DOI: 10.5061/dryad.bcc2fqzph
title: Data from: An integrative approach to prioritize candidate causal genes for complex traits in cattle

description: Genome-wide association studies (GWAS) have identified many quantitative
 trait loci (QTL) associated with complex traits, predominantly in
 non-coding regions, posing challenges in pinpointing the causal variants
 and their target genes. Three types of evidence can help identify the gene
 through which QTL act: (1) proximity to the most significant GWAS variant,
 (2) correlation of gene expression with the trait, and (3) the gene’s
 physiological role in the trait. However, there is still uncertainty in
 the success of these methods in identifying the correct genes. Here we
 test the ability of these methods in a comparatively simple series of
 traits associated with the concentration of polar lipids in milk. We
 conducted single-trait GWAS for ~14 million imputed variants and 56
 individual milk polar lipid (PL) phenotypes in 336 cows. A meta-analysis
 of multi-trait GWAS identified 10,063 significant SNPs at FDR ≤ 10% (P ≤
 7.15E-5). Transcriptome data from blood (~12.5K genes, 143 cows) and
 mammary tissue (~12.2K genes, 169 cows) were analysed using the genetic
 score omics regression (GSOR) method. This method links observed gene
 expression to genetically predicted phenotypes and was used to find
 associations between gene expression and 56 PL phenotypes. GSOR identified
 2,186 genes in blood and 1,404 in mammary tissue associated with at least
 one PL phenotype (FDR ≤ 1%). We partitioned the genome into
 non-overlapping windows of 100 Kb to test for overlap between
 GSOR-identified genes and GWAS signals. We found a significant overlap
 between these two datasets, indicating that GSOR significant genes were
 more likely to be located within 100 Kb windows that have GWAS signals
 compared to those without (P = 0.01; odds ratio = 1.47). These windows
 included 70 significant genes expressed in mammary tissue and 95 in blood.
 Compared to all expressed genes in each tissue, these genes were enriched
 for lipid metabolism gene ontology (GO). That is, 7 of the 70 significant
 mammary transcriptome genes (P &lt; 0.01; odds ratio = 3.98) and 5 of
 the 95 significant blood genes (P &lt; 0.10; odds ratio = 2.24) were
 involved in lipid metabolism GO. The candidate causal genes include DGAT1,
 ACSM5, SERINC5, ABHD3, CYP2U1, PIGL, ARV1, SMPD5, and NPC2, with some
 overlap between the two tissues. The overlap between GWAS, GSOR, and GO
 analyses suggests that together these methods can identify genes mediating
 QTL, though their power remains limited, as reflected by modest odds
 ratios. Larger sample sizes would enhance the power of these analyses, but
 issues like linkage disequilibrium would remain.
description: [https://doi.org/10.5061/dryad.bcc2fqzph](https://doi.org/10.5061/dryad.bcc2fqzph) ## Description of the data and file structure This dataset is publicly available upon acceptance of the paper titled: "An integrative approach to prioritize candidate causal genes for complex traits in cattle". Also, a Read_Me.txt file is included in the zipped data file. **Genotype File** The genotype data consists of three PLINK binary files: GenotypesHD.bed, GenotypesHD.bim, and GenotypesHD.fam. These files include high-density (HD) imputed genotypes for 336 cows, based on Run7 of the 1000 Bull Genomes Project. **Phenotype File** The PhenoPLs.csv file contains the phenotypes of 56 milk polar lipid species measured in the 336 cows. This file includes a header row with information about the group each phenotype belongs to. The polar lipid groups represented are: * Phosphatidylcholine (PC) - Phosphatidylethanolamine (PE) * Phosphatidylserine (PS) - Phosphatidylinositol (PI) * Sphingomyelin (SM) - Lactosylceramide (LacCer) * Glucosylceramide (GluCer) Note: Outliers have not been removed from the phenotype data. **Fixed Effects File** The FixedEffectPLs.txt file provides fixed effects data, including the combined effect of year and batch (with 9 levels from B1 to B9) for the 336 cows. The first two columns represent the Family ID (FID) and Individual ID (IID).