Publications by authors named "Hilary K Finucane"

39 Publications

Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH.

Nat Genet 2021 08 29;53(8):1166-1176. Epub 2021 Jul 29.

The Jackson Laboratory, Bar Harbor, ME, USA.

Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed hybridization chain reaction fluorescence in situ hybridization coupled with flow cytometry (HCR-FlowFISH), a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CRISPR activity screen analysis (CASA), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene and display activating and/or silencing effects. At the cholesterol-level-associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominate causal variants and, importantly, identify their target genes.
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http://dx.doi.org/10.1038/s41588-021-00900-4DOI Listing
August 2021

Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.

Nat Commun 2021 06 7;12(1):3394. Epub 2021 Jun 7.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants' effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.
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http://dx.doi.org/10.1038/s41467-021-23134-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184741PMC
June 2021

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations.

Hum Mol Genet 2021 07;30(16):1521-1534

Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.
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http://dx.doi.org/10.1093/hmg/ddab130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330913PMC
July 2021

Genome-wide enhancer maps link risk variants to disease genes.

Nature 2021 05 7;593(7858):238-243. Epub 2021 Apr 7.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Genome-wide association studies (GWAS) have identified thousands of noncoding loci that are associated with human diseases and complex traits, each of which could reveal insights into the mechanisms of disease. Many of the underlying causal variants may affect enhancers, but we lack accurate maps of enhancers and their target genes to interpret such variants. We recently developed the activity-by-contact (ABC) model to predict which enhancers regulate which genes and validated the model using CRISPR perturbations in several cell types. Here we apply this ABC model to create enhancer-gene maps in 131 human cell types and tissues, and use these maps to interpret the functions of GWAS variants. 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 through variants in enhancers that act in different cell types. In inflammatory bowel disease (IBD), causal variants are enriched in predicted enhancers by more than 20-fold in particular cell types such as dendritic cells, and ABC achieves higher precision than other regulatory methods at connecting noncoding variants to target genes. These variant-to-function maps reveal an enhancer that contains an IBD risk variant and that regulates the expression of PPIF to alter the membrane potential of mitochondria in macrophages. Our study reveals principles of genome regulation, identifies genes that affect IBD and provides a resource and generalizable strategy to connect risk variants of common diseases to their molecular and cellular functions.
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http://dx.doi.org/10.1038/s41586-021-03446-xDOI Listing
May 2021

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power.

Nat Genet 2021 02 18;53(2):195-204. Epub 2021 Jan 18.

Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.
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http://dx.doi.org/10.1038/s41588-020-00766-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867648PMC
February 2021

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

Nat Genet 2020 12 16;52(12):1355-1363. Epub 2020 Nov 16.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. 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.
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http://dx.doi.org/10.1038/s41588-020-00735-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710571PMC
December 2020

Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features.

Nat Commun 2020 03 6;11(1):1237. Epub 2020 Mar 6.

Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we use seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in five disease-relevant immune cell lines, based on a set of features related to regulatory potential. Trait/disease-associated variants are enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 have at least one variant meeting one or both of these criteria, 5 of which are further supported by genetic fine-mapping. Our work provides a comprehensive strategy to characterize genetic variation at important disease-associated loci, and aids in the effort to identify trait causal genetic variants.
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http://dx.doi.org/10.1038/s41467-020-15022-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060350PMC
March 2020

Reconciling S-LDSC and LDAK functional enrichment estimates.

Nat Genet 2019 08;51(8):1202-1204

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

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http://dx.doi.org/10.1038/s41588-019-0464-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006477PMC
August 2019

Author Correction: Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.

Nat Genet 2019 Aug;51(8):1295

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

In the version of the paper initially published, information on competing interests for author Benjamin M. Neale was missing. The 'Competing interests' statement should have included the sentence 'B.M.N. is on the Scientific Advisory Board of Deep Genomics'.
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http://dx.doi.org/10.1038/s41588-019-0468-xDOI Listing
August 2019

Interrogation of human hematopoiesis at single-cell and single-variant resolution.

Nat Genet 2019 04 11;51(4):683-693. Epub 2019 Mar 11.

Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.

Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait-cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations.
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http://dx.doi.org/10.1038/s41588-019-0362-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441389PMC
April 2019

Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection.

