Haplotype phasing

We develop computational tools to solve statistical and algorithmic challenges in quantitative genetics.

We are based in the Division of Genetics and Center for Data Sciences at Brigham and Women's Hospital / Harvard Medical School. We are affiliated with the Program in Medical and Population Genetics at the Broad Institute.

Our work is generously supported by an NIH Director's New Innovator Award, a Burroughs Wellcome Fund Career Award at the Scientific Interfaces, a Glenn Foundation for Medical Research and AFAR Grant for Junior Faculty, a Broad Institute Next Generation Fund award, and startup funding from the Brigham and Women's Hospital Divisions of Genetics and Cardiovascular Medicine.

Latest News

UK Biobank clonal hematopoiesis paper published in Nature

July 11, 2018
Our work on mosaic chromosomal alterations in the UK Biobank N=150K interim release has been published in Nature! This study used long-range haplotype phasing information to detect mosaicism in blood at very low clonal fractions (down to ~1%), producing an atlas of 8,342 mosaic events. The statistical power of this data set revealed several rare inherited variants that strongly influence clonal expansions involving nearby chromosomal alterations and also refined the link between mosaicism and future blood cancers. [... Read more about UK Biobank clonal hematopoiesis paper published in Nature

Recent Publications

Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations

Loh P-R, Genovese G, Handsaker RE, Finucane HK, A Reshef Y, Palamara PF, Birmann BM, Talkowski ME, Bakhoum SF, McCarroll SA, Price AL. Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. Nature 2018;559(7714):350-355.Abstract
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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Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits

Hormozdiari F, Gazal S, van de Geijn B, Finucane HK, Ju CJ-T, Loh P-R, Schoech A, Reshef Y, Liu X, O'Connor L, Gusev A, Eskin E, Price AL. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 2018;50(7):1041-1047.Abstract
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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A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases

Zhu Z, Lee PH, Chaffin MD, Chung W, Loh P-R, Lu Q, Christiani DC, Liang L. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet 2018;50(6):857-864.Abstract
Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (r = 0.75, P = 6.84 × 10). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.
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Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

Finucane HK, A Reshef Y, Anttila V, Slowikowski K, Gusev A, Byrnes A, Gazal S, Loh P-R, Lareau C, Shoresh N, Genovese G, Saunders A, Macosko E, Pollack S, Pollack S, Perry JRB, Buenrostro JD, Bernstein BE, Raychaudhuri S, McCarroll S, Neale BM, Price AL. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet 2018;50(4):621-629.Abstract
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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Heritability of Atrial Fibrillation

Weng L-C, Choi SH, Klarin D, Smith GJ, Loh P-R, Chaffin M, Roselli C, Hulme OL, Lunetta KL, Dupuis J, Benjamin EJ, Newton-Cheh C, Kathiresan S, Ellinor PT, Lubitz SA. Heritability of Atrial Fibrillation. Circ Cardiovasc Genet 2017;10(6)Abstract
BACKGROUND: Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified. METHODS AND RESULTS: We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. CONCLUSIONS: Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.
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