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
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Recent Publications

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

Gazal S, Loh P-R, Finucane HK, Ganna A, Schoech A, Sunyaev S, Price AL. Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet 2018;50(11):1600-1607.Abstract
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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Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef YA, Finucane HK, Kelley DR, Gusev A, Kotliar D, Ulirsch JC, Hormozdiari F, Nasser J, O'Connor L, van de Geijn B, Loh P-R, Grossman SR, Bhatia G, Gazal S, Palamara PF, Pinello L, Patterson N, Adams RP, Price AL. Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 2018;50(10):1483-1493.Abstract
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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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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