Biology subjects

Yengo, L.

Publications and source records attributed to Yengo, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Genetic control and tissue specificity of transcriptomic rhythmicity

Circadian rhythms influence molecular traits in most tissues, yet genetic studies mostly treat molecular measurements as static. In particular, genetic effects on gene expression are typically investigated through expression quantitative trait locus (eQTL) studies, which ignore sampling time and therefore miss genetic effects on rhythm parameters such as amplitude and acrophase. In this study, we used a cosinor-based joint genotype-by-rhythm interaction framework applied to GTEx version 10 gene expression data, with donor inferred phase as a proxy for circadian time. We first identified rhythmically expressed genes in 49 of the 50 tissues profiled in GTEx. We then tested common variants within 50 kb of each rhythmic gene's transcription start site for genetic regulation of transcriptomic rhythmicity. We identified 167 circadian expression QTLs (cd-eQTLs), comprising 54 lead signals for 51 genes across 28 tissues. Most cd-eQTLs altered oscillation amplitude, while others affected acrophase or both. We then investigated the tissue specificity of cd-eQTLs and found that those acting on core circadian genes had consistent effects across tissues, whereas those acting on clock-regulated genes were largely tissue-specific. A phenome-wide association study in the Pan-UK Biobank linked 34 cd-eQTLs to 100 phenotypes, including self-reported chronotype, adiposity, blood cell counts and blood biochemistry. These results show that genetic variation shapes circadian gene expression in a tissue-dependent manner, and that accounting for time of day reveals regulatory effects that static eQTL analyses cannot detect.

genomics↗

bioRxiv2026

Using summary data to detect and quantify ascertainment in biobanks

Non-random participation in genetic studies can bias associations between genetic variants and outcomes. Existing methods to detect ascertainment bias often require individual-level data, thus limiting their broad applicability. Here, we introduce a summary-statistics-based method to detect and quantify ascertainment bias in large-scale genetic studies. Our method estimates a parameter,{theta} , which captures deviations in the mean polygenic score (PGS) of an ascertained sample relative to its expectation across non-ascertained or differentially ascertained references. We show through extensive simulations that our method is robust to population stratification and reference misspecification unlike naive mean PGS comparison. When applied to 21 traits across 11 large-scale biobanks, our method recapitulates known patterns of ascertainment and detects new evidence of ascertainment on genetic susceptibility to depression, height and blood pressure in many biobanks. Overall, our framework enables systematic assessment of ascertainment directly from summary statistics and provides a scalable tool for evaluating representativeness in large scale genetic studies.

genetics↗

bioRxiv2026

Quantifying annotation-stratified pleiotropy and co-polygenicity between complex traits

Understanding shared genetic architecture is essential to interpreting disease comorbidities and trait correlations. We introduce SBayesAPP, a Bayesian model that integrates GWAS summary statistics with functional annotations to jointly estimate annotation-stratified SNP effect-size correlation and pleiotropic variant proportion (co-polygenicity) between traits, dissecting genetic correlation and coheritability enrichment across annotations. Simulations and real data analyses show improved accuracy and interpretability over existing methods. In type 2 diabetes analyses with 15 traits, SBayesAPP reveals clear tissue- and cell-type-specific enrichment and distinguishes mechanisms driven by few large-effect variants versus many modest-effect variants. The analysis of smoking and lung cancer prioritizes lung and immune cells, and identifies cell-type-specific genetic correlations driven by either pleiotropic or lung-cancer-specific variants, consistent with a causal relationship model. For schizophrenia and educational attainment, despite near-zero genome-wide genetic correlation, cell-type-specific correlations range from -0.20 to 0.21, with strong (co)heritability enrichment and high co-polygenicity found in dopaminergic neurons and oligodendrocytes. These results highlight the ability of SBayesAPP to resolve annotation-specific genetic sharing and uncover biological mechanisms across complex traits.

genomics↗

bioRxiv2026

Multi-ancestry conditional and joint analysis (Manc-COJO) applied to GWAS summary statistics

