Biology subjects

Chai, X.

Publications and source records attributed to Chai, X..

21 records · Page 2Linked to original sources

bioRxiv2019

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWAS in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. The major limitation of these methods is that they cannot be used to elucidate the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make use of widely available GWAS summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and false positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWAS data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.

bioinformatics↗

bioRxiv2019

Frequency of mutations in 21 hereditary breast and ovarian cancer susceptibility genes among high-risk Chinese individuals

PurposeTo determine the prevalence and clinical prediction factors associated with deleterious mutations among 882 high-risk Chinese individuals who underwent multigene panel testing for hereditary breast and ovarian cancer (HBOC) risk assessment.\n\nExperimental DesignSubjects were selected from individuals referred for genetic testing using a 21-gene panel (Oseq-BRCA) between January 2015 and March 2018. The distribution and prevalence of deleterious mutations were analyzed for the full cohort as well as subtypes.\n\nResultsOverall, 176 deleterious mutations were observed in 19.50% (n = 172) individuals. Of these, 26 mutations are not reported in public databases and literatures. In the ovarian cancer only subgroup, 115 deleterious mutations were identified in 429 patients (48.6%). Patients with ovarian cancer with mutations were enriched for a family history of breast or ovarian cancers (p < 0.05). In the breast cancer only subgroup, 31 deleterious mutations were identified in 261 patients. Most mutations occurred in BRCA1 (8; 25.8%) and BRCA2 (11; 35.5%). An additional 12 deleterious mutations (38.7%) were found in 7 other susceptibility genes. An increased frequency of mutation rate (57.9%) was observed in the subgroup of subjects with histories of both breast and ovarian cancer.\n\nConclusions19.50% of individuals carried a deleterious mutation in HBOC susceptibility genes in our cohort. Subgroup of subjects with histories of both breast and ovarian cancer had the highest prevalence of mutations. Our results highlighted the genetic heterogeneity of HBOC and the efficiency of multigene panel in performing risk assessment.

cancer biology↗

bioRxiv2018

Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

Asian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7x, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

genetics↗