Using SNP Weights Derived From Gene Expression Modules to Improve GWAS Power for Feed Efficiency in Pigs

Abstract

The "large p small n" problem has posed a significant challenge in the analysis and interpretation of genome-wide association studies (GWAS). The use of prior information to rank genomic regions and perform SNP selection could increase the power of GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS. RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, and a three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules. Loading values from each gene module were used to assign weights to 49,691 commercial SNP markers, and SNP were selected using a weight threshold, resulting in 10 SNP sets ranging in size from 101 to 955 markers. Weighted GWAS for feed intake in 4,200 pigs was performed separately for each of the 10 SNP sets. A total of 36 unique significant SNP associations were identified across the ten gene modules (SNP sets). For comparison, a standard unweighted GWAS using all 49,691 SNP was performed, and only 2 SNP were significant. None of the SNP from the unweighted analysis resided in known QTL related to swine feed efficiency (feed intake, average daily gain, and feed conversion ratio) compared to 29 (80.6%) in the weighted analyses, with 9 SNP residing in feed intake QTL. These results suggest that the heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS. Hence, the proposed procedure for prioritizing SNP based on gene expression data across multiple tissues provides a promising approach for improving the power of GWAS.

Resistome Analysis of Global Livestock and Soil Microbiomes

“…The metatranscriptomic studies for both gut microbiomes of swine and poultry were selected from the FAANG database, 1 and the largest available metatranscriptomic datasets from published studies and free from antibiotic treatment were selected. These included the poultry metranscriptomic dataset: PRJEB23255, Germany ( Reyer et al, 2018 ) and the swine datasets: PRJNA529662, PRJNA529214, United States of America ( Keel et al, 2020 ). Similarly, the largest antibiotic free metatranscriptomic soil dataset was selected from the ENA database, 2 PRJNA366008, United States of America (unpublished).…”

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

“…Each sample had 49,576 transcriptomic features, and the overall dataset had 140 samples in total. A classification model may have a large chance of overfitting for this “large p small n ” situation ( Keel et al, 2019 ; Ren et al, 2020 ). A feature selection algorithm may be used to find a subset of features for building an accurate and stable classification model.…”

Integrating Variant Functional Annotation Scores Have Varied Abilities To Improve Power Of Genome-Wide Association Studies

“…To integrate GWAS summary statistics with meta-scores, in addition to meta-analysis and Fisher's method, we also consider the weighted p-value approach [21] and the stratified false discovery rate (sFDR) control method [22], which extended the traditional FDR control methodology [23]. Both weighted p-value and sFDR have been used to leverage linkage evidence [24,25], gene-expression data [26,27] and pleiotropy [28] to increase power of GWAS. Here we use these data-integration methods to integrate CADD or Eigen functional meta-scores with GWAS summary statistics of 1,132 phenotypes from the UK Biobank data [29].…”