Biology subjects

Conley, D.

Publications and source records attributed to Conley, D..

6 recordsLinked to original sources

bioRxiv2024

Within-Family GWAS does not Ameliorate the Decline in Prediction Accuracy across Populations

As polygenic prediction extends beyond the research domain to involve clinical applications, the urgency of solving the "portability problem" becomes amplified--that is, the fact that polygenic indices (PGI) constructed based on discovery analysis in one population (typically of exclusively continental European descent) predict poorly in other populations. In the present paper we test whether population differences in genetic nurture, assortative mating, or population stratification contribute to the fact that polygenic indices constructed based on GWAS results from European-descent samples predict more poorly in admixed populations with Native American and African ancestry. We do this by comparing the rates of decline in prediction accuracy of classical-GWAS-based PGIs versus within-family-based PGIs, each estimated in a population of European descent, as they are deployed in two samples of Latino Americans and African Americans. Within-family GWAS putatively eliminates the effects of parental genetic nurture, assortative mating, and population stratification; thus, we can determine whether without those confounding factors in the PGI construction, the relative prediction accuracy in the out-groups is ameliorated. Results show that relative prediction accuracy is not improved, suggesting that the differences across groups can be almost entirely explained by variation in genetic architecture (i.e. allele frequencies and short-range LD) rather than the aforementioned factors. Additional analysis of the impact of genetic architecture on the decline in prediction accuracy supports this conclusion. Future researchers should test within-family analysis at the prediction rather than the discovery stage.

genomics↗

bioRxiv2024

Does standard adjustment for genomic population structure capture direct genetic effects?

Contemporary genomic studies of complex traits, such as genome-wide association studies (GWASs) and polygenic index (PGI) analyses, often use the principal components of the genotype matrix (PCs) to adjust for population stratification. In this paper, we explore the extent to which we may be discounting direct genetic effects by adjusting for PCs. Using family-based models that control for parental genotype (obtained via Mendelian imputation), we test whether PCs have a direct genetic effect on nine complex phenotypes in the White British subsample of the UK Biobank. Further, we assess the extent to which estimates of polygenic effects meaningfully change when adjusting for PCs in within-family models. Across the nine traits, within-family effects of the top 40 PCs are highly similar to their population effects, suggesting that standard PC adjustments diminish, albeit to a small degree, detectable signals of direct genetic effects. Within family models also confirm that PCs have significant marginal effects on a few traits, most consistently, height and educational attainment. Nonetheless, the variance explained by the effects of PCs is modest, and adjusting for PCs does not appear to affect the magnitude and significance of PGI effects in within-family models.

genomics↗

bioRxiv2020

Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay

Outcomes of interest to demographers--fertility; health; education--are the product of both an individuals genetic makeup and his or her social environment. Yet Gene x Environment research (GxE) currently deploys a limited toolkit on the genetic side to study gene-environment interplay: polygenic scores (PGS, or what we call mPGS) that reflect the influence of genetics on levels of an outcome. The purpose of the present paper is to develop a genetic summary measure better suited for GxE research. We develop what we call variance polygenic scores (vPGS), or polygenic scores that reflect genetic contributions to plasticity in outcomes. The first part of the analysis uses the UK Biobank (N ~ 326,000 in the training set) and the Health and Retirement Study (HRS) to compare four approaches for constructing polygenic scores for plasticity. The results show that widely-used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for GxE. Then, using the polygenic scores that do capture distinctive genetic contributions to plasticity, we analyze heterogeneous effects of a UK education reform on health and educational attainment. The results show the properties of a new tool useful for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing polygenic scores to applied researchers.

genetics↗

bioRxiv2019

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

genetics↗

bioRxiv2017

A family-based method for leveraging random genetic variation to identify variance-controlling loci

The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation.\n\nSimulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.

genetics↗

bioRxiv2017

Analysis of genetic similarity among friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health (Add Health)

Humans tend to form social relationships with others who resemble them. Whether this sorting of like with like arises from historical patterns of migration, meso-level social structures in modern society, or individual-level selection of similar peers remains unsettled. Recent research has evaluated the possibility that unobserved genotypes may play an important role in the creation of homophilous relationships. We extend this work by using data from 9,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarities among pairs of friends. While there is some evidence that friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), further analysis suggests that meso-level forces, such as school assignment, are a principal source of genetic similarity between friends. We also observe apparent social-genetic effects in which polygenic scores of an individuals friends and schoolmates predict the individuals own educational attainment. In contrast, an individuals height is unassociated with the height genetics of peers.\n\nSignificanceOur study reported significant findings of a \"social genome\" that can be quantified and studied to understand human health and behavior. In a national sample of more than 9,000 American adolescents, we found evidence of social forces that act to make friends and schoolmates more genetically similar to one another as compared to random pairs of unrelated individuals. This subtle genetic similarity was observed across the entire genome and at sets of genomic locations linked with specific traits--educational attainment and body-mass index--a phenomenon we term \"social-genetic correlation.\" We also find evidence of a \"social-genetic effect\" such that the genetics of a persons friends and schoolmates influenced their own education, even after accounting for the persons own genetics.

genetics↗