X
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
More filters
Journal ArticleDOI
Population Differences in Transcript-Regulator Expression Quantitative Trait Loci
Pierre R. Bushel,Ray McGovern,Liwen Liu,Oliver Hofmann,Ahsan Huda,Ahsan Huda,Jun Lu,Jun Lu,Winston Hide,Xihong Lin +9 more
TL;DR: The structure of the biological process subtree and a gene interaction network of the TReQTL revealed that tumor necrosis factor, NF-kappaB and variants in G-protein coupled receptors signaling may play a central role as communicators in Foxp3 functional regulation.
Journal ArticleDOI
Multivariate Gene Selection and Testing in Studying the Exposure Effects on a Gene Set
TL;DR: Two computationally simple Canonical Correlation Analysis (CCA) based variable selection methods are proposed, to jointly select a subset of genes in a gene set that are associated with exposures that allow for better understanding of the underlying biological mechanism and for pursuing further biological investigation of these genes.
Journal ArticleDOI
A distinct and replicable variant of the squamous cell carcinoma gene inositol polyphosphate-5-phosphatase modifies the susceptibility of arsenic-associated skin lesions in Bangladesh.
Wei Jie Seow,Wen Chi Pan,Molly L. Kile,Lin Tong,Andrea A. Baccarelli,Quazi Quamruzzaman,Mahmuder Rahman,Golam Mostofa,Muhammad Rakibuz-Zaman,Muhammad G. Kibriya,Habibul Ahsan,Xihong Lin,David C. Christiani +12 more
TL;DR: In this article, single-nucleotide polymorphisms (SNPs) in inflammation, one-carbon metabolism, and skin cancer genes might influence susceptibility to arsenic-induced skin lesions.
Journal ArticleDOI
Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.
Ryan Sun,Miao Xu,Miao Xu,Xihao Li,Sheila M. Gaynor,Hufeng Zhou,Zilin Li,Yohan Bossé,Stephen Lam,Ming-Sound Tsao,Adonina Tardón,Chu Chen,Jennifer A. Doherty,Jennifer A. Doherty,Gary E. Goodman,Stig E. Bojesen,Stig E. Bojesen,Maria Teresa Landi,Mattias Johansson,John K. Field,Heike Bickeböller,H-Erich Wichmann,H-Erich Wichmann,Angela Risch,Angela Risch,Gadi Rennert,Suzanne Arnold,Xifeng Wu,Olle Melander,Hans Brunnström,Loic Le Marchand,Geoffrey Liu,Angeline S. Andrew,Eric J. Duell,Lambertus A. Kiemeney,Hongbing Shen,Aage Haugen,Mikael Johansson,Kjell Grankvist,Neil E. Caporaso,Penella J. Woll,M. Dawn Teare,Ghislaine Scelo,Yun-Chul Hong,Jian-Min Yuan,Philip Lazarus,Matthew B. Schabath,Melinda C. Aldrich,Demetrios Albanes,Raymond H. Mak,David A. Barbie,Paul Brennan,Rayjean J. Hung,Christopher I. Amos,David C. Christiani,Xihong Lin +55 more
TL;DR: This work model and integrate extensive multiomics data sources, utilizing a total of 40 genome‐wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses.
Posted Content
Hypothesis Testing for Sparse Binary Regression
TL;DR: In this article, the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models was studied and a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression was observed.