular traits in populations of individuals with mostly European ancestry does not automatically translate to people of other ancestries. That is because the frequency of SNPs can differ across populations, and not all populations share the same environments. 

A related problem is that GWASs can sometimes produce spurious findings when cases and control are not drawn from the same populations. If some SNPs just happen to differ in frequency between two populations and those two populations are not represented equally in case and controls, then those SNPs will turn up as associated with the trait under consideration, despite being merely an artifact of the way the study was set up.  

In light of the above problems, some scientists have called for GWASs to be more inclusive of individuals from different ancestries, thereby making their results more equitable and accurate. The All of Us program of the US National Institutes of Health, which aims to collect DNA and phenotypic information from an ancestrally diverse collection of individuals, constitutes one attempt at being more inclusive. About forty-five percent of All of Us data comes from people who self-identify with a racial or ethnic group that has been historically underrepresented in medical research.

In addition to the aforementioned limitations, GWASs have proved controversial when they purport to explain differences in behavior among individuals. For example, a 2021 book entitled The Genetic Lottery: Why DNA Matters for Social Equality by Kathryn Paige Harden, who studies psychology and behavioral genetics at the University of Texas at Austin in Austin, Texas, generated strong reactions from scientists, some of it supportive, some of it critical. In her book, Harden argues that GWASs have shown that a portion of the variability in educational achievement, and therefore wealth, that a person attains is attributable to genetic differences. That portion is around ten to fifteen percent. She argues that it is not possible to address economic disparities, a goal she makes clear in the book that she supports, without considering the genetic advantages and disadvantages that different people inherit as part of the genetic lottery. In a review of Harden’s book titled “Lottery, Luck, or Legacy” and published in the journal Evolution, Graham Coop and Molly Przeworski, who study population genetics at the University of California-Davis, in Davis, California, and Columbia University in New York, New York, respectively, took issue with her conclusions. They state that they do not dispute the fact that educational attainment, like any trait that varies within a group, is partly heritable, if by heritable one means that traits are more similar in parents and offspring than in randomly selected individuals in a population. Nevertheless, they argue that genetics offers little value to the conversation about how to achieve a just society. Instead, they argue, genetics serves as a distraction from much more important political conversations related to economic disparities and education, for example, about reducing child poverty. In their review, the authors point to limitations in the ability of GWASs to tease out genetic factors from environmental factors in the case of a behavioral trait like educational attainment as one basis of their critique. Harden responded to her critics in the same journal, and Coop and Przeworski responded to her reply there as well.

Legacy and Impact as of 2025

According to the US National Human Genome Research Institute, or NHGRI, the medical impact of GWASs could be substantial. The NHGRI argues that GWASs are laying the groundwork for the era of personalized medicine, in which the current one-size-fits-all approach to medical care will give way to more customized strategies. The NHGRI envisions a day when health professionals will be able to use GWASs to tailor prevention programs to each person’s unique genetic makeup and, if a patient does become ill, use the information from GWASs to select the treatments most likely to be effective and least likely to cause adverse reactions in that particular patient. The NHGRI notes that GWASs have already helped identify previously unknown genetic contributions to age-related macular degeneration, type 2 diabetes, Parkinson’s disease, heart disorders, obesity, Crohn’s disease, and prostate cancer, as well as genetic variations that influence response to anti-depressant medications.

Nevertheless, it remains the case that, as of 2025, the medical benefits to accrue from GWASs have been modest. In a 2019 article in The Journal of Clinical Investigation, Michael J. Joyner, a physician and exercise specialist from the Mayo Clinic, in Rochester, Minnesota, and Nigel Paneth, a pediatrician who researches epidemiology from Michigan State University in East Lansing, Michigan, argue that GWASs have not fulfilled their promise to improve medicine. They make clear that, in their view, GWASs are unlikely to ever fulfill that promise, given their limitations. They urge the biomedical research community to reconsider what they call its obsession with the human genome and reassess its research priorities, including funding, to align with the health needs of the US population more closely. Not all scientists are as pessimistic about the potential of GWASs for improving health. Teri Manolio, a physician and scientist with the NHGRI, argues that GWAS findings are leading to clinical applications including risk prediction, disease classification, and drug development, even if the advances so far have been incremental.

