Biology subjects

Schoeler, T.

Publications and source records attributed to Schoeler, T..

7 recordsLinked to original sources

bioRxiv2026

Triangulating evidence to detect signatures of stabilizing selection acting on protein levels in humans

Stabilizing selection is thought to maintain quantitative traits near evolutionary optima, yet the extent to which it constrains human molecular phenotypes remains largely unknown. Here, we investigate signatures of stabilizing selection on the circulating levels of 2'923 plasma proteins in up to 50'000 UK Biobank participants by integrating complementary evidence across evolutionary timescales. We combine genetic signatures of selection accumulated over generations with associations between protein abundance and contemporary fitness-related outcomes. Genetic evidence was assessed using the frequency-dependent architecture of common variants with LDpred2 and a newly developed likelihood-based framework leveraging rare loss-of-function variant burdens. Contemporary evidence was obtained through quadratic associations between protein abundance and three fitness proxies: reproductive success, somatic health, and parental longevity. Across all approaches, negative selection coefficients predominated. Among significant estimates, 100% of common-variant and 92.4% of rare-variant estimates were consistent with stabilizing selection, compared with 99.8% for somatic health, 97.7% for parental longevity, and 89.3% for reproductive success. Despite capturing distinct aspects of selection, the approaches showed partial convergence. Rare-variant-based estimates correlated with health-score-derived coefficients (rho = 0.36, p = 5.2 x 10^-6) and independent measures of evolutionary constraint, including protein heritability, loss-of-function intolerance, and cross-species transcript evolutionary rates. Together, our findings suggest that stabilizing selection is widespread across the human plasma proteome and highlight the value of integrating genetic architecture with fitness-related phenotypes to understand evolutionary constraints on molecular variation.

evolutionary biology↗

bioRxiv2025

Parental haplotypes reconstruction in up to 440,209 individuals reveals recent assortative mating dynamics

Assortative mating (AM), the tendency for individuals to choose partners with similar traits, plays an important role in social stratification and has a wide-spread impact on the genetic architecture of complex human traits. A genetic footprint of this behaviour, genetic assortative mating (GAM), has been documented for many traits. However, most existing approaches to estimate GAM either rely on genotyped couples - rare in large biobanks - or on estimates based on gametic phase disequilibrium (GPD), which reflect cumulative effects over multiple generations and cannot detect short-term events. We introduce a novel, scalable approach that infers genetic data for the parental generation to boost statistical power and improve interpretability when estimating GAM in biobank cohorts. Our method reconstructs the two parental haplotypes of biobank individuals by leveraging state-of-the-art inter-chromosomal phasing based on close relative information. By correlating polygenic scores computed separately on maternally and paternally inherited haplotypes, we infer the extent of GAM. Applied to 245,884 individuals from the UK Biobank and 194,325 individuals from the Estonian Biobank, our haplotype-based estimates showed strong concordance with GAM estimates from genotyped mate pairs across 69 traits and improved performance compared to existing GPD-based methods. We replicated previously identified patterns of GAM for many traits (educational attainment, height, BMI, and alcohol consumption), and revealed new ones (overall health and sedentary lifestyle). Temporal and geographic stratification revealed accelerated assortment in recent generations -- particularly for education and height -- and modest differences between urban and rural contexts. Our method enables scalable, interpretable, and generation-specific estimation of GAM in large biobank cohorts, providing new insights into the dynamic nature of mate choice and its impact on the genetic makeup of populations.

genetics↗

bioRxiv2022

Correction for participation bias in the UK Biobank reveals non-negligible impact on genetic associations and downstream analyses

While large-scale volunteer-based studies such as the UK Biobank (UKBB) have become the cornerstone of genetic epidemiology, the study participants are rarely representative of their target population. Here, we aim to evaluate the impact of non-random participation in the UKBB, and to pin down areas of research that are particularly susceptible to biases when using non-representative samples for genome-wide discovery. By comparing 14 harmonized characteristics of the UKBB participants to that of a representative sample, we derived a model for participation probability. We then conducted inverse probability weighted genome-wide association analyses (wGWA) on 19 UKBB traits. Comparing the output obtained from wGWA (Neffective=94,643 - 102,215) to standard GWA analyses (N=263,464 - 283,749), we assessed the impact of participation bias on three estimated quantities, namely 1) genotype-phenotype associations, 2) heritability and genetic correlation estimates and 3) exposure-outcome causal effect estimates obtained from Mendelian Randomization. Participation bias can lead to both overestimation (e.g., cancer, education) and underestimation (e.g., coffee intake, depression/anxiety) of SNP effects. Novel SNPs were identified in wGWA for 12 of the included traits, highlighting SNPs missed as a result of participation bias. While the impact of participation bias on heritability estimates was small (average change in h2: 1.5%, maximum: 5%), substantial distortions were present for genetic correlations (average absolute change in rg: 0.07, maximum: 0.31) and Mendelian Randomization estimates (average absolute change in standardized estimates: 0.04, maximum: 0.15), most markedly for socio-behavioural traits including education, smoking and BMI. Overall, the bias mainly affected the magnitude of effects, rather than direction. In contrast, genome-wide findings for more molecular/physical traits (e.g., LDL, SBP) exhibited less bias as a result of selective participation. Our results highlight that participation bias can distort genomic findings obtained in non-representative samples, and we propose a viable solution to reduce such bias. Moving forward, more efforts ensuring either sample representativeness or correcting for participation bias are paramount, especially when investigating the genetic underpinnings of behaviour, lifestyles and social outcomes.

