Doyle et al. Genet Sel Evol (2020) 52:2 https://doi.org/10.1186/s12711-020-0523-1 Genetics Selection Evolution

RESEARCH ARTICLE Open Access Genomic regions associated with muscularity in beef cattle difer in fve contrasting cattle breeds Jennifer L. Doyle1,2, Donagh P. Berry1* , Roel F. Veerkamp3, Tara R. Carthy1, Ross D. Evans4, Siobhán W. Walsh2 and Deirdre C. Purfeld1

Abstract Background: Linear type traits, which refect the muscular characteristics of an animal, could provide insight into how, in some cases, morphologically very diferent animals can yield the same carcass weight. Such variability may contribute to diferences in the overall value of the carcass since primal cuts vary greatly in price; such variability may also hinder successful genome-based association studies. Therefore, the objective of our study was to identify genomic regions that are associated with fve muscularity linear type traits and to determine if these signifcant regions are common across fve diferent breeds. Analyses were carried out using linear mixed models on imputed whole-genome sequence data in each of the fve breeds, separately. Then, the results of the within-breed analyses were used to conduct an across-breed meta-analysis per trait. Results: We identifed many quantitative trait loci (QTL) that are located across the whole genome and associated with each trait in each breed. The only commonality among the breeds and traits was a large-efect pleiotropic QTL on BTA2 that contained the MSTN gene, which was associated with all traits in the Charolais and Limousin breeds. Other plausible candidate genes were identifed for muscularity traits including PDE1A, PPP1R1C and multiple col- lagen and HOXD genes. In addition, associated (gene ontology) GO terms and KEGG pathways tended to difer between breeds and between traits especially in the numerically smaller populations of Angus, Hereford, and Sim- mental breeds. Most of the SNPs that were associated with any of the traits were intergenic or intronic SNPs located within regulatory regions of the genome. Conclusions: The commonality between the Charolais and Limousin breeds indicates that the genetic architecture of the muscularity traits may be similar in these breeds due to their similar origins. Conversely, there were vast difer- ences in the QTL associated with muscularity in Angus, Hereford, and Simmental. Knowledge of these diferences in genetic architecture between breeds is useful to develop accurate genomic prediction equations that can operate efectively across breeds. Overall, the associated QTL difered according to trait, which suggests that breeding for a morphologically diferent (e.g. longer and wider versus shorter and smaller) more efcient animal may become pos- sible in the future.

Background Linear type traits have been used extensively to charac- terize conformation in both dairy [1–3] and beef cattle *Correspondence: [email protected] [4, 5]. Muscularity linear type traits have previously been 1 Teagasc, Animal and Grassland Research and Innovation Centre, documented as moderate to highly heritable traits in beef Moorepark, Fermoy, Co. Cork, Ireland Full list of author information is available at the end of the article cattle [5–7] and are known to be genetically associated

