Combining information from genome-wide association and multi-tissue gene expression studies to elucidate factors underlying genetic variation for residual feed intake in Australian Angus cattle. 
Genome-wide association studies (GWAS) are extensively used to identify single nucleotide polymorphisms (SNP) underlying the genetic variation of complex traits.  However, much uncertainly often still exists about the causal variants and genes at quantitative trait loci (QTL).  The aim of this study was to identify QTL associated with residual feed intake (RFI) and genes in these regions whose expression is also associated with this trait.  Angus cattle (2190 steers) with RFI records were genotyped and imputed to high density arrays (770 K) and used for a GWAS approach to identify QTL associated with RFI.  RNA sequences from 126 Angus divergently selected for RFI were analyzed to identify the genes whose expression was significantly associated this trait with special attention to those genes residing in the QTL regions.  The heritability for RFI estimated for this Angus population was 0.3.  In a GWAS, we identified 78 SNPs associated with RFI on six QTL (on BTA1, BTA6, BTA14, BTA17, BTA20 and BTA26).  The most significant SNP was found on chromosome BTA20 (rs42662073) and explained 4% of the genetic variance.  The minor allele frequencies of significant SNPs ranged from 0.05 to 0.49.  All regions, except on BTA17, showed a significant dominance effect.  In 1 Mb windows surrounding the six significant QTL, we found 149 genes from which OAS2, STC2, SHOX, XKR4, and SGMS1 were the closest to the most significant QTL on BTA17, BTA20, BTA1, BTA14, and BTA26, respectively.  In a 2 Mb windows around the six significant QTL, we identified 15 genes whose expression was significantly associated with RFI: BTA20) NEURL1B and CPEB4; BTA17) RITA1, CCDC42B, OAS2, RPL6, and ERP29; BTA26) A1CF, SGMS1, PAPSS2, and PTEN; BTA1) MFSD1 and RARRES1; BTA14) ATP6V1H and MRPL15.  Our results showed six QTL regions associated with RFI in a beef Angus population where five of these QTL contained genes that have expression associated with this trait.  Therefore, here we show that integrating information from gene expression and GWAS studies can help to better understand the genetic mechanisms that determine variation in complex traits.