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Alkes L. Price
Broad Institute
204Publications
68H-index
43.6kCitations
Publications 205
Newest
#1Farhad Hormozdiari (Broad Institute)H-Index: 24
#2Bryce van de Geijn (Broad Institute)H-Index: 9
Last.Alkes L. Price (Broad Institute)H-Index: 68
view all 11 authors...
Transposable elements (TE) comprise roughly half of the human genome. Though initially derided as junk DNA, they have been widely hypothesized to contribute to the evolution of gene regulation. However, the contribution of TE to the genetic architecture of diseases remains unknown. Here, we analyze data from 41 independent diseases and complex traits to draw three conclusions. First, TE are uniquely informative for disease heritability. Despite overall depletion for heritability (54% of SNPs, 39...
1 CitationsSource
#1Xia Jiang (KI: Karolinska Institutet)H-Index: 6
#2Hilary K. Finucane (Broad Institute)H-Index: 29
Last.Sara Lindstroem (UW: University of Washington)H-Index: 46
view all 333 authors...
Source
#1Xia Jiang (KI: Karolinska Institutet)H-Index: 6
#2Hilary K. Finucane (Broad Institute)H-Index: 29
Last.Sara Lindstroem (UW: University of Washington)H-Index: 46
view all 333 authors...
Source
#1Xia Jiang (KI: Karolinska Institutet)H-Index: 6
#2Hilary K. Finucane (Broad Institute)H-Index: 29
Last.Sara Lindstroem (UW: University of Washington)H-Index: 46
view all 333 authors...
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 cor...
9 CitationsSource
#1Armin Schoech (Broad Institute)H-Index: 10
#2Daniel M. Jordan (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 13
Last.Alkes L. Price (Broad Institute)H-Index: 68
view all 10 authors...
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)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estima...
13 CitationsSource
#1Omer Weissbrod (Harvard University)H-Index: 13
#2Farhad Hormozdiari (Harvard University)H-Index: 24
Last.Alkes L. Price (Harvard University)H-Index: 68
view all 14 authors...
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 (and/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 bro...
Source
#1Huwenbo Shi (Harvard University)H-Index: 9
#2Steven Gazal (Harvard University)H-Index: 22
Last.Alkes L. Price (Harvard University)H-Index: 68
view all 12 authors...
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 30 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average N=93K for EAS, N=274K for EUR) wit...
1 CitationsSource
#1Kushal K Dey (Harvard University)H-Index: 7
#2Bryce K van de Geijn (Harvard University)
Last.Alkes L. Price (Harvard University)H-Index: 68
view all 6 authors...
Deep learning models have shown great promise in predicting genome-wide regulatory effects from the DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of two types of deep learning annotations: (1) variant-level annotations (based on the reference allele), assessing whether they are more informative for complex disease than the underlying experimental data used to train the predictive models; and (2...
Source
#1Xia JiangH-Index: 6
#2Hilary K. FinucaneH-Index: 29
Last.Sara LindstroemH-Index: 46
view all 333 authors...
textabstractAn amendment to this paper has been published and can be accessed via a link at the top of the paper.
Source
#1Luke O’Connor (Harvard University)H-Index: 7
#2Armin Schoech (Harvard University)H-Index: 10
Last.Alkes L. Price (Broad Institute)H-Index: 68
view all 6 authors...
Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection—purging large-effect mutations in these regions—leaves behind common-variant associations in thousands of less critical regions instead. We refer to...
7 CitationsSource
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