Biology subjects

Price, A. L.

Publications and source records attributed to Price, A. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Ancient DNA reveals that natural selection has upregulated the immune system over the last 10,000 years

The specific mechanisms through which human biology and disease susceptibility evolved with major shifts in West Eurasian environments and societies over the last 10,000 years(1)--particularly rising infectious burden(2)--remain poorly characterized, despite ancient DNA studies(3-6) identifying hundreds of candidate loci under positive selection(6). Here, we identify specific immune diseases/traits, genes/variants, pathways, and tissues/cell types impacted by natural selection by systematically integrating variant-level selection statistics with genome-wide association study (GWAS), quantitative trait locus (QTL), and molecular bulk/single-cell and gene pathway data. Genome-wide, positively-selected alleles are associated with reduced susceptibility to infectious diseases like tuberculosis (TB), influenza, and intestinal infections; consistent with selection-signal enrichments in immune cells within barrier tissues such as the respiratory tract and gut mucosa. In contrast, positively-selected alleles increase risk of intestinal inflammatory disease and autoimmune hypothyroidism, supportive of a tradeoff between infection and immune-mediated pathology, and consistent with adaptive alleles being QTLs for genes upregulating inflammation and other host-defense pathways. We reveal many novel adaptive loci with convergent signals from selection, infectious disease GWAS and immune-gene QTLs (including at FUT6 for intestinal infections; at ASAP1 for TB; and at LYZ, an antimicrobial enzyme), fine-mapping selection onto likely causal variants. Surprisingly, adaptive alleles had a protective effect on allergic conditions like asthma and dermatitis, challenging a common view that these conditions arose through evolutionary mismatch of present-day hygienic contexts relative to past, pathogen-rich environments(7).

evolutionary biology↗

bioRxiv2025

Investigating Rat-Brain Normal Tissue and Tumor FLASH Effects with a Novel Very High Energy Electron Beam

PurposeUltra-high dose rate (FLASH) irradiation is reported to reduce normal tissue toxicity while maintaining tumor control, however mechanism(s) remain obscure. To study FLASH mechanisms in brain tissue, we developed a novel experimental platform featuring a unique high-energy electron linear accelerator (High Intensity Gamma Source, HIGS) paired with an organotypic ex vivo brain metastasis model. MethodsWe varied inter-pulse spacing to modulate the mean dose rate (MDR) of our unique 35 MeV electron beam, while maintaining extremely high instantaneous dose rate (IDR), and used film dosimetry to characterize dosimetry and targeting accuracy. We combined this HIGS-FLASH beam with an organotypic rat brain slice/breast carcinoma co-culture model of brain metastasis to assess effects on normal and neoplastic tissues. Live cell and bioluminescence imaging demonstrated cancer cell growth effects, while normal tissue responses and immune activation were assessed with live cell imaging, cytokine profiles, and confocal microscopy. We performed comparison experiments with 20 MeV electrons from a Varian clinical linear accelerator (VCLA) operating at conventional dose rate. ResultsThe highest IDR of the HIGS-FLASH beam to-date was 20.7 {+/-} 0.6 MGy/s, with maximum MDR of 20.7 MGy/s (1 s pulse of 20.7 Gy). Beam targeting was accurate to < 1 mm and reproducible. HIGS-FLASH and VCLA dose rates equivalently decreased cancer cell growth. HIGS-FLASH irradiation significantly increased TNF and fractalkine levels and confocal microscopy revealed distinct changes in microglial morphology in normal brain slices, suggesting microglia activation following HIGS-FLASH irradiation. ConclusionsOur novel experimental platform produces extremely high dose rates and rapid normal/neoplastic tissue readouts for mechanistic research into the effects of FLASH radiation on the brain. HIGS-FLASH irradiation induces comparable cancer cell growth inhibition but differential effects on cytokines and microglial morphology, suggesting that acute innate immune responses may be involved in FLASH normal tissue effects in the brain.