As of 2025, the National Human Genome Research Institute and other public and private organizations continue to fund and pursue GWASs on a variety of complex human traits. 

Sources

1. Botstein, David, Raymond L. White, Mark Skolnick, and Ronald W. Davis. "Construction of a Genetic Linkage Map in Man Using Restriction Fragment Length Polymorphisms." American Journal of HumanGgenetics 32 (1980): 314–31. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1686077/ (Accessed July 14, 2024).
2. Brodie, Aharon, Jonathan Roy Azaria, and Yanay Ofran. “How Far from the SNP May the Causative Genes Be?” Nucleic Acids Research 44 (2016): 6046–54. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5291268/ (Accessed July 12, 2024).
3. Bumgarner, Roger. “DNA Microarrays: Types, Applications, and their Future.” Current Protocols in Molecular Biology 101 (2013). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4011503/ (Accessed July 12, 2024).
4. Chial, Heidi. “DNA Sequencing Technologies Key to the Human Genome Project.” Nature Education 1 (2008): 219. https://www.nature.com/scitable/topicpage/dna-sequencing-technologies-key-to-the-human-828/#:~:text=Thanks%20to%20the%20Human%20Genome,pairs%20in%20the%20human%20genome. (Accessed July 12, 2024).
5. Coop, Graham, and Molly Przeworski. "Lottery, Luck, or Legacy. A Review of “The Genetic Lottery: Why DNA matters for Social Equality." Evolution 76 (2022): 846–53. https://pmc.ncbi.nlm.nih.gov/articles/PMC9313868/ (Accessed July 14, 2024).
6. Coop, Graham, and Molly Przeworski. "Luck, Lottery, or Legacy? The Problem of Confounding. A Reply to Harden." Evolution 76 (2022): 2464–68. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627830/ (Accessed July 14, 2024).
7. de Souza, Natalie. "Get Out the Map." Nature Reviews Genetics 8 (2007): S10. https://www.nature.com/articles/miledna08 (Accessed July 11, 2024).
8. Harden, Kathryn Paige. "On Genetics and Justice: A Reply to Coop and Przeworski." Evolution 76 (2022): 2469–74. https://pmc.ncbi.nlm.nih.gov/articles/PMC10337657/ (Accessed July 14, 2024).
9. Harden, Kathryn Paige. The Genetic Lottery: Why DNA Matters for Social Equality. Princeton: Princeton University Press, 2021.
10. Hettiarachchi, Gaya, and Anton A. Komar. “GWAS to Identify SNPs Associated with Common Diseases and Individual Risk: Genome Wide Association Studies (GWAS) to Identify SNPs Associated with Common Diseases and Individual Risk.” In Single Nucleotide Polymorphisms, eds. Zuben E. Sauna and Chava Kimchi-Sarfaty, 51–76. https://link.springer.com/chapter/10.1007/978-3-031-05616-1_4 (Accessed July 11, 2024).
11. Illumina. “Disease Association Studies.” Illumina. https://www.illumina.com/areas-of-interest/complex-disease-genomics/gwas.html#:~:text=GWAS%20with%20the%20commonly%20used,Type%202%20diabetes (Accessed July 12, 2024).
12. International Human Genome Sequencing Consortium. “Initial Sequencing and Analysis of the Human Genome.” Nature 409 (2001): 860–921. https://www.nature.com/articles/35057062 (Accessed July 12, 2024).
13. Jansen, Philip R., Kyoko Watanabe, Sven Stringer, Nathan Skene, Julien Bryois, Anke R.Hammerschlag, Christiaan A. de Leeuw, Jeroen S. Benjamins, Ana B. Muñoz-Manchado, Mats Nagel, Jeanne E Savage, Henning Tiemeier, Tonya White; 23andMe Research Team; Joyce Y. Tung, David A. Hinds, Vladimir Vacic, Xin Wang, Patrick F. Sullivan, Sophie van der Sluis, Tinca J. C. Polderman, August B. Smit, Jens Hjerling-Leffler, Eus J. W. Van Someren, and Danielle Posthuma. "Genome-wide Analysis of Insomnia in 1,331,010 Individuals Identifies New Risk Loci and Functional Pathways." Nature Genetics 51 (2019): 394–403. https://www.nature.com/articles/s41588-018-0333-3 (Accessed July 12, 2024).
14. Joyner, Michael J., and Nigel Paneth. "Promises, Promises, and Precision Medicine." The Journal of Clinical Investigation 129 (2019): 946–8. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391102/ (Accessed July 14, 2024).
