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Yao Jen Li

Researcher at Academia Sinica

Publications -  13
Citations -  902

Yao Jen Li is an academic researcher from Academia Sinica. The author has contributed to research in topics: Lung cancer & Single-nucleotide polymorphism. The author has an hindex of 11, co-authored 12 publications receiving 765 citations. Previous affiliations of Yao Jen Li include National Taiwan University.

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Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia.

Qing Lan, +133 more
- 01 Dec 2012 - 
TL;DR: It is observed that there is no evidence of association for lung cancer at 15q25 in never-smoking women in Asia, providing strong evidence that this locus is not associated with lung cancer independent of smoking.
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Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types

Joshua N. Sampson, +448 more
TL;DR: Correlation analysis indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the Genetic etiology for the same disease can vary by population and environmental exposures.
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Genetic variants associated with longer telomere length are associated with increased lung cancer risk among never‐smoking women in Asia: a report from the female lung cancer consortium in Asia

Mitchell J. Machiela, +133 more
TL;DR: The results indicate that a genetic background that favors longer telomere length may increase lung cancer risk, which is consistent with earlier prospective studies relating longer telomerre length with increased lung cancerrisk.
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Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations.

Wei Jie Seow, +196 more
TL;DR: The results extend the catalogue of regions associated with lung adenocarcinoma in non-smoking Asian women and highlight the importance of how the germline could inform risk for specific tumour mutation patterns, which could have important translational implications.