Sriram Sankararaman

Affiliations: 
2010 Computer Science University of California, Berkeley, Berkeley, CA, United States 
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"Sriram Sankararaman"
Mean distance: 14.44 (cluster 29)
 
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Michael I. Jordan grad student 2010 UC Berkeley
 (Statistical models for analyzing human genetic variation.)
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Publications

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An U, Pazokitoroudi A, Alvarez M, et al. (2023) Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries. Nature Genetics
Fu B, Pazokitoroudi A, Xue A, et al. (2023) A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis. Biorxiv : the Preprint Server For Biology
Hou K, Ding Y, Xu Z, et al. (2023) Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nature Genetics
Zhang X, Kim B, Singh A, et al. (2023) MaLAdapt reveals novel targets of adaptive introgression from Neanderthals and Denisovans in worldwide human populations. Molecular Biology and Evolution
Zou J, Zhou J, Faller S, et al. (2022) Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity. G3 (Bethesda, Md.)
Chiu AM, Molloy EK, Tan Z, et al. (2022) Inferring population structure in biobank-scale genomic data. American Journal of Human Genetics. 109: 727-737
Pazokitoroudi A, Wu Y, Burch KS, et al. (2020) Efficient variance components analysis across millions of genomes. Nature Communications. 11: 4020
Sankararaman S. (2020) Methods for detecting introgressed archaic sequences. Current Opinion in Genetics & Development. 62: 85-90
Agrawal A, Chiu AM, Le M, et al. (2020) Scalable probabilistic PCA for large-scale genetic variation data. Plos Genetics. 16: e1008773
Durvasula A, Sankararaman S. (2020) Recovering signals of ghost archaic introgression in African populations. Science Advances. 6: eaax5097
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