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Identifying the Genomic Basis of Biological Variation: Polygenic Phenotypes

By Diane P. Genereux

Polygenic Phenotypes

For many human traits, GWAS has identified multiple “associated” genomic regions—places where variants are at a higher frequency among individuals with a given trait but are not unique to individuals with a particular trait of interest. These two ideas are united in the concept of polygenic quantitative inheritance: the idea that the “risk” of having a given trait is governed not by a single change of large effect, but instead by multiple different genetic changes of individually small effect, such that an individual’s phenotype is roughly equivalent to the number of such changes that are present in the genome, each weighted by the magnitude of its effect. In their joint paper “Polygenic Inheritance, GWAS, Polygenic Risk Scores, and the Search for Functional Variants,” Daniel Crouch and Walter Bodmer review the process whereby genomics gradually shifted from the view that polygenicity is the exception to an appreciation of it as a common underpinning of biological variation. Some of the best-studied polygenic traits are discussed below:

Height is a highly polygenic human trait. It varies greatly across individuals, and its congruence among closely related individuals, including members of identical twin pairs raised in disparate environments, points to a genetic component. GWAS results have confirmed this anticipated polygenicity, yet this finding underscores the importance of large samples for detecting large numbers of variants of small impact. The first height-associated gene, HMGA2, was identified in 2007 through a GWAS study of some 5,000 individuals, then confirmed through targeted assessment of genotypes in about 19,000 more individuals by Michael Weedon et al. in their Nature Genetics publication “A Common Variant of HMGA2 Is Associated with Adult and Childhood Height in the General Population.” However, it was not until a 2022 study of a cohort of 5.4 million individuals—three orders of magnitude greater than the initial cohort—that researchers were able to discover 12,000 height-associated genetic variants, as explained by Loïc Yengo and colleagues through their recent article in Nature, “A Saturated Map of Common Genetic Variants Associated with Human Height.” The authors estimate these variants to account for 40 percent of height variation among humans.

Autism spectrum disorder (ASD) is a complex phenotype with a broad range of manifestations relevant to communication and social interactions. During the 1960s, psychologists tended to attribute the emergence of this phenotype primarily to environmental factors. Elon Green provides a history of research on autism, including its misportrayal and misconception, in “Rewriting Autism History,” published in the Atlantic. For example, it was not until 1977 that a role for genetic variants became clear through a study authored by Susan Folstein and Michael Rutter, “Infantile Autism: A Genetic Study of 21 Twin Pairs,” revealing that genetically identical twins, but not fraternal twins, were much more likely than anticipated by chance to be concordant for autism-relevant phenotypes. GWAS has subsequently brought further clarity to these initial findings of a strong genetic component. ASD is now widely understood to have a strong polygenic component, with the impact of certain mutations variable between male and female individuals, as explained by Danny Antaki and colleagues in “A Phenotypic Spectrum of Autism Is Attributable to the Combined Effects of Rare Variants, Polygenic Risk, and Sex.” Moreover, many of the mutations associated with ASD are more common than would be anticipated to occur by chance in individuals with other cognitive phenotypes, though the mechanistic connections have yet to be thoroughly elucidated. This situation is reported by Beate St Pourcain and colleagues in “ASD and Schizophrenia Show Distinct Developmental Profiles in Common Genetic Overlap with Population-Based Social Communication Difficulties.”

Cancer is not one but instead a set of diseases broadly characterized by aberrant overgrowth of cells bearing specific mutations. It is extremely common in human populations. For individuals living in the United States, for example, the lifetime probability of developing some form of cancer is roughly 40 percent. Detailed cancer incidence statistics for individuals living in the United States are available from the National Cancer Institute at its related website, Cancer Statistics. Some of the most successful therapies developed to date target genetic changes that unleash this unregulated cell division, underscoring the urgency to discover inherited genetic changes that increase the risk of specific cancers. One of the first such genetic risk factors was discovered during an era when, despite growing knowledge that some cancers tended to be more common among family members, viral infections—and not mutations in human DNA—were generally still thought to be the most common cause, as discussed by D. W. Smithers in “Family Histories of 459 Patients with Cancer of the Breast.” Further, mathematical modeling by Mary-Claire King using survey data from individuals with and without breast cancer predicted the existence of inherited genomic risk factors that strongly increase the lifetime risk of breast cancer; this prediction was later confirmed through molecular techniques. In a 2019 interview conducted by Ushma Neill, “A Conversation with Mary-Claire King,” King reflects on the academic path that led to her discovery.

More recently, GWAS using large numbers of individuals has proven powerful for identifying a broader set of genetic variants associated with elevated cancer risk, as discussed by Haoyu Zhang and colleagues in “Genome-Wide Association Study Identifies 32 Novel Breast Cancer Susceptibility Loci from Overall and Subtype-Specific Analyses.” Cancer can also arise through genetic changes that are not inherited but instead occur in a specific tissue during the lifetime of an individual, so identifying genetic differences between an individual’s tumor and their normal tissue can help identify cancer-related mutations. This approach is discussed, for example, by Diana Mandelker and Ozge Ceyhan-Birsoy in “Evolving Significance of Tumor-Normal Sequencing in Cancer Care.”