cancer biology↗

bioRxiv2024

Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation

We present a method for detecting evidence of natural selection in ancient DNA time-series data that leverages an opportunity not utilized in previous scans: testing for a consistent trend in allele frequency change over time. By applying this to 8433 West Eurasians who lived over the past 14000 years and 6510 contemporary people, we find an order of magnitude more genome-wide significant signals than previous studies: 347 independent loci with >99% probability of selection. Previous work showed that classic hard sweeps driving advantageous mutations to fixation have been rare over the broad span of human evolution, but in the last ten millennia, many hundreds of alleles have been affected by strong directional selection. Discoveries include an increase from [~]0% to [~]20% in 4000 years for the major risk factor for celiac disease at HLA-DQB1; a rise from [~]0% to [~]8% in 6000 years of blood type B; and fluctuating selection at the TYK2 tuberculosis risk allele rising from [~]2% to [~]9% from [~]5500 to [~]3000 years ago before dropping to [~]3%. We identify instances of coordinated selection on alleles affecting the same trait, with the polygenic score today predictive of body fat percentage decreasing by around a standard deviation over ten millennia, consistent with the "Thrifty Gene" hypothesis that a genetic predisposition to store energy during food scarcity became disadvantageous after farming. We also identify selection for combinations of alleles that are today associated with lighter skin color, lower risk for schizophrenia and bipolar disease, slower health decline, and increased measures related to cognitive performance (scores on intelligence tests, household income, and years of schooling). These traits are measured in modern industrialized societies, so what phenotypes were adaptive in the past is unclear. We estimate selection coefficients at 9.9 million variants, enabling study of how Darwinian forces couple to allelic effects and shape the genetic architecture of complex traits.

evolutionary biology↗

bioRxiv2024

Lateral cell polarization drives organization of epithelia in sea anemone embryos and embryonic cell aggregates

One of the first organizing processes during animal development is the assembly of embryonic cells into epithelia. In certain animals, including Hydra and sea anemones, epithelia also emerge when cells from dissociated tissues are aggregated back together. Although cell adhesion is required to keep cells together, it is not clear whether cell polarization plays a role as epithelia emerge from disordered aggregates. Here, we demonstrate that lateral cell polarization is essential for epithelial organization in both embryos and aggregates of the sea anemone Nematostella vectensis. Specifically, knock down of the lateral polarity protein Lgl disrupts epithelia in developing embryos and impairs the capacity of dissociated cells to epithelialize from aggregates. Cells in lgl mutant epithelia lose their columnar shape and have mispositioned mitotic spindles and ciliary basal bodies. Together, our data suggest that in Nematostella, Lgl is required to establish lateral cell polarity and position cytoskeletal organelles in cells of embryos and aggregates during de novo epithelial organization.

developmental biology↗

bioRxiv2023

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

genomics↗

bioRxiv2022

Modeling tissue co-regulation to estimate tissue-specific contributions to disease

Integrative analyses of genome-wide association studies (GWAS) and gene expression data across diverse tissues and cell types have enabled the identification of putative disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues makes it difficult to distinguish biologically causal tissues from tagging tissues. While previous work emphasized the potential of accounting for tissue co-regulation, tissue-specific disease effects have not previously been formally modeled. Here, we introduce a new method, tissue co-regulation score regression (TCSC), that disentangles causal tissues from tagging tissues and partitions disease heritability (or covariance) into tissue-specific components. TCSC leverages gene-disease association statistics across tissues from transcriptome-wide association studies (TWAS), which implicate both causal and tagging genes and tissues. TCSC regresses TWAS chi-square statistics (or products of z-scores) on tissue co-regulation scores reflecting correlations of predicted gene expression across genes and tissues. In simulations, TCSC distinguishes causal tissues from tagging tissues while controlling type I error. We applied TCSC to GWAS summary statistics for 78 diseases and complex traits (average N = 302K) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at 5% FDR, including well-established findings, biologically plausible novel findings (e.g. aorta artery and glaucoma), and increased specificity of known tissue-trait associations (e.g. subcutaneous adipose, but not visceral adipose, and HDL). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. For the positive genetic covariance between BMI and red blood cell count, brain substantia nigra contributed positive covariance while pancreas contributed negative covariance; this suggests that genetic covariance may reflect distinct tissue-specific contributions. Overall, TCSC is a precise method for distinguishing causal tissues from tagging tissues, improving our understanding of disease and complex trait biology.

