Biology subjects

Tenesa, A.

Publications and source records attributed to Tenesa, A..

13 recordsLinked to original sources

bioRxiv2023

Schrödinger's Cat in Simulations in Genome-wide Association Studies

Simulations are essential components for testing methods or assumptions in genome-wide association studies (GWAS). We show through theory and examples that markers can be both causal and non-causal simultaneously, resulting in a paradox, had simulation not being run properly. We developed a software (ParaPheSim - a paradoxical phenotype simulator) that allows users to create different traits whose genetic correlation is exactly 1 and yet they have no genetic overlap.

genomics↗

bioRxiv2022

Genetic score omics regression and multi-trait meta-analysis detect widespread cis-regulatory effects shaping bovine complex traits

To complete the genome-to-phenome map, transcriptome-wide association studies (TWAS) are performed to correlate genetically predicted gene expression with observed phenotypic measurements. However, the relatively small training population assayed with gene expression could limit the accuracy of TWAS. We propose Genetic Score Omics Regression (GSOR) correlating observed gene expression with genetically predicted phenotype, i.e., genetic score. The score, calculated using variants near genes with assayed expression, provides a powerful association test between cis-effects on gene expression and the trait. In simulated and real data, GSOR outperforms TWAS in detecting causal/informative genes. Applying GSOR to transcriptomes of 16 tissue (N[~]5000) and 37 traits in [~]120,000 cattle, multi-trait meta-analyses of omics-associations (MTAO) found that, on average, each significant gene expression and splicing mediates cis-genetic effects on 8[~]10 traits. Supported by Mendelian Randomisation, MTAO prioritised genes/splicing show increased evolutionary constraints. Many newly discovered genes/splicing regions underlie previously thought single-gene loci to influence multiple traits.

genomics↗

bioRxiv2022

Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle

Many quantitative trait loci (QTL) are located in non-coding genomic regions. Therefore, QTL are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription so QTL changing gene expression (eQTL) or RNA splicing (sQTL) are expected to significantly contribute to phenotypic variations. Here, we quantify the contribution of eQTL and sQTL detected from 16 tissues (N~5,000) to 37 complex traits of ~120k cattle. Using Bayesian methods, we show that including more regulatory variants in the model explains larger proportions of heritability. Across traits, cis and trans eQTL and sQTL detected from 16 tissues jointly explain ~70% (SE=0.5%) of heritability, 44% more than expected from the same number of random variants, where trans e/sQTL contribute 24% (14% more than expected). Multi-tissue cis and trans e/sQTL also explain 71% (SE=0.3%) of heritability for the metabolome, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.

genomics↗

bioRxiv2022

The conservation of human functional variants and their effects across mammals

Despite the clear potential of livestock models of human functional variants to provide important insights into the biological mechanisms driving human diseases and traits, their use to date has been limited. Generating such models via genome editing is costly and time consuming, and it is unclear which variants will have conserved effects across species. In this study we address these issues by studying naturally occurring livestock models of human functional variants. We show that orthologues of over 1.6 million human variants are already segregating in domesticated mammalian species, including several hundred previously directly linked to human traits and diseases. Models of variants linked to particular phenotypes, including metabolomic disorders and height, have been preferentially maintained across species, meaning studying the genetic basis of these phenotypes is particularly tractable in livestock. Using machine learning we demonstrate it is possible to identify human variants that are more likely to have an existing livestock orthologue, and, importantly, we show that the effects of functional variants are often conserved in livestock, acting on orthologous genes with the same direction of effect. Consequently, this work demonstrates the substantial potential of naturally occurring livestock carriers of orthologues of human functional variants to disentangle their functional impacts.

genetics↗

bioRxiv2022

Expanded Analysis of Pigmentation Genetics in UK Biobank

The genetics of pigmentation is an excellent model for understanding gene interactions in a trait almost entirely unaffected by environment. We have analysed pigmentation phenotypes in UK Biobank using DISSECT, a tool which enables genome-wide association studies (GWAS) whilst accounting for relatedness between individuals, and thus allows a much larger cohort to be studied. We have increased the number of candidate genes associated with red and blonde hair colour, basal skin colour and tanning response to UV radiation. As previously described, we find almost all red hair individuals have two variant MC1R alleles; exome sequence data expands the number of associated coding variants. Rare red-headed individuals with only a single MC1R variant are enriched for an associated eQTL at the ASIP gene. We find that females are most likely to self-report red or blonde hair, paler skin and less tanning ability than men, and that variants at KITLG, MC1R, OCA2 and IRF4 show significant sex differences in effect. After taking sex into account, pigmentation phenotypes are not correlated with sex hormone levels, except for tanning ability, which shows a positive correlation with testosterone in men. Across the UK there is a correlation between place of birth and hair colour; red hair being more common in the north and west, whilst blonde hair is more common in the east. Combining GWAS with transcriptome data to generate a transcriptome wide association study identifies candidate genes whose expression in skin or melanocytes shows association with pigmentation phenotypes. A comparison of candidates associated with different pigmentation phenotypes finds that candidates for blonde hair, but not skin colour, are enriched for skin and hair genes suggesting that it may be hair shape and structure that impacts hair colour, rather than the melanocyte/keratinocyte interaction.

