Biology subjects

Cole, J. M.

Publications and source records attributed to Cole, J. M..

5 recordsLinked to original sources

bioRxiv2026

Representation in genetic studies affects inference about genetic architecture

Knowledge of a trait's "genetic architecture," namely the joint distribution of allele frequencies of causal variants and the direction and magnitude of their effects, is essential to understanding its evolution and underlying biology. Inferences about genetic architecture are based on data collected in different ways in cohorts recruited through heterogeneous mechanisms. As a result, cohorts differ in genotype, environment, and trait distributions. For example, the UK Biobank (UKB) was designed for broad population representation, whereas FinnGen drew extensively from clinical registries enriched for diagnosed health conditions. Here, we asked whether representation in genetic studies influences inferences about genetic architectures. Using GWAS data from the UKB, All of Us (AoU), and FinnGen, we compared several summaries of genetic architecture. We find systematic differences in some summaries, such as SNP heritability, which were often (in 6/13 traits) significantly lower in AoU than in the UKB (and never the reverse), even when matching samples such that they have similar genetic ancestry compositions. This result aligns with other recent evidence that biobanks enriched for diagnosed health conditions, also sometimes characterized by less-standardized phenotyping, have lower heritability than population-based biobanks. We highlight a second case, where a summary of genetic architecture varies considerably but not systematically across traits and biobanks. Such is the case for the mean direction of allelic effects ("sign bias"). For example, 72% of rare minor alleles affecting type 2 diabetes risk are inferred to be risk-increasing based on AoU data, while nearly all (>99%) are inferred to be risk-increasing based on UKB data. We hypothesize that the inferred sign bias is heavily influenced by the skewness of the trait distribution in the study and otherwise largely independent of other study or trait characteristics, including whether the trait is binary or quantitative. We provide strong support for this hypothesis through simulations and data from the three biobanks: the variation in inferred sign bias for rare minor alleles across traits and biobanks is explained remarkably well (82% and 97% of variance explained for trait-associated SNPs and randomly selected SNPs, respectively) solely by the trait's skewness in the biobank, with residual biobank-specificity explaining little. Our findings suggest that inferences about the map between genetic and trait variation can depend on study design and participation in genetic studies in surprising ways.

genomics↗

bioRxiv2024

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a novel method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover polygenic signals of genome-wide SDS for both viability and fecundity. An interesting life-history tradeoff emerges: alleles that increase viability more in one sex increase fecundity more in the other sex. Lastly, we find evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome. Significance statementSelection often acts differently on females and males, as evidenced by the striking sexual dimorphism found in many taxa. As a result, alleles can have different fitness effects in each sex. Consequences can include higher levels of genetic variation and higher disease burdens in populations. This study introduces a novel method to quantify this sex-differential selection (SDS) and reveals that it acts throughout the human genome. We discovered a life history tradeoff between survival and fecundity in females and males and that SDS on fecundity acts on alleles affecting arm fat-free mass.

evolutionary biology↗

bioRxiv2022

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Sex differences in complex traits are suspected to be in part due to widespread gene-by-sex interactions (GxSex), but empirical evidence has been elusive. Here, we infer the mixture of ways polygenic effects on physiological traits covary between males and females. We find that GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects ("amplification"), rather than in the identity of causal variants. Amplification patterns account for sex differences in trait variance. In some cases, testosterone may mediate amplification. Finally, we develop a population-genetic test linking GxSex to contemporary natural selection and find evidence for sexually antagonistic selection on variants affecting testosterone levels. Taken together, our results suggest that the amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/490973v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@19c267eorg.highwire.dtl.DTLVardef@a32233org.highwire.dtl.DTLVardef@1a6daeorg.highwire.dtl.DTLVardef@116cbff_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2020

Selection and gene flow define polygenic barriers between incipient butterfly species

Characterizing the genetic architecture of species boundaries remains a difficult task. Hybridizing species provide a powerful system to identify the factors that shape genomic variation and, ultimately, identify the regions of the genome that maintain species boundaries. Unfortunately, complex histories of isolation, admixture and selection can generate heterogenous genomic landscapes of divergence which make inferences about the regions that are responsible for species boundaries problematic. However, as the signal of admixture and selection on genomic loci varies with recombination rate, their relationship can be used to infer their relative importance during speciation. Here, we explore patterns of genomic divergence, admixture and recombination rate among hybridizing lineages across the Heliconius erato radiation. We focus on the incipient species, H. erato and H. himera, and distinguish the processes that drive genomic divergence across three contact zones where they frequently hybridize. Using demographic modeling and simulations, we infer that periods of isolation and selection have been major causes of genome-wide correlation patterns between recombination rate and divergence between these incipient species. Upon secondary contact, we found surprisingly highly asymmetrical introgression between the species pair, with a paucity of H. erato alleles introgressing into the H. himera genomes. We suggest that this signal may result from a current polygenic species boundary between the hybridizing lineages. These results contribute to a growing appreciation for the importance of polygenic architectures of species boundaries and pervasive genome-wide selection during the early stages of speciation with gene flow.

evolutionary biology↗

bioRxiv2018

Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits with imputed sequences of 27K Holstein bulls

Imputation has been routinely used to infer sequence variants in large genotyped populations based on reference populations of sequenced individuals. With increasing numbers of animals sequenced and the implementation of the 1000 Bull Genomes Project, fine-mapping of causal variants for complex traits is becoming possible in cattle. Using 404 ancestor bull sequences as reference, we imputed over 3 million selected sequence variants to 27,214 Holstein bulls with highly reliable phenotypes (breeding values) for 35 production, reproduction, and body conformation traits. We first performed whole-genome single-marker scans for each of the 35 traits using a mixed-model association test. The single-trait association statistics were then merged into multi-trait tests of 3 trait groups: production, reproduction, and body conformation. Both single- and multi-trait GWAS results were used to identify 282 candidate QTL regions for fine-mapping in the cattle genome. To facilitate fast and powerful fine-mapping analyses, we developed a Bayesian Fine-MAPping approach (BFMAP) to integrate fine-mapping with functional enrichment analysis. Our fine-mapping results identified 69 promising candidate genes for dairy traits, including ABCC9, VPS13B, MGST1, SCD, MKL1, and CSN1S1 for production traits; CHEK2, GC, and KALRN for reproduction traits; and TMTC2, ARRDC3, ZNF613, CCND2, and FGF6 for body conformation traits. Based on existing functional annotation data for cattle, we revealed biologically meaningful enrichment in our fine-mapped variants that can be readily tested in functional validation studies. In summary, these results demonstrated the utility of a fast Bayesian approach for fine-mapping and functional enrichment analysis, identified candidate causative genes and variants, and enhanced our understanding of the genetic basis of complex traits in dairy cattle.

genetics↗