Biology subjects

O'Connor, L. J.

Publications and source records attributed to O'Connor, L. J..

15 recordsLinked to original sources

bioRxiv2026

Highly efficient genotype compression leveraging genealogical relatedness

Large genetic datasets are terabytes in size, presenting a computational challenge that will intensify as sequencing efforts scale. We present a lossless compression algorithm, kodama, which supports matrix multiplication and is suitable for large-scale statistical analyses. Kodama leverages genealogical relatedness among nominally unrelated individuals and infers a novel data structure similar to the ancestral recombination graph (ARG), called the linear ARG. We applied kodama to whole genome sequencing data from UK Biobank and All of Us. Inferred linear ARGs were 17-89 times smaller on disk compared to the input data; the entire UK Biobank N=200k dataset can be loaded into memory (58GB). Compared with the recently proposed genotype representation graph (GRG), the linear ARG is 2.5 times smaller. Genotype matrix multiplications, which are the bottleneck in most statistical applications, are extremely fast with the linear ARG; we performed a GWAS on the UK Biobank 200k cohort across 89 traits with 42 covariates in 100 seconds, representing a 4,700-fold speedup over PLINK 2.0. We expect that the linear ARG will enable genetic analyses to scale to millions of samples.

genetics↗

bioRxiv2025

Defining and cataloging variants in pangenome graphs

Structural variation causes some human haplotypes to align poorly with the linear reference genome, leading to reference bias. A pangenome reference graph could ameliorate this bias by relating a sample to multiple reference assemblies. However, this approach requires a new definition of a genetic variant. We introduce a definition of pangenome variants and a method, pantree, to identify them. Our approach involves a pangenome reference tree which includes all nodes (sequences) of the pangenome graph, but only a subset of its edges; non-reference edges are variant edges. Our variants are biallelic and have well-defined positions. Analyzing the Minigraph-Cactus draft human pangenome reference graph, we identified 29.6 million genetic variants. Most variants (99.2%) are small, and most small variants (73.9%) are SNPs. 3.5 million variants (11.7%) have a reference allele which is not on GRCh38; these variants are difficult to detect without a pangenome reference, or with existing pangenome-based approaches. They tend to be embedded within tangled, multiallelic regions. We analyze two medically relevant regions, around the HLA-A and RHD genes, identifying thousands of small variants embedded within several large insertions, deletions, and inversions. We release an open-source software tool together with a VCF variant catalogue.

genomics↗

bioRxiv2025

Principled measures and estimates of trait polygenicity

The polygenicity of traits is often invoked and sometimes quantified in quantitative, statistical, and human genetics. What do we mean by the polygenicity of a trait? We propose a principled definition that encompasses a range of polygenicity measures. We show that these measures satisfy certain mathematical properties, we argue that these properties are sensible if not necessary, and we show that, conversely, measures that satisfy these properties also satisfy our definition. We consider four specific measures in greater detail, describe how they differ and show that three of them can be estimated from GWAS summary statistics using an existing method, Fourier Mixture Regression. We estimate these measures for 36 traits in humans. We find a dearth of traits with polygenicity values that fall within the large gap between Mendelian and highly polygenic traits. We discuss the evolutionary and cellular processes underlying trait polygenicity.

genetics↗

bioRxiv2024

Effect heterogeneity reveals complex pleiotropic effects of rare coding variants

Recent expansion of large-scale biobank resources has enabled rare-variant association studies (RVAS) and systematic investigation of rare variant pleiotropy across thousands of phenotypes simultaneously. However, existing statistical frameworks for dissecting pleiotropy were largely developed for common variants and are not well suited to gene-based rare variant signals, limiting the interpretation of cross-phenotype associations. Here, we develop ALLSPICE, a likelihood-based method that tests whether rare variant effects in genes exhibiting cross-phenotype associations are proportional or heterogeneous across continuous traits while accounting for phenotypic correlation using summary statistics. ALLSPICE is well-calibrated in simulations and is implemented as an R package for scalable analysis of gene-level rare-variant burden associations. We applied ALLSPICE to RVAS of 359 continuous traits in the UK Biobank and identified 124 significant heterogeneous events among 11,810 pairs of gene-trait associations. By identifying effect heterogeneity within genes associated with multiple phenotypes, ALLSPICE clarifies shared and heterogeneous rare variant architectures underlying cross-phenotype associations and provides insight into rare variant pleiotropy.