genetics↗

bioRxiv2021

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution without the need for annotation of individual cells to cell types; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =346K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

bioinformatics↗

bioRxiv2021

Mutationathon: towards standardization in estimates of pedigree-based germline mutation rates

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a "Mutationathon", a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

evolutionary biology↗

bioRxiv2021

Leveraging gene co-regulation to identify gene sets enriched for disease heritability

Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWAS) can be used to detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a new method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by the predicted expression of causal disease genes in the gene set. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; GCSC determines that a gene set is enriched for disease heritability if genes with high co-regulation to the gene set have higher TWAS chi-square statistics than genes with low co-regulation to the gene set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well-calibrated, and well-powered to identify gene sets that are enriched for disease heritability explained by predicted expression. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits (average N =344K), analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched gene sets, recapitulating known biology. For Alzheimers disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify gene sets associated to disease.

genetics↗

bioRxiv2021

Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and early work on integrating GWAS with scRNA-seq has shown promise, but work on integrating GWAS with scATAC-seq has been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases and traits (average N =298K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (resp. adult) brain cell types for 22 (resp. 23) of 28 traits using scATAC-seq data, and for 8 (resp. 17) of 28 traits using scRNA-seq data. Notable findings using scATAC-seq data included highly significant enrichments of fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases and traits, and inform future analyses of other diseases/traits.

genetics↗

bioRxiv2021

Incorporating family history of disease improves polygenic risk scores in diverse populations

Polygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

genetics↗

bioRxiv2021

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). Cell type, disease progression, and cellular process programs captured distinct heritability signals even within the same cell type, as we show in multiple complex diseases that affect the brain (Alzheimers disease, multiple sclerosis), colon (ulcerative colitis) and lung (asthma, idiopathic pulmonary fibrosis, severe COVID-19). The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease progression M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease progression immune cell type programs were associated, whereas for epithelial cells, disease progression programs were most prominent, perhaps suggesting a role in disease progression over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

genomics↗

bioRxiv2020

Genome-wide maps of enhancer regulation connect risk variants to disease genes

Genome-wide association studies have now identified tens of thousands of noncoding loci associated with human diseases and complex traits, each of which could reveal insights into biological mechanisms of disease. Many of the underlying causal variants are thought to affect enhancers, but we have lacked genome-wide maps of enhancer-gene regulation to interpret such variants. We previously developed the Activity-by-Contact (ABC) Model to predict enhancer-gene connections and demonstrated that it can accurately predict the results of CRISPR perturbations across several cell types. Here, we apply this ABC Model to create enhancer-gene maps in 131 cell types and tissues, and use these maps to interpret the functions of fine-mapped GWAS variants. For inflammatory bowel disease (IBD), causal variants are >20-fold enriched in enhancers in particular cell types, and ABC outperforms other regulatory methods at connecting noncoding variants to target genes. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes via variants in enhancers that act in different cell types. Guided by these variant-to-function maps, we show that an enhancer containing an IBD risk variant regulates the expression of PPIF to tune mitochondrial membrane potential. Together, our study reveals insights into principles of genome regulation, illuminates mechanisms that influence IBD, and demonstrates a generalizable strategy to connect common disease risk variants to their molecular and cellular functions.