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with carcass merit [8, 9] and with both animal live weight Animals were discarded from the dataset if the sire, and price [4]. Terefore, the genetic merit of a young dam, herd, or classifer was unknown, or if the par- animal for these traits may be a good representation of ity of the dam was not recorded. Parity of the dam was its merit for carcass traits. While both carcass value and recoded as 1, 2, 3, 4, and ≥ 5. Contemporary group was conformation have been reported to be correlated with defned as herd-by-scoring date generated separately per linear type traits [9], the correlation with any one type breed. Each contemporary group had to have at least fve trait is not equal to 1 which implies that the same car- records. Following these edits, data were available on cass value can be achieved with morphologically diferent 81,200 animals: 3356 AA, 31,049 CH, 3004 HE, 35,159 animals; by extension then, this implies that, for exam- LM and 8632 SI. ple, an animal with a better developed loin and a shallow chest may have the same yield as an animal with a lesser Generation of adjusted phenotypes developed loin and a deep chest. Such morphological dif- Prior to inclusion in the analysis, all phenotypes were frst ferences could contribute, in turn, to diferences in indi- adjusted within-breed in ASREML [17] using the model: vidual carcass retail cut weights, and thus overall carcass y = HSD + Sex + AM + DP + Animal + e, value. Many previous genomic studies in cattle have focused where y is the linear type trait, HSD is the fxed efect of on live weight and carcass traits as the phenotypes of herd by scoring date (11,130 levels), Sex is the fxed efect interest [10–12], but only a few have been published on of the sex of the animal (male or female), AM is the fxed the underlying features that contribute to diferences in efect of the age in months of the animal (11 classes from linear type traits in either beef cattle [13] or dairy cattle 6 to 16 months), DP is the fxed efect of the parity of the [14]. While previous studies have attempted to compare dam (1, 2, 3, 4 and ≥ 5), Animal is the random additive and contrast putative mutations, genes, and associated efect of the animal, and e is the random residual efect. biological pathways across multiple breeds of beef cattle Te adjusted phenotype was the raw phenotype minus for carcass traits [15], no study has attempted to do this the fxed efect solutions of HSD, Sex, AM and DP. using linear type traits. Knowledge of any kind of simi- larities or diferences between breeds could enable the Genotype data introduction of more accurate multi-breed genomic eval- Of the 81,200 animals with linear type trait information, uations for both pure and crossbred animals. Terefore, 19,449 animals from fve beef breeds (1444 AA, 6433 the objective of the present study was to identify genomic CH, 1129 HE, 8745 LM, and 1698 SI) were imputed to regions associated with fve muscularity linear type traits whole-genome sequence as part of a larger dataset of and to determine if these associated regions are common 638,662 multi-breed genotyped animals. All 638,662 ani- across multiple beef cattle breeds. mals were genotyped using the Bovine Illumina SNP50 panel [n = 5808; 54,001single nucleotide polymorphisms (SNPs)], the Illumina High Density (HD) panel (HD; Methods n 5504; 777,972 SNPs), the Illumina 3k panel (n 2256; Phenotypic data = = 2900 SNPs), the Illumina low-density (LD) genotyping As part of the Irish national beef breeding program, rou- panel (n = 15,107; 6909 SNPs) or a bespoke genotype tine scoring of linear type traits is carried out on both panel (IDB) developed in Ireland [18] with three ver- registered and commercial beef herds by trained clas- sions, i.e. version 1 (n = 28,288; 17,137 SNPs), version 2 sifers who are employed by the Irish Cattle Breeding (n = 147,235; 18,004 SNPs) and version 3 (n = 434,464; Federation [4, 16], with each classifer scoring animals 53,450 SNPs). Each animal had a call rate higher than from a range of diferent breeds. Te muscularity type 90% and only autosomal SNPs, SNPs with a known chro- traits used in the present study describe the develop- mosome and position on UMD 3.1, and SNPs with a call ment of the hind quarter (DHQ), inner thigh (DIT), and rate higher than 90% within a panel were retained for loin (DL), and the width of the thigh (TW) and with- imputation. ers (WOW). Each trait was scored on a scale from 1 to All genotyped animals were imputed to HD using a 15 where 1 = low and 15 = high for DHQ, DIT and DL, two-step approach in FImpute2 with pedigree informa- and 1 = narrow and 15 = wide for TW and WOW (see tion [19]; this involved imputing the 3 k, LD and IDB Additional fle 1: Table S1). Data on these fve linear type genotyped animals to the Bovine SNP50 density, and traits were available for 147,704 purebred Angus (AA), consequently imputing all resulting genotypes (including Charolais (CH), Hereford (HE), Limousin (LM), or Sim- the Bovine SNP50 genotypes) to HD using a multi-breed mental (SI) beef cattle scored between the age of 6 and reference population of 5504 infuential sires genotyped 16 months from 2000 to 2016 [7]. on the HD panel. Imputation to whole-genome sequence Doyle et al. Genet Sel Evol (2020) 52:2 Page 3 of 18

