Biology subjects

Hawkes, G.

Publications and source records attributed to Hawkes, G..

7 recordsLinked to original sources

bioRxiv2026

Quantifying heritability in functional regions of the coding and non-coding genome across diverse individuals

Most heritability studies have focused on common genetic variation and have largely excluded non-European or highly admixed populations. Here, we estimated the whole-genome heritability (h2WGS) of seven complex traits using 73,600,953 autosomal and chromosome X variants for 198,931 unrelated individuals from three genetic similarity groups. Adjusting for population stratification, we showed that rare variants (minor allele frequency<0.01) explain 30.6-67.7% of h2WGS. We used simulations to show how functionally-stratified heritability can be inflated due to linkage with missing or lower-quality variants. Finally, after accounting for confounding effects, we found substantial heritability enrichment from variants in non-coding RNA exons (maximum fold enrichment, FE=8.7, P=3.56e-7), UTRs (23.0, P=3.46e-30), distal regulatory regions (33.8, P=3.26e-42), and protein-coding exons (58.0, P=7.78e-15). Our study provides a framework for investigating the genetic architecture of complex human phenotypes using diverse whole-genome sequencing data and highlights the contribution of rare and non-coding genetic variants to the heritability of complex traits.

genomics↗

bioRxiv2026

Federated cross-biobank conditional analysis identifies LDL-C lowering effects of DNAJC13 haploinsufficiency and LDLR regulation

Whole genome sequencing in diverse population-scale biobanks offers new insights into the genetic architecture of complex traits from rare and non-coding variants. However, rare single variant and aggregate associations are often confounded by linkage disequilibrium and haplotype structure, resulting in large numbers of false-positive associations. Previous methods that rely on reference panels or linkage disequilibrium-matrices to determine conditional independence in meta-analyses do not scale to very rare variants, which may be observed in only one biobank and can exhibit long-range haplotypes. Here, we implement a federated approach to perform iterative conditional meta-analysis on individual-level genotype and phenotype data across biobanks while adhering to data sharing policies. We applied our methodology to a meta-analysis of LDL-C in 614,375 individuals from UK Biobank and All of Us, encompassing six genetic ancestry groups. After conditioning, only 4.3% of significantly associated rare single variants and 6.9% of aggregates remained statistically independent. The proportion of significant aggregates that remained independent after conditioning was higher for coding-based tests than non-coding. We further validate that our approach effectively suppresses false-positive associations using simulations centred on the LDLR locus. We identify allelic series of variants associated with reduced LDL-C, including loss-of-function variants in DNAJC13 and variants in the 3-prime untranslated region of LDLR. Our results highlight that federated conditioning can distinguish independent rare variant signals from linkage and haplotype structure artifacts in multi-ancestry meta-analyses across separate biobanks.

genetics↗

bioRxiv2025

Genotype-level quality control substantially reduces error rates in population-scale whole-genome sequencing

Population-scale whole-genome sequencing data will contain many individual-level genotype errors, even after allele-level quality control (QC). We establish the need for genotype-level QC using UK Biobank (N=490,726) and All of Us v8 (N=414,830), where we remove up to 100 million ([~]9%) additional low-quality variants. We demonstrate reduced false positive rate in downstream genetic association studies, highlight the power of parent-offspring trios for QC, and illustrate the need for sex-specific X-chromosome filtering. We provide a QCed All of Us v8 dataset in plink-pgen format, and an efficient pipeline for QC and conversion from VCF to plink-pgen for UK Biobank.

genetics↗

bioRxiv2025

Whole-genome sequencing analysis of anthropometric traits in 672,976 individuals reveals convergence between rare and common genetic associations

Genetic association studies have mostly focussed on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we used whole-genome sequence (WGS) data in 672,976 individuals of diverse ancestry to evaluate the contribution and architecture of rare non-coding variants to three commonly studied anthropometric traits: height, body mass index (BMI) and waist-hip ratio adjusted for BMI (WHRadjBMI). Analysing 447,461 individuals in UK Biobank for discovery and 225,515 individuals in All of Us for replication, we identified 90 novel rare and low-frequency single variant associations. This includes two independent rare variants upstream of IGF2BP2 that both substantially reduce WHRadjBMI, but have distinct effects on other adiposity traits. We identified 135 coding variant aggregates, several of which were missed by exome sequencing studies. For example, UBR3 protein-truncating variants were associated with a 2.7kg/m2 increase in BMI. We additionally identified 51 non-coding variant aggregate associations, including in the 5UTR of FGF18 (a highly constrained gene with no previously reported coding associations) associated with up to 6cm effects on height. We show that 97% of rare variant associations occur near GWAS loci demonstrating convergence of rare and common variant associations. Finally, we show that ultra rare variants (MAF<0.01%) explain a small fraction of heritability (<10%) compared to common variants for these traits, that heritability is largely shared across ancestries, and that this heritability is concentrated at or near common variant loci. Our work demonstrates the importance of large-scale WGS for fully understanding the genetic architecture of complex traits.

genetics↗

bioRxiv2023

Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N=200,003), TOPMed (N=87,652) and All of Us (N=45,445). We performed rare (<0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal, intergenic and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 x 10-10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed a novel approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

genetics↗

bioRxiv2023

Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regions associated with circulating protein levels

The role of non-coding rare variation in common phenotypes is largely unknown, due to a lack of whole-genome sequence data, and the difficulty of categorising non-coding variants into biologically meaningful regulatory units. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequence data, consisting of 391 million variants and 1,450 circulating protein levels in [~]20,000 UK Biobank participants. We identified 777 independent rare non-coding single variants associated with circulating protein levels (P<1x10-9), after conditioning on protein-coding and common associated variants. Rare non-coding aggregate testing identified 108 conditionally independent regulatory regions. Unlike protein-coding variation, rare non-coding genetic variation was almost as likely to increase as decrease protein levels. The regions we identified overlapped predicted tissue-specific enhancers more than promoters, suggesting they represent tissue-specific regulatory regions. Our results have important implications for the identification, and role, of rare non-coding variation associated with common human phenotypes.

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↗