Nat Commun 2019 02 15;10(1):790. Epub 2019 Feb 15.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)], where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.
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http://dx.doi.org/10.1038/s41467-019-08424-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377669PMC
February 2019

Shared heritability and functional enrichment across six solid cancers.

Nat Commun 2019 01 25;10(1):431. Epub 2019 Jan 25.

Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r = 0.57, p = 4.6 × 10), breast and ovarian cancer (r = 0.24, p = 7 × 10), breast and lung cancer (r = 0.18, p =1.5 × 10) and breast and colorectal cancer (r = 0.15, p = 1.1 × 10). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.
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http://dx.doi.org/10.1038/s41467-018-08054-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347624PMC
January 2019

Estimating cross-population genetic correlations of causal effect sizes.

Genet Epidemiol 2019 03 25;43(2):180-188. Epub 2018 Nov 25.

Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.

Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross-population correlation of true causal effect sizes ( ρ b ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g , we can use this ratio to estimate ρ b . Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.
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http://dx.doi.org/10.1002/gepi.22173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375794PMC
March 2019

Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.

Nat Genet 2018 11 8;50(11):1600-1607. Epub 2018 Oct 8.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).
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http://dx.doi.org/10.1038/s41588-018-0231-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236676PMC
November 2018

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk.

Nat Genet 2018 10 3;50(10):1483-1493. Epub 2018 Sep 3.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.
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http://dx.doi.org/10.1038/s41588-018-0196-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202062PMC
October 2018

Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations.

Nature 2018 07 11;559(7714):350-355. Epub 2018 Jul 11.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6-9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3-TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5-50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.
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http://dx.doi.org/10.1038/s41586-018-0321-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6054542PMC
July 2018

Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits.

Nat Genet 2018 07 25;50(7):1041-1047. Epub 2018 Jun 25.

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.
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http://dx.doi.org/10.1038/s41588-018-0148-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030458PMC
July 2018

Analysis of shared heritability in common disorders of the brain.

Science 2018 06;360(6395)

Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
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http://dx.doi.org/10.1126/science.aap8757DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097237PMC
June 2018

Analysis of shared heritability in common disorders of the brain.

Science 2018 06;360(6395)

Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
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http://dx.doi.org/10.1126/science.aap8757DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097237PMC
June 2018

Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.

Nat Genet 2018 04 9;50(4):538-548. Epub 2018 Apr 9.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.
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http://dx.doi.org/10.1038/s41588-018-0092-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5942893PMC
April 2018

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.

Nat Genet 2018 04 9;50(4):621-629. Epub 2018 Apr 9.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.
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http://dx.doi.org/10.1038/s41588-018-0081-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896795PMC
April 2018

Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.

Nat Genet 2017 Oct 11;49(10):1421-1427. Epub 2017 Sep 11.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 × 10); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.
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http://dx.doi.org/10.1038/ng.3954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133304PMC
October 2017

Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues.

Am J Hum Genet 2017 Apr 23;100(4):605-616. Epub 2017 Mar 23.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. Electronic address:

Genetic variants that modulate gene expression levels play an important role in the etiology of human diseases and complex traits. Although large-scale eQTL mapping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variants regulate expression remain unclear, particularly for distal eQTLs, which these studies are not well powered to detect. Here, we leveraged all variants (not just those that pass stringent significance thresholds) to analyze the functional architecture of local and distal regulation of gene expression in 15 human tissues by employing an extension of stratified LD-score regression that produces robust results in simulations. The top enriched functional categories in local regulation of peripheral-blood gene expression included coding regions (11.41×), conserved regions (4.67×), and four histone marks (p < 5 × 10 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral-blood gene expression: coding regions (4.47×), conserved regions (4.51×), and two histone marks (p < 3 × 10 for all enrichments). Analyses of the genetic correlation of gene expression across tissues confirmed that local regulation of gene expression is largely shared across tissues but that distal regulation is highly tissue specific. Our results elucidate the functional components of the genetic architecture of local and distal regulation of gene expression.
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http://dx.doi.org/10.1016/j.ajhg.2017.03.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384099PMC
April 2017

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

Bioinformatics 2017 01 22;33(2):272-279. Epub 2016 Sep 22.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Motivation: LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.

Results: In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.

Availability And Implementation: The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw613DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5542030PMC
January 2017

Reference-based phasing using the Haplotype Reference Consortium panel.