genetics↗

bioRxiv2024

Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.

genomics↗

bioRxiv2022

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking. We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures.

bioinformatics↗

bioRxiv2022

Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using massive local correlation matrices, but this approach is slow, especially in ancestrally diverse studies. Here, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We publish LDGMs and ancestry specific LDGM precision matrices for 18 million common SNPs (MAF>1%) in five ancestry groups, validate their accuracy, and demonstrate order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implement an extremely fast multi-ancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally genetic association data across millions of variants and individuals.

genetics↗

bioRxiv2021

Shared components of heritability across genetically correlated traits

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed Pleiotropic Decomposition Regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of 5-6 traits genetically correlated with coronary disease, asthma, and type II diabetes respectively, producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes compared with the original summary statistics: by 94% and 70% for asthma and T2D out of sample, and by a predicted 300% for CAD.

genetics↗

bioRxiv2020

Negative short-range genomic autocorrelation of causal effects on human complex traits

Most models of complex trait genetic architecture assume that signed causal effect sizes of each SNP (defined with respect to the minor allele) are uncorrelated with those of nearby SNPs, but it is currently unknown whether this is the case. We develop a new method, autocorrelation LD regression (ACLR), for estimating the genome-wide autocorrelation of causal minor allele effect sizes as a function of genomic distance. Our method estimates these autocorrelations by regressing the products of summary statistics on distance-dependent LD scores. We determined that ACLR robustly assesses the presence or absence of nonzero autocorrelation, producing unbiased estimates with well-calibrated standard errors in null simulations regardless of genetic architecture; if true autocorrelation is nonzero, ACLR correctly detects its sign, although estimates of the autocorrelation magnitude are susceptible to bias in cases of certain genetic architectures. We applied ACLR to 31 diseases and complex traits from the UK Biobank (average N=331K), meta-analyzing results across traits. We determined that autocorrelations were significantly negative at distances of 1-50bp (P = 8 x 10-6, point estimate -0.35 {+/-}0.08) and 50-100bp (P = 2 x 10-3, point estimate -0.33 {+/-} 0.11). We show that the autocorrelation is primarily driven by pairs of SNPs in positive LD, which is consistent with the expectation that linked SNPs with opposite effects are less impacted by natural selection. Our findings suggest that this mechanism broadly affects complex trait genetic architectures, and we discuss implications for association mapping, heritability estimation, and genetic risk prediction.

genetics↗

bioRxiv2020

The distribution of common-variant effect sizes

The genetic effect-size distribution describes the number of variants that affect disease risk and the range of their effect sizes. Accurate estimates of this distribution would provide insights into genetic architecture and set sample-size targets for future genome-wide association studies. We developed Fourier Mixture Regression (FMR) to estimate common-variant effect-size distributions from GWAS summary statistics. We validated FMR in simulations and in analyses of UK Biobank data, using interim-release summary statistics (max N=145k) to predict the results of the full release (N=460k). Analyzing summary statistics for 10 diseases (avg Neff=169k) and 22 other traits, we estimated the sample size required for genome-wide significant SNPs to explain 50% of SNP-heritability. For most diseases the requisite number of cases is 100k-1M, an attainable number; ten times more would be required to explain 90% of heritability. In well-powered GWAS, genome-wide significance is a conservative threshold, and loci at less stringent thresholds have true positive rates that remain close to 1 if confounding is controlled. Analyzing the shape of the effect-size distribution, we estimate that heritability accumulates across many thousands of SNPs with a wide range of effect sizes: the largest effects (at the 90th percentile of heritability) are 100 times larger than the smallest (10th percentile), and while the midpoint of this range varies across traits, its size is similar. These results suggest attainable sample size targets for future GWAS, and they underscore the complexity of genetic architecture.

genetics↗

bioRxiv2019

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winners curse. In simulations with in-sample LD, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N=318K) with in-sample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

genetics↗

bioRxiv2019

Quantifying genetic effects on disease mediated by assayed gene expression levels

Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

genetics↗

bioRxiv2018

Polygenicity of complex traits is explained by negative selection

Complex traits and common disease are highly polygenic: thousands of common variants are causal, and their effect sizes are almost always small. Polygenicity could be explained by negative selection, which constrains common-variant effect sizes and may reshape their distribution across the genome. We refer to this phenomenon as flattening, as genetic signal is flattened relative to the underlying biology. We introduce a mathematical definition of polygenicity, the effective number of associated SNPs, and a robust statistical method to estimate it. This definition of polygenicity differs from the number of causal SNPs, a standard definition; it depends strongly on SNPs with large effects. In analyses of 33 complex traits (average N=361k), we determined that common variants are [~]4x more polygenic than low-frequency variants, consistent with pervasive flattening. Moreover, functionally important regions of the genome have increased polygenicity in proportion to their increased heritability, implying that heritability enrichment reflects differences in the number of associations rather than their magnitude (which is constrained by selection). We conclude that negative selection constrains the genetic signal of biologically important regions and genes, reshaping genetic architecture.

genetics↗

bioRxiv2018

Quantification of genetic components of population differentiation in UK Biobank traits reveals signals of polygenic selection

The genetic architecture of most human complex traits is highly polygenic, motivating efforts to detect polygenic selection involving a large number of loci. In contrast to previous work relying on top GWAS loci, we developed a method that uses genome-wide association statistics and linkage disequilibrium patterns to estimate the genome-wide genetic component of population differentiation of a complex trait along a continuous gradient, enabling powerful inference of polygenic selection. We analyzed 43 UK Biobank traits and focused on PC1 and North-South and East-West birth coordinates across 337K unrelated British-ancestry samples, for which our method produced close to unbiased estimates of genetic components of population differentiation and high power to detect polygenic selection in simulations across different trait architectures. For PC1, we identified signals of polygenic selection for height (74.5{+/-}16.7% of 9.3% total correlation with PC1 attributable to genome-wide genetic effects; P = 8.4x10-6) and red hair pigmentation (95.9{+/-}24.7% of total correlation with PC1 attributable to genome-wide genetic effects; P = 1.1x10-4); the bulk of the signal remained when removing genome-wide significant loci, even though red hair pigmentation includes loci of large effect. We also detected polygenic selection for height, systolic blood pressure, BMI and basal metabolic rate along North-South birth coordinate, and height and systolic blood pressure along East-West birth coordinate. Our method detects polygenic selection in modern human populations with very subtle population structure and elucidates the relative contributions of genetic and non-genetic components of trait population differences.

genetics↗

bioRxiv2017

Estimating the proportion of disease heritability mediated by gene expression levels

Disease risk variants identified by GWAS are predominantly noncoding, suggesting that gene regulation plays an important role. eQTL studies in unaffected individuals are often used to link disease-associated variants with the genes they regulate, relying on the hypothesis that noncoding regulatory effects are mediated by steady-state expression levels. To test this hypothesis, we developed a method to estimate the proportion of disease heritability mediated by the cis-genetic component of assayed gene expression levels. The method, gene expression co-score regression (GECS regression), relies on the idea that, for a gene whose expression level affects a phenotype, SNPs with similar effects on the expression of that gene will have similar phenotypic effects. In order to distinguish directional effects mediated by gene expression from non-directional pleiotropic or tagging effects, GECS regression operates on pairs of cis SNPs in linkage equilibrium, regressing pairwise products of disease effect sizes on products of cis-eQTL effect sizes. We verified that GECS regression produces robust estimates of mediated effects in simulations. We applied the method to eQTL data in 44 tissues from the GTEx consortium (average NeQTL = 158 samples) in conjunction with GWAS summary statistics for 30 diseases and complex traits (average NGWAS = 88K) with low pairwise genetic correlation, estimating the proportion of SNP-heritability mediated by the cis-genetic component of assayed gene expression in the union of the 44 tissues. The mean estimate was 0.21 (s.e. = 0.01) across 30 traits, with a significantly positive estimate (p < 0.001) for every trait. Thus, assayed gene expression in bulk tissues mediates a statistically significant but modest proportion of disease heritability, motivating the development of additional assays to capture regulatory effects and the use of our method to estimate how much disease heritability they mediate.

genetics↗