Biology subjects

O'Dushlaine, C.

Publications and source records attributed to O'Dushlaine, C..

14 recordsLinked to original sources

bioRxiv2026

Recovering signatures of archaic introgression using ancestral recombination graphs

Neanderthal and Denisovan genomes have reshaped our understanding of archaic introgression. Yet, the limited number of archaic genomes sequenced and the reliance on unadmixed outgroups have left much of this history unresolved. We introduce TRACE, a method to identify archaic ancestry using features of ancestral recombination graphs inferred from contemporary genomes alone. Simulations show that TRACE reliably detects archaic introgression without requiring archaic genomes or unadmixed outgroups. Applied to 1000 Genomes data, TRACE recovers known Neanderthal and Denisovan introgression and reveals signals of ghost admixture from previously uncharacterized hominins in both Africans and non-Africans. Strikingly, ghost ancestry persists in Neanderthal and Denisovan ancestry deserts, challenging their interpretation as Homo sapiens-specific regions. In Oceanians, TRACE finds deep lineages enriched in Denisovan--and not Neanderthal--regions, supporting a model of super-archaic gene flow. TRACE provides a scalable framework for mapping the legacy of archaic introgression in the absence of archaic genome sequences.

evolutionary biology↗

bioRxiv2024

Causal considerations can determine the utility of machine learning assisted GWAS

Machine Learning (ML) is increasingly employed to generate phenotypes for genetic discovery, either by imputing existing phenotypes into larger cohorts or by creating novel phenotypes. While these ML-derived phenotypes can significantly increase sample size, and thereby empower genetic discovery, they can also inflate the false discovery rate (FDR). Recent research has focused on developing estimators that leverage both true and machine-learned phenotypes to properly control the type-I error. Our work complements these efforts by exploring how the true positive rate (TPR) and FDR depend on the causal relationships among the inputs to the ML model, the true phenotypes, and the environment. Using a simulation-based framework, we study architectures in which the machine-learned proxy phenotype is derived from biomarkers (i.e. inputs) either causally upstream or downstream of the target phenotype. We show that no inflation of the false discovery rate occurs when the proxy phenotype is generated from upstream biomarkers, but that false discoveries can occur when the proxy phenotype is generated from downstream biomarkers. Next, we show that power to detect variants truly associated with the target phenotype depends on its heritability and correlation with the proxy phenotype. However, the source of the correlation is key to evaluating a proxy phenotype's utility for genetic discovery. We demonstrate that evaluating machine-learned proxy phenotypes using out-of-sample predictive performance (e.g. phenotypic correlation) provides a poor lens on utility. This is because overall predictive performance does not differentiate between genetic and environmental correlation. In addition to parsing these properties of machine-learned phenotypes via simulations, we further illustrate them using real-world data from the UK Biobank.

genetics↗

bioRxiv2024

Extending the Coding-variant Allelic Series Test to Summary Statistics

Genes for which a dose-response relationship exists between mutational severity and phenotypic impact make for logical therapeutic targets, as the effects of pharmacological modulation can be anticipated from the natural variation present in a population. We refer to genes where such a dose-response relationship exists as harboring allelic series, and have introduced the rare coding-variant allelic series test (COAST) for their identification. The original COAST required access to individual-level data. However, such data are often unavailable due to privacy concerns or logistical challenges. Meanwhile, single-variant summary statistics of the type produced by genome-wide association studies are plentiful. Here we introduce COAST-SS, an extension of COAST that accepts summary statistics as input, namely the per-variant effect sizes and standard errors, along with estimates of the minor allele frequency and local linkage disequilibrium (LD). As a running example, we consider identifying allelic series for circulating lipid traits, drawing on data from the UK Biobank, Million Veterans Program, and Trans-Omics of Precision Medicine Program. Through extensive analyses of real and simulated data, we demonstrate that COAST-SS provides p-values effectively equivalent to those from the original COAST. Interestingly, we find that when LD is low, as is expected among rare variants, COAST-SS is robust to misspecification of the LD matrix, providing valid inference even when the LD matrix is set to the identity matrix. We explore several strategies for annotating the pathogenicity of variants supplied to COAST-SS, finding that they often yield similar power for detecting candidate allelic series. Lastly, we employ COAST-SS to screen for lipid trait allelic series in a meta-analyzed cohort of up to 840K subjects. COAST-SS has been incorporated into the publicly available AllelicSeries R package.

