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Genome-Wide Association Study for Feed Efficiency and Growth Traits in U.S. Beef Cattle Christopher M

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Seabury et al. BMC Genomics (2017) 18:386 DOI 10.1186/s12864-017-3754-y RESEARCH ARTICLE Open Access Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle Christopher M. Seabury1*, David L. Oldeschulte1, Mahdi Saatchi2, Jonathan E. Beever3, Jared E. Decker4,5, Yvette A. Halley1, Eric K. Bhattarai1, Maral Molaei1, Harvey C. Freetly6, Stephanie L. Hansen2, Helen Yampara-Iquise4, Kristen A. Johnson7, Monty S. Kerley4, JaeWoo Kim4, Daniel D. Loy2, Elisa Marques8, Holly L. Neibergs7, Robert D. Schnabel4,5, Daniel W. Shike3, Matthew L. Spangler9, Robert L. Weaber10, Dorian J. Garrick2,11 and Jeremy F. Taylor4* Abstract Background: Single nucleotide polymorphism (SNP) arrays for domestic cattle have catalyzed the identification of genetic markers associated with complex traits for inclusion in modern breeding and selection programs. Using actual and imputed Illumina 778K genotypes for 3887 U.S. beef cattle from 3 populations (Angus, Hereford, SimAngus), we performed genome-wide association analyses for feed efficiency and growth traits including average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), and residual feed intake (RFI), with marker-based heritability estimates produced for all traits and populations. Results: Moderate and/or large-effect QTL were detected for all traits in all populations, as jointly defined by the estimated proportion of variance explained (PVE) by marker effects (PVE ≥ 1.0%) and a nominal P-value threshold (P ≤ 5e-05). Lead SNPs with PVE ≥ 2.0% were considered putative evidence of large-effect QTL (n = 52), whereas those with PVE ≥ 1.0% but < 2.0% were considered putative evidence for moderate-effect QTL (n = 35). Identical or proximal lead SNPs associated with ADG, DMI, MMWT, and RFI collectively supported the potential for either pleiotropic QTL, or independent but proximal causal mutations for multiple traits within and between the analyzed populations. Marker-based heritability estimates for all investigated traits ranged from 0.18 to 0.60 using 778K genotypes, or from 0.17 to 0.57 using 50K genotypes (reduced from Illumina 778K HD to Illumina Bovine SNP50). An investigation to determine if QTL detected by 778K analysis could also be detected using 50K genotypes produced variable results, suggesting that 50K analyses were generally insufficient for QTL detection in these populations, and that relevant breeding or selection programs should be based on higher density analyses (imputed or directly ascertained). Conclusions: Fourteen moderate to large-effect QTL regions which ranged from being physically proximal (lead SNPs ≤ 3Mb) to fully overlapping for RFI, DMI, ADG, and MMWT were detected within and between populations, and included evidence for pleiotropy, proximal but independent causal mutations, and multi-breed QTL. Bovine positional candidate genes for these traits were functionally conserved across vertebrate species. Keywords: GWAS, QTL, Feed efficiency and growth, Beef Cattle * Correspondence: [email protected]; [email protected] 1Department of Veterinary Pathobiology, Texas A&M University, College Station 77843, USA 4Division of Animal Sciences, University of Missouri, Columbia 65211, USA Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Seabury et al. BMC Genomics (2017) 18:386 Page 2 of 25 Background (DMI; lb/d), average daily gain on feed (ADG; lb/d), and Feed efficiency analyses for beef cattle and other import- mid-test metabolic body weight (MMWT; lb0.75)inU.S. ant food animals generally seek to relate measures of Angus, Hereford, and SimAngus (Simmental × Angus) feed intake with measures of animal productivity (i.e., beef cattle [13], with corresponding heritability estimates agricultural input versus output). Relevant to beef cattle produced for all investigated traits. Thereafter, we investi- production, and particularly among feedlot operations, gated whether QTL discovered using the Illumina the most costly component of the production process is BovineHD markers (hereafter 778K) were also detectable feed, with feed-based expenditures during certain stages using the Illumina BovineSNP50 Assay (hereafter 50K), of production representing more than 80% of the total which is important for modern genomic selection pro- costs [1]. The overall economic importance of feed effi- grams predicated on historic 50K analyses, and also ciency in beef cattle was initially recognized more than compared the marker-based heritability estimates pro- 40 years ago [2, 3], with modern improvements in the duced by the two arrays. Finally, we evaluated our 778K efficiency of feed utilization expected to yield positive results within the context