15. Ju, Dan, Daniel Hui, Dorothy A. Hammond, Ambroise Wonkam, and Sarah A. Tishkoff. "Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine." Annual Review of Biomedical Data Science 5 (2022): 321–39. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904154/ (Accessed July 14, 2024).
16. Klein, Robert J., Caroline Zeiss, Emily Y. Chew, Jen-Yue Tsai, Richard S. Sackler, Chad Haynes, Alice K. Henning, John Paul SanGiovanni, Shrikant M. Mane, Susan T. Mayne, Michael B. Bracken, Frederick L. Ferris, Jurg Ott, Colin Barnstable, and Josephine Hoh. "Complement Factor H Polymorphism in Age-Related Macular Degeneration." Science 308 (2005): 385–9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1512523/ (Accessed July 12, 2024).
17. Korf, Bruce R. “Introduction to Human Genetics.” In Clinical and Translational Science (Second Edition), eds. David Robertson and Gordon H. Williams, 281–311. Academic Press, 2017.
18. LaFramboise, Thomas. "Single Nucleotide Polymorphism Arrays: A Decade of Biological, Computational and Technological Advances." Nucleic Acids Research 37 (2009): 4181–93. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715261/ (Accessed July 12, 2024).
19. Lazareva Tatyana E., Yuri A. Barbitoff, Anton I. Changalidis, Alexander A. Tkachenko, Evgeniia M. Maksiutenko, Yulia A. Nasykhova, and Andrey S. Glotov1. “Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies”. Journal of Personalized Medicine 12(2022): 2040. doi: 10.3390/jpm12122040 (Accessed July 12, 2024).
20. Lu, Chang, Bastian Greshake Tzovaras, and Julian Gough. "A Survey of Direct-to-Consumer Genotype Data, and Quality Control Tool (GenomePrep) for Research." Computational and Structural Biotechnology Journal 19 (2021): 3747–54. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267563/ (Accessed July 12, 2024).
21. Maher, Brendan. “Personal Genomes: The Case of the Missing Heritability.” Nature 456 (2008): 18–21. https://www.nature.com/articles/456018a (Accessed July 14, 2024).
22. Manolio, Teri A. "Bringing Genome-wide Association Findings into Clinical Use." Nature Reviews Genetics 14 (2013): 549–58. https://www.nature.com/articles/nrg3523 (Accessed July 14, 2024).
23. Manolio, Teri A., Francis S. Collins, Nancy J. Cox, David B. Goldstein, Lucia A. Hindorff, David J. Hunter, Mark I. McCarthy, Erin M. Ramos, Lon R. Cardon, Aravinda Chakravarti, Judy H. Cho, Alan E. Guttmacher, Augustine Kong, Leonid Kruglyak, Elaine Mardis, Charles N. Rotimi, Montgomery Slatkin, David Valle, Alice S. Whittemore, Michael Boehnke, Andrew G. Clark, Evan E. Eichler, Greg Gibson, Jonathan L. Haines, Trudy F. C. Mackay, Steven A. McCarroll, and Peter M. Visscher. "Finding the Missing Heritability of Complex Diseases." Nature 461 (2009): 747–53.
24. Matthews, Lucas J., and Eric Turkheimer. "Three Legs of the Missing Heritability Problem." Studies in History and Philosophy of Science 93 (2022): 183–91. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172633/ (Accessed July 14, 2024).
25. Mills, Melinda C., and Charles Rahal. "A Scientometric Review of Genome-wide Association Studies." Communications Biology 2 (2019): 9. https://www.nature.com/articles/s42003-018-0261-x (Accessed July 14, 2024).
26. National Cancer Institute. “Case-control Study.” National Cancer Institute. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/case-control-study (Accessed July 12, 2024).
27. National Cancer Institute. “Linkage Analysis.” National Cancer Institute. https://www.cancer.gov/publications/dictionaries/genetics-dictionary/def/linkage-analysis (Accessed July 11, 2024).
28. National Human Genome Research Institute. “Genome-Wide Association Studies.” National Human Genome Research Institute. https://www.genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet (Accessed July 12, 2024).