genetics↗

bioRxiv2023

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

Machine learning (ML)-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the mean{chi} 2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits, and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEMs utility by evaluating embeddings and multivariate traits in two separate datasets: i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance, and ii) a real data from the UK Biobank including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

bioinformatics↗

bioRxiv2023

Pitfalls in performing genome-wide association studies on ratio traits

Genome-wide association studies (GWAS) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator-driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

genetics↗

bioRxiv2022

Whole-genome sequencing across 449 samples spanning 47 ethnolinguistic groups provides insights into genetic diversity in Nigeria

African populations have been drastically underrepresented in genomics research and failure to capture the genetic diversity across the numerous ethnolinguistic groups (ELGs) found in the continent has hindered the equity of precision medicine initiatives globally. Here, we describe the whole-genome sequencing of 449 Nigerian individuals across 47 unique self-reported ELGs. Population structure analysis reveals genetic differentiation amongst our ELGs, consistent with previous findings. From the 36 million SNPs and INDELs discovered in our dataset, we provide a high-level catalog of both novel and medically-relevant variation present across the ELGs. These results emphasize the value of this resource for genomics research, with added granularity by representing multiple ELGs from Nigeria. Our results also underscore the potential of using these cohorts with larger sample sizes to improve our understanding of human ancestry and health in Africa.

genomics↗

bioRxiv2022

A survey of proteomic variation across two ethnic groups in Nigeria and its relationship to obesity risk

Proteomic variation between individuals has immense potential for identifying novel drug targets and disease mechanisms. However, with high-throughput proteomic technologies still in their infancy, they have largely been applied in large majority European ancestry cohorts (e.g. the UK Biobank). An open question is the degree to which proteomic signatures seen in European and other groups mirror those seen in diverse populations, such as cohorts from Africa. Coupled with genetic information, we can also gain a better understanding of the role of genetic variants in the regulation of the proteome and subsequent disease mechanisms. To address the gap in our understanding of proteomic variation in individuals of African ancestry, we collected proteomic data from 176 individuals across two ethnic groups (Igbo and Yoruba) in Nigeria. These individuals were also stratified into high BMI (BMI > 30 kg/m2) and normal BMI (20 kg/m2 < BMI < 30 kg/m2) categories. We characterized differences in plasma protein abundance using the Olink Explore 1536 panel between high and normal BMI individuals, finding strong associations consistent with previously known signals in individuals of European descent. We additionally found 73 sentinel cis-pQTL in this dataset, with 21 lead cis-pQTL not observed in catalogs of variation from European-ancestry individuals. In summary, our study highlights the value of leveraging proteomic data in cohorts of diverse ancestry for investigating trait-specific mechanisms and discovering novel genetic regulators of the plasma proteome.

genomics↗

bioRxiv2020

Biological insights from the whole genome analysis of human embryonic stem cells

There has not yet been a systematic analysis of hESC whole genomes at a single nucleotide resolution. We therefore performed whole genome sequencing (WGS) of 143 hESC lines and annotated their single nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer scale structural and single nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESCs genomes and human blood-derived genomes at similar frequencies. However, WGS did identify SNVs associated with cancer or other diseases that will likely alter cellular phenotypes and may compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/337352v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1c28e3aorg.highwire.dtl.DTLVardef@7a07corg.highwire.dtl.DTLVardef@b95f2borg.highwire.dtl.DTLVardef@1732af_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFMerkle and Ghosh et al. describe insights from the whole genome sequences of commonly used human embryonic stem cell (hESC) lines. Analyses of these sequences show that while hESC genomes had more large structural variants than humans do from genetic inheritance, hESCs did not have an observable excess of finer-scale variants. However, many hESC lines contained rare loss-of-function variants and combinations of common variants that may profoundly shape their biological phenotypes. Thus, genome sequencing data can be valuable to those selecting cell lines for a given biological or clinical application, and the sequences and analysis reported here should facilitate such choices. HIGHLIGHTSO_LIOne third of hESCs we analysed are siblings, and almost all are of European ancestry C_LIO_LILarge structural variants are common in hESCs, but finer-scale variation is similar to that human populations C_LIO_LIMany strong-effect loss-of-function mutations and cancer-associated mutations are present in specific hESC lines C_LIO_LIWe provide user-friendly resources for rational hESC line selection based on genome sequence C_LI