genetics↗

bioRxiv2021

Genetic nurture effects on education: a systematic review and meta-analysis

Child educational development is associated with major psychological, social, economic and health milestones throughout the life course. Understanding the early origins of educational inequalities and their reproduction across generations is therefore crucial. Recent genomic studies provide novel insights in this regard, uncovering "genetic nurture" effects, whereby parental genotypes influence offsprings educational development via environmental pathways rather than genetic transmission. These findings have yet to be systematically appraised. We conducted the first systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes and investigate key moderators. Twelve studies comprising 38,654 distinct parent(s)-offspring pairs or trios from eight cohorts were included, from which we derived 22 estimates of genetic nurture effects. Multilevel random effects models showed that the effect of genetic nurture on offsprings educational outcomes ({beta}genetic nurture = 0.08, 95% CI [0.07, 0.09]) was about half the size of direct genetic effects ({beta}direct genetic = 0.17, 95% CI [0.13, 0.20]). Maternal and paternal genetic nurture effects were similar in magnitude, suggesting comparable roles of mothers and fathers in determining their childrens educational outcomes. Genetic nurture effects were largely explained by parental educational level and family socioeconomic status, suggesting that genetically influenced environments play an important role in shaping child educational outcomes. Even after accounting for genetic transmission, we provide evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes. Further exploring these downstream environmental pathways may inform educational policies aiming to break the intergenerational cycle of educational underachievement and foster social mobility. Public Significance StatementThis meta-analysis demonstrates that parents genetics influence their childrens educational outcomes through the rearing environments that parents provide. This "genetic nurture" effect is largely explained by family socioeconomic status and parental education level, is similar for mothers and fathers (suggesting that both parents equally shape their childrens educational outcomes) and is about half the size of direct genetic effects on childrens educational outcomes. Interventions targeting such environmental pathways could help to break the intergenerational cycle of educational underachievement and foster social mobility.

genetics↗

bioRxiv2020

Combining multivariate genomic approaches to elucidate the comorbidity between ASD and ADHD

BackgroundSeveral lines of evidence point toward the presence of shared genetic factors underlying Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). However, Genome-Wide Association Studies (GWAS) have yet to identify risk variants (i.e. Single-Nucleotide Polymorphisms, SNPs) shared by ADHD and ASD. MethodsTwo complementary multivariate analyses - genomic structural equation modelling (SEM) and colocalization analysis - were exploited to identify the shared SNPs for ASD and ADHD, using summary data from two independent GWAS of ASD (N=46,350) and ADHD individuals (N=55,374). ResultsGenomic SEM identified 7 novel SNPs shared between ASD and ADHD (pgenome-wide<5e-8), including three SNPs that were not identified in any of the original univariate GWAS of ASD and ADHD (rs227378, rs2391769 and rs325506). We also mapped 4 novel genes (MANBA, DPYD, INSM1, and PAX1) to SNPs shared by ASD and ADHD, as well as 4 genes that had already been mapped to SNPs identified in either ASD or ADHD GWAS (SORCS3, XRN2, PTBP2 and NKX2-4). All the shared genes between ADHD and ASD were more prominently expressed in the brain than the genes mapped to SNPs specific to ASD or ADHD. Colocalization analyses revealed that 44% percent of the SNPs associated with ASD (p<1e-6) colocalized with ADHD SNPs and 26% of the SNPs associated with ADHD (p<1e-6) colocalized with ASD SNPs. ConclusionsUsing multivariate genomic analyses, the present study reveals the shared genetic pathways that underlie ASD and ADHD. Further investigation of these pathways may help identify new targets for treatment of these disorders.

genetics↗

bioRxiv2019

Identifying risk factors involved in the common versus specific liabilities to substance abuse: A genetically informed approach

The co-occurrence of abuse of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability through genetically influenced individual vulnerabilities and traits. To disentangle the aetiology of common versus specific liabilities to substance abuse, polygenic scores can be used as genetic proxies indexing such risk and protective individual vulnerabilities or traits. In this study, we used genomic data from a UK birth cohort study (ALSPAC, N=4218) to generate 18 polygenic scores indexing mental health vulnerabilities, personality traits, cognition, physical traits, and substance abuse. Common and substance-specific factors were identified based on four classes of substance abuse (alcohol, cigarettes, cannabis, other illicit substances) assessed over time (age 17, 20, and 22). In multivariable regressions, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our findings implicated several genetically influenced traits and vulnerabilities in the common liability to substance abuse, most notably risk taking (bstandardized=0.14; 95%CI: 0.10,0.17), followed by extraversion (bstandardized =-0.10; 95%CI: -0.13,-0.06), and schizophrenia risk (bstandardized=0.06; 95%CI: 0.02;0.09). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarettes (bstandardized-EA= -0.15; 95%CI: -0.19,-0.12; bstandardized-BMI=0.05; 95%CI: 0.02,0.09), alcohol (bstandardized-EA=0.07; 95%CI: 0.03,0.11; bstandardized-BMI= -0.06; 95%CI: -0.10, -0.02), and other illicit substances (bstandardized-EA=0.12; 95%CI: 0.07,0.17; bstandardized-BMI= -0.08; 95%CI:-0.13,-0.04). This is the first study based on genomic data that clarifies the aetiological architecture underlying the common versus substance-specific liabilities, providing novel insights for the prevention and treatment of substance abuse.

epidemiology↗

bioRxiv2019

Estimating the sensitivity of associations between risk factors and outcomes to shared genetic effects

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes - child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely. Author summaryAn objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes - educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder-is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.

genetics↗