genetics↗

bioRxiv2020

Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS

Psychiatric disorders are highly genetically correlated, and many studies have focused on their shared genetic components. However, little research has been conducted on the genetic differences between psychiatric disorders, because case-case comparisons of allele frequencies among cases currently require individual-level data from cases of both disorders. We developed a new method (CC-GWAS) to test for differences in allele frequency among cases of two different disorders using summary statistics from the respective case-control GWAS; CC-GWAS relies on analytical assessments of the genetic distance between cases and controls of each disorder. Simulations and analytical computations confirm that CC-GWAS is well-powered and attains effective control of type I error. In particular, CC-GWAS identifies and discards false positive associations that can arise due to differential tagging of a shared causal SNP (with the same allele frequency in cases of both disorders), e.g. due to subtle differences in ancestry between the input case-control studies. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder and major depressive disorder, and identified 116 independent genome-wide significant loci distinguishing these three disorders, including 21 CC-GWAS-specific loci that were not genome-wide significant in the input case-control summary statistics. Two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 from the Kruppel-like family of transcription factors; these genes have been linked to neurite outgrowth and axon regeneration. We performed a broader set of case-case comparisons by additionally analyzing ADHD, anorexia nervosa, autism, obsessive-compulsive disorder and Tourettes Syndrome, yielding a total of 196 independent loci distinguishing eight psychiatric disorders, including 72 CC-GWAS-specific loci. We confirmed that loci identified by CC-GWAS replicated convincingly in applications to data sets for which independent replication data were available. In conclusion, CC-GWAS robustly identifies loci with different allele frequencies among cases of different disorders using results from the respective case-control GWAS, providing new insights into the genetic differences between eight psychiatric disorders.

genetics↗

bioRxiv2020

In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores

Poor trans-ethnic portability of polygenic risk score (PRS) models is a critical issue that may be partially due to limited knowledge of causal variants shared among populations. Hence, leveraging noncoding regulatory annotations that capture genetic variation across populations has the potential to enhance the trans-ethnic portability of PRS. To this end, we constructed a unique resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 public epigenetic datasets to predict binding patterns of 142 cell-type-regulating transcription factors across 245 cell types. With this resource, we partitioned the common SNP heritability of diverse polygenic traits and diseases from 111 GWAS summary statistics of European (EUR, average N=180K) and East Asian (EAS, average N=157K) origin. For 95 traits, we were able to identify a single IMPACT annotation most strongly enriched for trait heritability. Across traits, these annotations captured an average of 43.3% of heritability (se = 13.8%) with the top 5% of SNPs. Strikingly, we observed highly concordant polygenic trait regulation between populations: the same regulatory annotations captured statistically indistinguishable SNP heritability (fitted slope = 0.98, se = 0.04). Since IMPACT annotations capture both large and consistent proportions of heritability across populations, prioritizing variants in IMPACT regulatory elements may improve the trans-ethnic portability of PRS. Indeed, we observed that EUR PRS models more accurately predicted 21 tested phenotypes of EAS individuals when variants were prioritized by key IMPACT tracks (49.9% mean relative increase in R2). Notably, the improvement afforded by IMPACT was greater in the trans-ethnic EUR-to-EAS PRS application than in the EAS-to-EAS application (47.3% vs 20.9%, P < 1.7e-4). Overall, our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ethnic portability of genetic data, and this has important implications for future risk prediction models that work across populations.

genetics↗

bioRxiv2020

Improving the informativeness of Mendelian disease pathogenicity scores for common disease

Despite considerable progress on pathogenicity scores prioritizing both coding and noncoding variants for Mendelian disease, little is known about the utility of these pathogenicity scores for common disease. Here, we sought to assess the informativeness of Mendelian diseasederived pathogenicity scores for common disease, and to improve upon existing scores. We first applied stratified LD score regression to assess the informativeness of annotations defined by top variants from published Mendelian disease-derived pathogenicity scores across 41 independent common diseases and complex traits (average N = 320K). Several of the resulting annotations were informative for common disease, even after conditioning on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model. We then improved upon the published pathogenicity scores by developing AnnotBoost, a gradient boosting-based framework to impute and denoise pathogenicity scores using functional annotations from the baseline-LD model. AnnotBoost substantially increased the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying pervasive variant-level overlap between Mendelian disease and common disease. The boosted scores also produced significant improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores have high potential to improve candidate gene discovery and fine-mapping for common disease.

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

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Many diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82x (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

genetics↗