29. National Human Genome Research Institute. “Genome-Wide Association Studies Fact Sheet.” 

    National Human Genome Research Institute. https://www.genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet (Accessed July 14, 2024).

30. National Human Genome Research Institute. “International HapMap Project.” National Human Genome Research Institute. https://www.genome.gov/10001688/international-hapmap-project (Accessed July 12, 2024).
31. National Human Genome Research Institute. “The Human Genome Project.” National Human Genome Research Institute. https://www.genome.gov/human-genome-project (Accessed July 12, 2024).
32. National Institutes of Health. “All of Us Research Program Makes Nearly 250,000 Whole Genome Sequences Available to Advance Precision Medicine.” National Institutes of Health, April 20, 2023. https://allofus.nih.gov/news-events/announcements/all-us-research-program-makes-nearly-250000-whole-genome-sequences-available-advance-precision-medicine (Accessed July 14, 2024).
33. Okbay, Aysu, Yeda Wu, Nancy Wang, Hariharan Jayashankar, Michael Bennett, Seyed Moeen Nehzati, Julia Sidorenko, Hyeokmoon Kweon, Grant Goldman, Tamara Gjorgjieva, Yunxuan Jiang, Barry Hicks, Chao Tian, David A. Hinds, Rafael Ahlskog, Patrik K. E. Magnusson, Sven Oskarsson, Caroline Hayward, Archie Campbell, David J. Porteous, Jeremy Freese, Pamela Herd, 23andMe Research Team, Social Science Genetic Association Consortium, Chelsea Watson, Jonathan Jala, Dalton Conley, Philipp D. Koellinger, Magnus Johannesson, David Laibson, Michelle N. Meyer, James J. Lee, Augustine Kong, Loic Yengo, David Cesarini, Patrick Turley, Peter M. Visscher, Jonathan P. Beauchamp, Daniel J. Benjamin & Alexander I. Young. “Polygenic Prediction of Educational Attainment Within and Between Families from Genome-wide Association Analyses in 3 Million Individuals.” Nature Genetics 54 (2022): 43749. https://doi.org/10.1038/s41588-022-01016-z (Accessed July 12, 2024).
34. Plomin, Robert, and Leonard C. Schalkwyk. “Microarrays.” Developmental Sciences 10 (2007): 19–23. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776927/ (Accessed July 12, 2024).
35. Rajagopalan, Ramya M., and Joan H. Fujimura. "Variations on a Chip: Technologies of Difference in Human Genetics Research." Journal of the History of Biology 51 (2018): 841–73.
36. Risch, Neil, and Kathleen Merikangas. "The Future of Genetic Studies of Complex Human Diseases." Science 273 (1996): 1516–7.
37. Sollis, Elliot, Abayomi Mosaku, Ala Abid, Annalisa Buniello, Maria Cerezo, Laurent Gil, Tudor Groza, Osman Güneş, Peggy Hall, James Hayhurst, Arwa Ibrahim, Yue Ji, Sajo John, Elizabeth Lewis, Jacqueline A. L. MacArthur, Aoife McMahon, David Osumi-Sutherland, Kalliope Panoutsopoulou, Zoë Pendlington, Santhi Ramachandran, Ray Stefancsik, Jonathan Stewart, Patricia Whetzel, Robert Wilson, Lucia Hindorff, Fiona Cunningham, Samuel A. Lambert, Michael Inouye, Helen Parkinson, and Laura W. Harris. “The NHGRI-EBI GWAS Catalog: Knowledgebase and Deposition Resource.” Nucleic Acids Research 51 (2022): D977–85. https://pubmed.ncbi.nlm.nih.gov/36350656/ (Accessed July 12, 2024).