Conditional and joint (COJO) analysis of genome-wide association study (GWAS) summary statistics to identify single nucleotide polymorphisms (SNPs) independently associated with a trait is standard in post-GWAS pipelines. GWAS meta-analyses are increasingly conducted across multiple ancestry groups but how to perform COJO in a multi-ancestry context is not known. Here we introduce Manc-COJO, a method for multi-ancestry COJO analysis. Simulations and real-data analyses show that Manc-COJO improves the detection of independent association signals and reduces false positives compared to COJO and ad hoc adaptations for multi-ancestry use. We also introduce Manc-COJO:MDISA, a follow-up within ancestry algorithm to identify ancestry-specific associations after fitting Manc-COJO identified SNPs. The C++ implementation of Manc-COJO substantially improves on computational efficiency (for single ancestry >120 times faster than GCTA-COJO software) and supports linkage disequilibrium references derived either from individual-level genotype data or pre-computed matrices, facilitating analysis when data sharing is limited.

bioinformatics↗

bioRxiv2026

Converging evidence of positive selection at height-associated loci in Europe

The contribution of natural selection to height differences across Europe remains elusive. Prior evidence of selection on height based on genome-wide association study (GWAS) data has been challenged because of residual confounding in earlier GWAS results. Here, we reassess this evidence using complementary approaches integrating multi-ancestry and within-family GWAS data with allele frequency estimates from 13 European ancestry populations. We detect signatures of positive selection at height-associated loci within and across populations, which remain significant after accounting for ancient admixture events and is largely driven by allele frequency differences between the Netherlands and other European ancestry populations. Altogether, our results provide new and converging evidence of polygenic adaptation on height in Europe.

genetics↗

bioRxiv2023

Genetics of skeletal proportions in two different populations

Human height can be divided into sitting height and leg length, reflecting growth of different parts of the skeleton whose relative proportions are captured by the ratio of sitting to total height (as sitting height ratio, SHR). Height is a highly heritable trait, and its genetic basis has been well-studied. However, the genetic determinants of skeletal proportion are much less well-characterized. Expanding substantially on past work, we performed a genome-wide association study (GWAS) of SHR in [~]450,000 individuals with European ancestry and [~]100,000 individuals with East Asian ancestry from the UK and China Kadoorie Biobanks. We identified 565 loci independently associated with SHR, including all genomic regions implicated in prior GWAS in these ancestries. While SHR loci largely overlap height-associated loci (P < 0.001), the fine-mapped SHR signals were often distinct from height. We additionally used fine-mapped signals to identify 36 credible sets with heterogeneous effects across ancestries. Lastly, we used SHR, sitting height, and leg length to identify genetic variation acting on specific body regions rather than on overall human height.

genetics↗

bioRxiv2023

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Author SummaryHuman genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

genetics↗

bioRxiv2022

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a new method, SBayesRC, that integrates GWAS summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyse 28 traits in the UK Biobank using [~]7 million common SNPs and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and by up to 33% in trans-ancestry prediction, compared to the baseline method SBayesR which does not use annotations, and outperforms state-of-the-art methods LDpred-funct, PolyPred-S and PRS-CSx by 12-15%. Investigation of factors affecting prediction accuracy identified a significant interaction between SNP density and annotation information, encouraging future use of whole-genome sequence variants for prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from non-synonymous SNPs.

genetics↗

bioRxiv2022

A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

genetics↗

bioRxiv2021

Assortative Mating Biases Marker-based Heritability Estimators

Many complex traits are subject to assortative mating (AM), with recent molecular genetic findings confirming longstanding theoretical predictions that AM alters genetic architecture by inducing long range dependence across causal variants. However, all marker-based heritability estimators assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments estimators and likelihood-based estimators produce biased estimates in the presence of AM and that common approaches to account for population structure fail to mitigate this bias. Then, examining height and educational attainment in the UK Biobank, we demonstrate that these biases affect real world traits. Finally, we derive corrected heritability estimators for traits under equilibrium AM.

genetics↗

bioRxiv2020

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive [Formula], dominance [Formula] and additive-by-additive [Formula] genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits [Formula]. In contrast, the average estimate of [Formula] across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance [Formula] across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.

genetics↗

bioRxiv2020

Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fishers exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p=0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p=0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p=0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle. Author SummaryProvided that there is a similarity in the biological control of complex traits in different species, the results of genome-wide association study for a given trait in a model species can be utilized as prior information to identify genes and gene-associated variants that affect the same trait in a non-model species. Here we report that the number of genes found to be affecting height in both humans and cattle is more than can be expected by chance, suggesting a similarity in the biology of height in both species. In addition, genes that are associated with height in humans were found to be highly enriched with variants that are strongly associated with height in cattle. Using height as a model trait, we demonstrated that across-species prior information cannot compete with a within-species prior information, with respect to the identification of genes and the gene-associated variants that affect a complex trait. Across-species prior information can be useful, however, when within-species prior information is either unavailable or poor.