genomics↗

bioRxiv2020

Exome-wide association studies in general and long-lived populations identify genetic variants related to human age

Aging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40x10-10) and ZYG11A rs74227999 (p = 2.50x10-08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males [≥] 92 years, Females [≥] 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

genetics↗

bioRxiv2020

Computationally efficient whole genome regression for quantitative and binary traits

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes, and only requires local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives which must load genomewide matrices into memory. This results in substantial savings in compute time and memory usage. The method is applicable to both quantitative and binary phenotypes, including rare variant analysis of binary traits with unbalanced case-control ratios where we introduce a fast, approximate Firth logistic regression test. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach compared to several existing methods using quantitative and binary traits from the UK Biobank dataset with up to 407,746 individuals.

genetics↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2017

KaryoScan: abnormal karyotype detection from whole-exome sequence

MotivationDetection of abnormal karyotypes from whole-exome sequencing has significant clinical potential, enabling a primary screen for chromosomal anomalies among samples undergoing short-read sequencing for nucleotide resolution genomic characterization.\n\nResultsWe present KaryoScan, a high-throughput method for detecting chromosomal anomalies within large cohort exome sequencing studies. We detect and validate autosomal and sex chromosomal aneuploidies in a large exome sequencing cohort, and demonstrate detection of smaller and complex events (partial chromosome, mosaic, copy neutral, and complex rearrangements), representing the range of anomalies that can be uncovered from the exome.\n\nAvailabilityhttps://github.com/rgcgithub/karyoscan

bioinformatics↗

bioRxiv2017

Profiling and leveraging relatedness in a precision medicine cohort of 92,455 exomes

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in-line with expectations given the underlying population and ascertainment approach. For example, we identified [~]66,000 close (first- and second-degree) relationships within DiscovEHR involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships as DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in [~]1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations through reconstructed pedigrees, including a tandem duplication in LDLR causing familial hypercholesterolemia. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

genomics↗

bioRxiv2017

Profiling copy number variation and disease associations from 50,726 DiscovEHR Study exomes

Copy number variants (CNVs) are a substantial source of genomic variation and contribute to a wide range of human disorders. Gene-disrupting exonic CNVs have important clinical implications as they can underlie variability in disease presentation and susceptibility. The relationship between exonic CNVs and clinical traits has not been broadly explored at the population level, primarily due to technical challenges. We surveyed common and rare CNVs in the exome sequences of 50,726 adult DiscovEHR study participants with linked electronic health records (EHRs). We evaluated the diagnostic yield and clinical expressivity of known pathogenic CNVs, and performed tests of association with EHR-derived serum lipids, thereby evaluating the relationship between CNVs and complex traits and phenotypes in an unbiased, real-world clinical context. We identified CNVs from megabase to exon-level resolution, demonstrating reliable, high-throughput detection of clinically relevant exonic CNVs. In doing so, we created a catalog of high-confidence common and rare CNVs and refined population frequency estimates of known and novel gene-disrupting CNVs. Our survey among an unselected clinical population provides further evidence that neuropathy-associated duplications and deletions in 17p12 have similar population prevalence but are clinically under-diagnosed. Similarly, adults who harbor 22q11.2 deletions frequently had EHR documentation of neurodevelopmental/neuropsychiatric disorders and congenital anomalies, but not a formal genetic diagnosis (i.e., deletion). In an exome-wide association study of lipid levels, we identified a novel five-exon duplication within LDLR segregating in a large kindred with features of familial hypercholesterolemia. Exonic CNVs provide new opportunities to understand and diagnose human disease.

genomics↗