Farhad Hormozdiari
Harvard University
111 Papers
369 Citations
Farhad Hormozdiari is an academic researcher from Harvard University. The author has contributed to research in topics: Biology & Genome-wide association study. The author has an hindex of 36, co-authored 97 publications. Previous affiliations of Farhad Hormozdiari include Broad Institute & Howard Hughes Medical Institute.
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Papers
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies
Gleb Kichaev,Wen-Yun Yang,S. Lindstrom,Farhad Hormozdiari,Eleazar Eskin,A. Price,Peter Kraft,Bogdan Pasaniuc +7 more
TL;DR: A probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation and introduces a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays.
Intergenerational genomic DNA methylation patterns in mouse hybrid strains.
Luz D. Orozco,Liudmilla Rubbi,Lisa J. Martin,Fang Fang,Farhad Hormozdiari,Nam Che,Andrew D. Smith,Aldons J. Lusis,Matteo Pellegrini +8 more
TL;DR: The majority of DNA methylation differences among individuals are associated with genetic differences, and a much smaller proportion of these epigenetic differences are due to sex, imprinting or stochastic intergenerational effects.
Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits
Yuchen Zhou,Justin Khasentino,Taedong Yun,Mahantesh I. Biradar,J. Shreibati,Dongbing Lai,T.-H. Schwantes-An,Robert Luben,Z. McCaw,Jorgen Engmann,Rui Providência,A. Schmidt,Patricia B. Munroe,Howard Yang,Andrew Carroll,A. Khawaja,Cory Y. McLean,Babak Behsaz,Farhad Hormozdiari +18 more
Identification of causal genes for complex traits
TL;DR: This work proposes CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure, and validate the method using a real mouse high-density lipoprotein data.
Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes.
Yehudit Hasin-Brumshtein,Arshad H. Khan,Farhad Hormozdiari,Calvin Pan,Brian W. Parks,Vladislav A. Petyuk,Paul D. Piehowski,Anneke Brümmer,Matteo Pellegrini,Xinshu Xiao,Eleazar Eskin,Richard D. Smith,Aldons J. Lusis,Desmond J. Smith +13 more
TL;DR: High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes, and thousands of alternative splicing events regulated by genetic variants.