38. Sugrue, Leo P., and Rahul S. Desikan. "What Are Polygenic Scores and Why Are They Important?" Jama 321 (2019): 1820–21. https://jamanetwork.com/journals/jama/fullarticle/2730627?casa_token=uOrSElXoqf0AAAAA:K4Pb4YqhtNw3PI0FFgjn409jMIh_PsScUnUdAqGvjjJZ2SJ8hQDL8LTxjCmFlV8CdIW5NsexCkw (Accessed July 12, 2024).
39. Tabery, James. Tyranny of the Gene: Personalized Medicine and Its Threat to Public Health. New York City: Penguin Random House, 2023.
40. The International HapMap Consortium. “The International HapMap Project.” Nature 426 (2003): 789–96. https://www.nature.com/articles/nature02168 (Accessed July 12, 2024).
41. Visscher, Peter M., Matthew A. Brown, Mark I. McCarthy, and Jian Yang. "Five Years of GWAS Discovery." The American Journal of Human Genetics 90 (2012): 7–24. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257326/ (Accessed July 14, 2024).
42. Visscher, Peter M., Naomi R. Wray, Qian Zhang, Pamela Sklar, Mark I. McCarthy, Matthew A. Brown, and Jian Yang. "10 years of GWAS Discovery: Biology, Function, and Translation." The American Journal of Human Genetics 101 (2017): 5–22. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501872/ (Accessed July 14, 2024).
43. The Wellcome Trust Case Control Consortium. “Genome-wide Association Study of 14,000 Cases of Seven Common Diseases and 3,000 Shared Controls.” Nature 447 (2007): 661–78. https://doi.org/10.1038/nature05911 (Accessed July 12, 2024).
44. Winerman, Lea. “A Second Look at Twin Studies.” Monitor on Psychology 35 (2004): 46. https://www.apa.org/monitor/apr04/second#:~:text=But%20some%20research%20suggests%20that%20parents%2C%20teachers%2C,identical%20twins%20more%20similarly%20than%20fraternal%20twins. (Accessed July 14, 2024).
45. Yengo, Loïc, Sailaja Vedantam, Eirini Marouli, Julia Sidorenko, Eric Bartell, Saori Sakaue, Marielisa Graff, Anders U. Eliasen, Yunxuan Jiang, Sridharan Raghavan, Jenkai Miao, Joshua D. Arias, Sarah E. Graham, Ronen E. Mukamel, Cassandra N. Spracklen, Xianyong Yin, Shyh-Huei Chen, Teresa Ferreira, Heather H. Highland, Yingjie Ji, Tugce Karaderi, Kuang Lin, Kreete Lüll, Deborah E. Malden, 23andMe Research Team, VA Million Veteran Program, DiscovEHR (DiscovEHR and MyCode Community Health Initiative), eMERGE (Electronic Medical Records and Genomics Network), Lifelines Cohort Study, The PRACTICAL Consortium, Understanding Society Scientific Group, et al, Joel N. Hirschhorn. “A Saturated Map of Common Genetic Variants Associated with Human Height.” Nature 610 (2022): 704–12. https://www.nature.com/articles/s41586-022-05275-y (Accessed July 12, 2024).
46. Young, Alexander I. "Solving the Missing Heritability Problem." PLoS Genetics 15 (2019): e1008222. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008222 (Accessed July 14, 2024).