Nat Genet 2016 11 3;48(11):1443-1448. Epub 2016 Oct 3.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ∼20× speedup and ∼10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2× the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.
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http://dx.doi.org/10.1038/ng.3679DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5096458PMC
November 2016

Genome-Wide Association Studies Suggest Limited Immune Gene Enrichment in Schizophrenia Compared to 5 Autoimmune Diseases.

Schizophr Bull 2016 09 30;42(5):1176-84. Epub 2016 May 30.

Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lancaster Medical School and Data Science Institute, Lancaster University, Lancaster, UK;

There has been intense debate over the immunological basis of schizophrenia, and the potential utility of adjunct immunotherapies. The major histocompatibility complex is consistently the most powerful region of association in genome-wide association studies (GWASs) of schizophrenia and has been interpreted as strong genetic evidence supporting the immune hypothesis. However, global pathway analyses provide inconsistent evidence of immune involvement in schizophrenia, and it remains unclear whether genetic data support an immune etiology per se. Here we empirically test the hypothesis that variation in immune genes contributes to schizophrenia. We show that there is no enrichment of immune loci outside of the MHC region in the largest genetic study of schizophrenia conducted to date, in contrast to 5 diseases of known immune origin. Among 108 regions of the genome previously associated with schizophrenia, we identify 6 immune candidates (DPP4, HSPD1, EGR1, CLU, ESAM, NFATC3) encoding proteins with alternative, nonimmune roles in the brain. While our findings do not refute evidence that has accumulated in support of the immune hypothesis, they suggest that genetically mediated alterations in immune function may not play a major role in schizophrenia susceptibility. Instead, there may be a role for pleiotropic effects of a small number of immune genes that also regulate brain development and plasticity. Whether immune alterations drive schizophrenia progression is an important question to be addressed by future research, especially in light of the growing interest in applying immunotherapies in schizophrenia.
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http://dx.doi.org/10.1093/schbul/sbw059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988748PMC
September 2016

Leveraging Distant Relatedness to Quantify Human Mutation and Gene-Conversion Rates.

Am J Hum Genet 2015 Dec 12;97(6):775-89. Epub 2015 Nov 12.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene-conversion rates by using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population-size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased sequenced Dutch individuals and inferred a point mutation rate of 1.66 × 10(-8) per base per generation and a rate of 1.26 × 10(-9) for <20 bp indels. By quantifying how estimates varied as a function of allele frequency, we inferred the probability that a site is involved in non-crossover gene conversion as 5.99 × 10(-6). We found that recombination does not have observable mutagenic effects after gene conversion is accounted for and that local gene-conversion rates reflect recombination rates. We detected a strong enrichment of recent deleterious variation among mismatching variants found within IBD regions and observed summary statistics of local sharing of IBD segments to closely match previously proposed metrics of background selection; however, we found no significant effects of selection on our mutation-rate estimates. We detected no evidence of strong variation of mutation rates in a number of genomic annotations obtained from several recent studies. Our analysis suggests that a mutation-rate estimate higher than that reported by recent pedigree-based studies should be adopted in the context of DNA-based demographic reconstruction.
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http://dx.doi.org/10.1016/j.ajhg.2015.10.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4678427PMC
December 2015

Shared genetic aetiology of puberty timing between sexes and with health-related outcomes.

Nat Commun 2015 Nov 9;6:8842. Epub 2015 Nov 9.

MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.

Understanding of the genetic regulation of puberty timing has come largely from studies of rare disorders and population-based studies in women. Here, we report the largest genomic analysis for puberty timing in 55,871 men, based on recalled age at voice breaking. Analysis across all genomic variants reveals strong genetic correlation (0.74, P=2.7 × 10(-70)) between male and female puberty timing. However, some loci show sex-divergent effects, including directionally opposite effects between sexes at the SIM1/MCHR2 locus (Pheterogeneity=1.6 × 10(-12)). We find five novel loci for puberty timing (P<5 × 10(-8)), in addition to nine signals in men that were previously reported in women. Newly implicated genes include two retinoic acid-related receptors, RORB and RXRA, and two genes reportedly disrupted in rare disorders of puberty, LEPR and KAL1. Finally, we identify genetic correlations that indicate shared aetiologies in both sexes between puberty timing and body mass index, fasting insulin levels, lipid levels, type 2 diabetes and cardiovascular disease.
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http://dx.doi.org/10.1038/ncomms9842DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667609PMC
November 2015
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