Biology subjects

Uffelmann, E.

Publications and source records attributed to Uffelmann, E..

3 recordsLinked to original sources

bioRxiv2023

Local Genetic Sex Differences in Quantitative Traits

Many traits show small global sex differences in genetic correlations and heritability. However, how these differences are distributed across the genome remains unknown. Here, we use LAVA to test for local genetic sex differences in genetic correlations, heritabilities, and the magnitude of genetic effects across 157 quantitative traits in the UK Biobank. Nearly every trait shows evidence for sex-dimorphic effects in at least one locus. We find that such loci can flag biological differences between the sexes. Moreover, we test for differences in the magnitude of genetic effects on the raw and the standardized scale. We show these have complementary interpretations, where only the latter scale is informative for heritability. Our results show how average metrics of genetic correlation and heritability across the whole genome can mask important variability between loci and that the scale of genetic effects needs to be considered carefully when comparing their magnitudes.

genetics↗

bioRxiv2022

Genome-wide association studies of polygenic risk score-derived phenotypes may lead to inflated false positive rates

In a recent study, a polygenic risk score (PRS) for Alzheimers disease was used to construct a new phenotype for a subsequent genome-wide association study (GWAS). Here we show that the applied method, in which the same genetic variants are used to construct the PRS-derived phenotype as well as to assess their effect in a GWAS of the same phenotype, leads to inflated false positive rates. We illustrate this bias by simulation. We first simulate an initial discovery cohort, and run a GWAS of a disorder like Alzheimers disease. We then simulate a target cohort, in which we construct a PRS based on the initial GWAS results. Following the published study, we select the bottom and top 5% of individuals in the PRS distribution and define them as controls and cases. Lastly, we run a GWAS on the new PRS-derived phenotype using all genetic variants. We show that at a significance threshold of 5 x 10-8, false positive rates are inflated up to 0.004 (an 80,000-fold increase compared to 5 x 10-8). We also show that such inflation can be prevented by excluding all variants that were used to construct the PRS (as well as all variants in linkage disequilibrium), when a GWAS on a PRS-derived phenotype is conducted.

bioinformatics↗

bioRxiv2021

Uncovering the Genetic Architecture of Broad Antisocial Behavior through a Genome-Wide Association Study Meta-analysis.

Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8,058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, P = 6.32 x 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). The SNP-based heritability of ASB was 8.4% (s.e.= 1.2%). Polygenic-risk-score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg{square}={square}0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg{square}={square}0.54), cognitive ability (intelligence rg= -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg={square}- 0.58, fathers age at death rg= -0.54). Our findings provide a starting point towards identifying critical biosocial risk mechanisms for the development of ASB.

genetics↗