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Xihong Lin
Researcher at Harvard University
Publications - 389
Citations - 32083
Xihong Lin is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 76, co-authored 361 publications receiving 26162 citations. Previous affiliations of Xihong Lin include Texas A&M University & University of Washington.
Papers
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Proceedings ArticleDOI
A Large-scale Genotyping Study Identifies Five Genes Associated With ARDS Development
David C. Christiani,Chau-Chyun Sheu,Feng Chen,Li Su,Ednan K. Bajwa,Rihong Zhai,Amy M. Nuernberg,Peter Clardy,Diana Gallagher,Michelle N. Gong,Paula Tejera,Angela J. Frank,Hakon Hakonarson,Xihong Lin,B. T. Thompson +14 more
Journal ArticleDOI
Connectivity in eQTL networks dictates reproducibility and genomic properties
TL;DR: In this article , the authors constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality.
Journal ArticleDOI
Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks
Rounak Dey,Wei Zhou,Tuomo Kiiskinen,Aki S. Havulinna,Amanda F. Elliott,Juha Karjalainen,Mitja I. Kurki,Ashley Qin,Seunggeun Lee,Aarno Palotie,Benjamin M. Neale,Mark J. Daly,Xihong Lin +12 more
TL;DR: In this article , the authors proposed an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness.
Journal ArticleDOI
Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.
TL;DR: It is shown that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes, and a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotype.
Posted ContentDOI
Leveraging a machine learning derived surrogate phenotype to improve power for genome-wide association studies of partially missing phenotypes in population biobanks
TL;DR: SynSurr as mentioned in this paper is an approach that jointly analyzes an incompletely observed target phenotype together with its predicted value from an ML model, referred to its prediction as a synthetic surrogate for the target phenotype.