genetics↗

bioRxiv2020

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Polygenic scores (PGS) have been widely used to predict complex traits and risk of diseases using variants identified from genome-wide association studies (GWASs). To date, most GWASs have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European populations. Here, we develop a new theory to predict the relative accuracy (RA, relative to the accuracy in populations of the same ancestry as the discovery population) of PGS across ancestries. We used simulations and real data from the UK Biobank to evaluate our results. We found across various simulation scenarios that the RA of PGS based on trait-associated SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of SNP effect sizes and heritability. Altogether, we find that LD and MAF differences between ancestries explain alone up to ~70% of the loss of RA using European-based PGS in African ancestry for traits like body mass index and height. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWASs are mostly shared across continents.

genetics↗

bioRxiv2019

Quantifying genetic heterogeneity between continental populations for human height and body mass index

Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide significant SNPs (rg(GWS)) for height and body mass index (BMI) in samples of European (EUR; n = 49,839) and African (AFR; n = 17,426) ancestry. The [Formula] between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding [Formula] was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that [Formula] differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

genetics↗

bioRxiv2019

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

Understanding how natural selection has shaped the genetic architecture of complex traits and diseases is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level data to estimate multiple features of genetic architecture, including signatures of natural selection. Here, we present an enhanced method (SBayesS) that only requires GWAS summary statistics and incorporates functional genomic annotations. We analysed GWAS data with large sample sizes for 155 complex traits and detected pervasive signatures of negative selection with diverse estimates of SNP-based heritability and polygenicity. Projecting these estimates onto a map of genetic architecture obtained from evolutionary simulations revealed relatively strong natural selection on genetic variants associated with cardiorespiratory and cognitive traits and relatively small number of mutational targets for diseases. Averaging across traits, the joint distribution of SNP effect size and MAF varied across functional genomic regions (likely to be a consequence of natural selection), with enrichment in both the number of associated variants and the magnitude of effect sizes in regions such as transcriptional start sites, coding regions and 5- and 3-UTRs.

genetics↗

bioRxiv2019

Widespread associations between grey matter structure and the human phenome

The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioural, cognitive, psychiatric and lifestyle data. We found a significant association of grey-matter structure with 58 out of 167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: N=9,888). Twenty-three of the 58 associations replicated (UKB replication sample: N=4,561; HCP, N=1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (e.g. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be significantly predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options.\n\nHighlightsO_LIOur linear mixed model approach unifies association and prediction analyses for highly dimensional vertex-wise MRI data\nC_LIO_LIGrey-matter structure is associated with measures of substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains\nC_LIO_LIBody size (height, weight, BMI, waist and hip circumference) is an important source of covariation between the phenome and grey-matter structure\nC_LIO_LIGrey-matter scores quantify grey-matter based risk for the associated traits and allow to study phenotypes not collected\nC_LIO_LIThe most general cortical processing (\"fsaverage\" mesh with no smoothing) maximises the brain-morphometricity for all UKB phenotypes\nC_LI

neuroscience↗

bioRxiv2019

No evidence for social genetic effects or genetic similarity among friends beyond that due to population stratification: a reappraisal of Domingue et al (2018)

Using data from 5,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health, Domingue et al. (2018) claimed to show that friends are genetically more similar to one another than randomly selected peers, beyond the confounding effects of population stratification by ancestry. The authors also claimed to show social-genetic effects, whereby individuals educational attainment (EA) is influenced by their friends genes. Neither claim is justified by the data. Mathematically we show that 1) although similarity at causal variants is expected under assortment, the genome-wide relationship between friends (and similarly between mates) is extremely small (an effect that could be explained by subtle population stratification) and 2) significant association between individuals EA and their friends polygenic score for EA is expected under homophily with no socio-genetic effects.

genetics↗

bioRxiv2019

Recovery of trait heritability from whole genome sequence data

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated individuals of European ancestry. We assigned 33.7 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60 - 0.71 for height and ~0.25 - 0.35 for BMI, depending on quality control and analysis strategies. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 < MAF < 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively. Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.

genetics↗