genetics↗

bioRxiv2020

A comprehensive catalogue of regulatory variants in the cattle transcriptome

Characterization of genetic regulatory variants acting on the transcriptome of livestock is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here, we build a cattle Genotype-Tissue Expression atlas (cattle GTEx, http://cgtex.roslin.ed.ac.uk/) as part of the pilot phase of Farm animal GTEx (FarmGTEx) project for the research community based on publicly available 11,642 RNA-Seq datasets. We describe the landscape of the transcriptome across over 100 tissues and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 24 major tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multi-omics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association study (TWAS) and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.

genomics↗

bioRxiv2020

SNP heritability: What are we estimating?

The SNP heritability [Formula] has become a central concept in the study of complex traits. Estimation of [Formula] based on genomic variance components in a linear mixed model using restricted maximum likelihood has been widely adopted as the method of choice were individual level data are available. Empirical results have suggested that this approach is not robust if the population of interest departs from the assumed statistical model. Prolonged debate of the appropriate model choice has yielded a number of approaches to account for frequency- and linkage disequilibrium dependent genetic architectures. Here we analytically resolve the question of how these estimates relate to [Formula] of the population from which samples are drawn. In particular, we show that the correct model for the purpose of inference about [Formula] does not require knowledge of the true genetic architecture of a trait. More generally, our results provide a complete perspective of these class of estimators of [Formula], highlighting practical shortcomings of current practise. We illustrate our theoretical results using simulations and data from UK Biobank.

genetics↗

bioRxiv2020

Sexual differences in genetic architecture in UK Biobank

Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood. Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses. This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

genomics↗

bioRxiv2019

Proteome-by-phenome Mendelian Randomisation detects 38 proteins with causal roles in human diseases and traits

Target identification remains a crucial challenge in drug development. To enable unbiased detection of proteins and pathways that have a causal role in disease pathogenesis or progression, we propose proteome-by-phenome Mendelian Randomisation (P2MR). We first detected genetic variants associated with plasma concentration of 249 proteins. We then used 64 replicated variants in two-sample Mendelian Randomisation to quantify evidence of a causal role for each protein across 846 phenotypes: this yielded 509 robust protein-outcome links. P2MR provides substantial promise for drug target prioritisation. We provide confirmatory evidence for a causal role for the proteins encoded at multiple cardiovascular disease risk loci (FGF5, IL6R, LPL, LTA), and discovered that intestinal fatty acid binding protein (FABP2) contributes to disease pathogenesis. Additionally, we find and replicate evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia. Our results provide specific prediction of the effects of changes of plasma protein concentration on complex phenotypes in humans.

genomics↗

bioRxiv2018

The genetic architecture of hair colour in the UK population

We have extensively mapped the genes responsible for hair colour in the UK population. MC1R mutations are well established as the principal genetic cause of red hair colour, but with variable penetrance. We find variation at genes encoding its agonist (POMC), inverse agonist (ASIP) and other loci contribute to red hair and demonstrate epistasis between MC1R and some of these loci. Blonde hair is associated with over 200 loci, and we find a genetic continuum from black through dark and light brown to blonde. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour.

genetics↗

bioRxiv2017

Indirect assortative mating for human disease and longevity

Phenotypic correlations of couples for phenotypes evident at the time of mate choice, like height, are well documented. Similarly, phenotypic correlations among partners for traits not directly observable at the time of mate choice, like longevity or late-onset disease status, have been reported. Partner correlations for longevity and late-onset disease are comparable in magnitude to correlations in 1st degree relatives. These correlations could arise as a consequence of convergence after mate choice, due to initial assortment on observable correlates of one or more risk factors (e.g. BMI), referred to as indirect assortative mating, or both. Using couples from the UK Biobank cohort, we show that longevity and disease history of the parents of white British couples is correlated. The correlations in parental longevity are replicated in the FamiLinx cohort. These correlations exceed what would be expected due to variations in lifespan based on year and location of birth. This suggests the presence of assortment on factors correlated with disease and lifespan, which show correlations across generations. Birth year, birth location, Townsend Deprivation Index, height, waist to hip ratio, BMI and smoking history of UK Biobank couples explained ~70% of the couple correlation in parental lifespan. For cardiovascular diseases, in particular hypertension, we find significant correlations in genetic values among partners, which support a model where partners assort for risk factors genetically correlated with cardiovascular disease. Identifying the factors that mediate indirect assortment on longevity and human disease risk will help to unravel what factors affect human disease and ultimately longevity.

genetics↗

bioRxiv2017

An atlas of genetic associations in UK Biobank

Genome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS, http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

genomics↗

bioRxiv2016

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohns disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.\n\nONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.

genomics↗