Xihao Li

Dr. Xihao Li

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 August 2020 in Nature Genetics
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Journal Article
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Xihao LiXihao Li
Xihao Li
Zilin LiZilin Li
Zilin Li
HZHufeng Zhou
Hufeng Zhou
SGSheila M. Gaynor
Sheila M. Gaynor
YLYaowu Liu
Yaowu Liu
XLXihong Lin
Xihong Lin

9 November 2022 in Nucleic Acids Research
Verified
Journal Article
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HZHufeng Zhou
Hufeng Zhou
TATheodore Arapoglou
Theodore Arapoglou
Xihao LiXihao Li
Xihao Li
Zilin LiZilin Li
Zilin Li
XZXiuwen Zheng
Xiuwen Zheng
XLXihong Lin
Xihong Lin
Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

2 March 2021 in Cancer Research
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XWXinan Wang
Xinan Wang
BRBiagio Ricciuti
Biagio Ricciuti
TNTom Nguyen
Tom Nguyen
Xihao LiXihao Li
Xihao Li
MRMichael S. Rabin
Michael S. Rabin
DCDavid C. Christiani
David C. Christiani
Lung carcinogenesis is a complex and stepwise process involving accumulation of genetic mutations in signaling and oncogenic pathways via interactions with environmental factors and host susceptibility. Tobacco exposure is the leading cause of lung cancer, but its relationship to clinically relevant mutations and the composite tumor mutation burden (TMB) has not been fully elucidated. In this study, we investigated the dose–response relationship in a retrospective observational study of 931 patients treated for advanced-stage non–small cell lung cancer (NSCLC) between April 2013 and February 2020 at the Dana Farber Cancer Institute and Brigham and Women’s Hospital. Doubling smoking pack-years was associated with increased KRASG12C and less frequent EGFRdel19 and EGFRL858R mutations, whereas doubling smoking-free months was associated with more frequent EGFRL858R. In advanced lung adenocarcinoma, doubling smoking pack-years was associated with an increase in TMB, whereas doubling smoking-free months was associated with a decrease in TMB, after controlling for age, gender, and stage. There is a significant dose–response association of smoking history with genetic alterations in cancer-related pathways and TMB in advanced lung adenocarcinoma. Significance: This study clarifies the relationship between smoking history and clinically relevant mutations in non–small cell lung cancer, revealing the potential of smoking history as a surrogate for tumor mutation burden.

27 October 2022 in Nature Methods
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Closed access
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Zilin LiZilin Li
Zilin Li
Xihao LiXihao Li
Xihao Li
HZHufeng Zhou
Hufeng Zhou
SGSheila M. Gaynor
Sheila M. Gaynor
MSMargaret Sunitha Selvaraj
Margaret Sunitha Selvaraj
XLXihong Lin
Xihong Lin

1 August 2022 in Nature Genetics
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JBJinyoung Byun
Jinyoung Byun
YHYounghun Han
Younghun Han
YLYafang Li
Yafang Li
JXJun Xia
Jun Xia
ELErping Long
Erping Long
CAChristopher I. Amos
Christopher I. Amos

11 October 2022 in Nature Communications
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MSMargaret Sunitha Selvaraj
Margaret Sunitha Selvaraj
Xihao LiXihao Li
Xihao Li
Zilin LiZilin Li
Zilin Li
APAkhil Pampana Pampana
Akhil Pampana Pampana
DZDavid Y. Zhang
David Y. Zhang
PNPradeep Natarajan
Pradeep Natarajan
Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.