Biology subjects

Yang Wu

Publications and source records attributed to Yang Wu.

3 recordsLinked to original sources

bioRxiv2020

Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data and heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate (FPR) in detecting secondary signals in the summary-data-based conditional and joint (COJO) association analysis, especially for imputed rare variants (FPR reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis, and integrative analysis of GWAS and expression quantitative trait locus data.

genetics↗

bioRxiv2016

Recurrently deregulated lncRNAs associated with HCC tumorigenesis and metastasis revealed by genomic, epigenomic and transcriptomic profiling in paired primary tumor and PVTT samples

Hepatocellular carcinoma (HCC) are highly potent to invade the portal venous system and subsequently develop into the portal vein tumor thrombosis (PVTT). PVTT could induce intrahepatic metastasis, which is closely associated with poor prognosis. A comprehensive systematic characterization of long noncoding RNAs (lncRNAs) associated with HCC metastasis has not been reported. Here, we first assayed 60 clinical samples (matched primary tumor, adjacent normal tissue, and PVTT) from 20 HCC patients using total RNA sequencing. We identified and characterized 8,603 novel lncRNAs from 9.6 billion sequenced reads, indicating specific expression of these lncRNAs in our samples. On the other hand, the expression patterns of 3,212 known and novel recurrently deregulated lncRNAs (in >=20% of our patients) were well correlated with clinical data in a TCGA cohort and published liver cancer data. Some lncRNAs (e.g., RP11-166D19.1/MIR100HG) were shown to be useful as putative biomarkers for prognosis and metastasis. Moreover, matched array data from 60 samples showed that copy number variations (CNVs) and alterations in DNA methylation contributed to the observed recurrent deregulation of 716 lncRNAs. Subsequently, using a coding-noncoding co-expression network, we found that many recurrently deregulated lncRNAs were enriched in clusters of genes related to cell adhesion, immune response, and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. The results of our integrative analysis provide a valuable resource regarding functional lncRNAs and novel biomarkers associated with HCC tumorigenesis and metastasis.

Genomics↗

bioRxiv2015

Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle

Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNPs).In this study, we investigate an approach to increase the efficiency of genomic prediction by using genome-wide markers. The approach is a feature selection based on genomic best linear unbiased prediction (GBLUP),which is a statistical method used to predict breeding values using SNPs for selection in animal and plant breeding. The objective of this study is the choice of kinship matrix for genomic best linear unbiased prediction (GBLUP).The G-matrix is using the information of genome-wide dense markers. We compare three kinds of kinships based on different combinations of centring and scaling of marker genotypes. And find a suitable kinship approach that adjusts for the resource population of Chinese Simmental beef cattle. Single nucleotide polymorphism (SNPs) can be used to estimate kinship matrix and individual inbreeding coefficients more accurately. So in our research a genomic relationship matrix was developed for 1059 Chinese Simmental beef cattle using 640000 single nucleotide polymorphisms and breeding values were estimated using phenotypes about Carcass weight and Sirloin weight. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Another aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship in the Chinese Simmental beef cattle.\n\nWe find that the feature selection of GBLUP using Xus and the Astle and Baldings kinships model performed similarly well, and were the best-performing methods in our study. Inbreeding and kinship matrix can be estimated with high accuracy using [≥]12,000s in Chinese Simmental beef cattle.

Genomics↗