of the established GWAS lit- economic returns across many facets of the beef industry erature, and found some positional candidate genes [1, 4]. Moreover, an estimated annual cost savings ex- underlying bovine feed efficiency and/or growth related ceeding $1 billion U.S. dollars could likely be achieved QTL to be conserved across divergent food-animal spe- by increasing the efficiency of feed utilization by 10% in cies, with a tangible proportion of our bovine GWAS U.S. beef cattle alone [5]. results also overlapping with specific loci implicated in Traditional measures of feed efficiency, such as the studies of obesity or metabolic syndrome, diabetes, and ratio of feed consumed to observed body weight gain insulin resistance among humans and/or mice. The re- (i.e., feed conversion ratio), are likely to be very success- sults of this study are immediately useful for enabling ful in selecting for increased growth rates in beef cattle genomic selection in beef cattle, but also suggest that (i.e., increased producer income), but selection for en- domestic cattle may be relevant models for some hu- hanced weaning or yearling weights may also lead to in- man biomedical studies. creased costs associated with maintaining larger mature cows (i.e., increased nutrient requirements and feed costs; Results and discussion increased calving difficulty due to larger birth weights, Heritability estimates for RFI, DMI, ADG, and MMWT etc) [4, 6, 7]. An alternative measure that has gained in Using a genomic relationship matrix [23] that was nor- popularity among livestock species is Residual Feed Intake malized using Gower’s centering approach [24, 25], to (RFI), with this trait often defined as the difference be- yield a sampling variance of 1.0, we estimated the pseudo- 2 2 2 2 tween an animal’s observed and expected feed intake in heritability [24, 25] [(i. e., ha = σa/(σa + σe); also repre- 2 relation to the animal’s body weight and growth rate dur- sented as: h =VA/(VA +VE)] for all investigated feed ing a specified feeding period (for review see [2, 4, 8–14]). efficiency and growth traits (RFI, DMI, ADG and The primary advantages of using RFI to estimate or meas- MMWT) in U.S. Angus, Hereford, and SimAngus cattle, ure feed efficiency include its phenotypic independence thereby representing a major segment of the commercial from daily gain as well as the traits used to calculate RFI, U.S. beef industry (Table 1). The pseudo-heritability, as with previous heritability estimates among cattle popula- previously defined [24, 25], is the proportion of pheno- tions ranging from 0.08 to 0.49 [8–10, 12, 13], thereby typic variance that is explained by the marker-based gen- making RFI a preferred measure for dissecting the under- omic relationship matrix [23]. We used both the 50K and lying biology related to feed efficiency, and for enabling the 778K marker sets to construct relationship matrices genomic selection [13–15]. Given the economic im- [23]. Pseudo-heritability estimates obtained using the portance of genomic selection for feed efficiency and 778K markers ranged from 0.18 to 0.60 across the investi- growth traits in beef cattle, genome-wide association gated traits for the three beef cattle populations, with indi- studies (GWAS) and/or linkage studies have now been vidual ranges of: RFI = 0.20–0.49; DMI = 0.18–0.46; ADG performed (for review see [11, 16–20]), with the advent =0.21–0.37; MMWT = 0.47–0.60 (Table 1). These esti- oftheIlluminaBovineSNP50Assay[21],andthere- mates are similar to those previously reported [13, 16, 17, after,theIlluminaBovineHDAssay[22]directlyenab- 26], and are strongly correlated (Angus, all traits, r =0.81; ling most of these studies. However, a need remains to Hereford, all traits, r = 0.99; SimAngus, all traits, r =0.77) thoroughly investigate quantitative trait loci (QTL) with previously obtained heritability estimates for the associated with RFI as well as other feed intake and same populations [13]. An in silico reduction of the growth traits among U.S. beef cattle populations. There- Illumina marker density from 778K to 50K (see Methods) fore, we used a single marker approach with relationship for all populations yielded similar pseudo-heritability esti- matrix and variance component analysis to map QTL as- mates across all traits (r > 0.99, all traits; see Table 2), sociated with feedlot RFI, average daily dry matter intake suggesting that either SNP platform was suitable for Seabury et al. BMC Genomics (2017) 18:386 Page 3 of 25 2 2 2 2 Table 1 Variance component analysis with pseudo-heritability estimates (ha = σa/(σa + σe); see references [24, 25]) representing the proportion of phenotypic variance explained by the 778K marker-based relationship matrix [23] for feed efficiency and component traits in U.S.
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