(WGS) was then undertaken using a reference popula- trait within each breed separately by using the QQman tion of 2333 Bos taurus animals from multiple breeds package [25] in R. from Run6.0 of the 1000 Bull Genomes Project [20]. All variants in the sequence reference population were called QTL detection, gene annotation and variance explained using SAMtools and genotype calls were improved using A genome-wide SNP signifcance threshold of −8 −5 the Beagle software to provide a consensus SNP density p ≤ 1 × 10 and a suggestive threshold of p ≤ 1 × 10 across all animals. Details of the alignment to UMD 3.1 were applied to each trait. SNPs in close proximity to bovine reference genome, variant calling and quality con- each other (< 500 kb) were classifed as being located trols completed within the multi-breed reference popula- within the same QTL. Genes within 500 kb of the most tion are described in Daetwyler et al. [20]. In total, 41.39 signifcant SNP in a peak above the genome-wide thresh- million SNPs were identifed across the genome and the old were identifed using Ensembl 94 [26] on the UMD average coverage was 12.85X. Imputation of the HD gen- 3.1 bovine genome assembly. Moreover, the functional otypes to WGS was completed by frst phasing all 638,662 consequence of all signifcantly associated SNPs was pre- imputed HD genotypes using Eagle (version 2.3.2) [21], dicted using the Variant Efect Predictor tool [27] from and subsequently imputing to WGS using minimac3 Ensembl. Te Cattle QTLdb (https​://www.anima​lgeno​ [22]. Te average genotype concordance of imputation to me.org/cgi-bin/QTLdb​/BT/index​) was used to identify WGS, defned as the proportion of correctly called SNPs QTL that were known to be associated with other traits versus all SNPs using a validation set of 175 Irish animals, in cattle. To identify QTL regions that were suggestive in was estimated to be 0.98 [23]. more than one breed, each chromosome was split into Quality control edits were imposed on the imputed 1-kb genomic windows, and windows containing sug- −5 sequence genotypes within each breed, separately. gestive SNPs (p ≤ 1 × 10 ) were compared across the Regions of poor WGS imputation accuracy, which could breeds. be due to local mis-assemblies or mis-orientated con- Te proportion of genetic variance of a trait explained tigs, were removed. Tese regions were identifed using by a SNP was calculated as: an additional dataset of 147,309 verifed parent progeny 2p(1 − p)a2 relations as described by [23], which removed 687,352 , σ2 SNPs from each breed. Ten, all SNPs with a minor allele g frequency (MAF) lower than 0.002 were removed. Fol- where p is the frequency of the minor allele, a is the allele lowing all SNP edits, 16,342,970, 17,733,147, 16,638,022, σ2 17,803,135 and 17,762,681 autosomal SNPs remained for substitution efect and g is the genetic variance of the the analysis of the AA, CH, HE, LM, and SI populations, trait in question. respectively. Meta‑analysis Following the within-breed association analyses, meta- Association analyses analyses were conducted for all traits across all fve beef Te association analyses were performed within each breeds using the weighted Z-score method in METAL breed separately using a linear mixed model in the GCTA [28]; only SNPs that were included in the analyses of all software [24]. Autosomal SNPs from the original HD of the individual breeds were considered here. METAL panel (i.e., 734,159 SNPs) were used to construct the combines the p-values and the direction of SNP efects genomic relationship matrix (GRM). Te model used for from individual analyses, and weights the individual the within-breed analysis was the following: studies based on the sample size to compute an overall y = µ + xb + u + e, Z-score:

Σiziwi where y is a vector of preadjusted phenotypes, μ is the Z = , 2 overall mean, x is the vector of imputed genotypes, b is Σiwi the vector of additive fxed efects of the candidate SNP u ∼ N 0, Gσ2 to be tested for association, u is the vector where wi is the square root of the sample size of of additive genetic efects, where G is the genomic rela- breed i, and zi is the Z-score for breed i calculated as = −1 − pi tionship matrix calculated from the HD SNP genotypes zi φ 1 2 �i , where φ is the cumulative distribu- σ2 ∼ σ2  and u is the additive genetic variance, and e N 0, I e tion function, and P and Δ are the p-value and direction σ2  i i is the vector of random residual efects and e is the of efect for breed i, respectively. residual variance. Manhattan plots were created for each Doyle et al. Genet Sel Evol (2020) 52:2 Page 4 of 18

Conditional analyses traits (see Additional fle 3: Figure S6). However, there Te summary statistics from the individual analyses for was some overlap in suggestive 1-kb windows between the CH population were further used to conduct condi- the traits DIT and TW; 11 windows contained SNPs tional analyses on BTA2 based on the Q204X mutation, of suggestive signifcance and the gene EMILIN22 on which was previously reported to be associated with BTA24 was identifed within those windows for both muscularity traits in cattle [29]. Tese analyses were traits. Nine genomic windows were associated with both undertaken for each trait in the CH population using the DL and WOW traits, i.e. on BTA6 (n = 2), BTA15 the conditional and joint association analysis (COJO) (n = 6), and BTA22 (n = 1). Te windows on BTA15 method in GCTA [30]. Te Q204X mutation was contained suggestive SNPs that were located within the included as a fxed efect in the association analysis UCP3 and CHRDL2 genes. model and the allele substitution efect of all remaining Eighty-four SNPs within nine QTL were sugges- SNPs were re-estimated. tively associated with the DHQ trait. Among these, −7 the most strongly associated (p = 3.34 × 10 ) SNP Pathway and enrichment analyses was rs433492843 on BTA23 located in an intron of the Pathway analysis was conducted on all plausible candi- PTCHD4 gene (Table 1); it accounted for 0.002% of the date genes within a 500-kb region up- and downstream genetic variance in this trait. A QTL on BTA1 was also of SNPs that were discovered to be suggestively or signif- strongly associated with DL with the most strongly asso- −6 cantly associated with each trait in each breed. For each ciated SNP being rs465472414 (p = 1.06 × 10 ), which gene list, DAVID 6.8 [31] was used to identify gene ontol- accounted for 0.08% of the genetic variance in this trait ogy (GO) terms and KEGG pathways which were sig- (Table 2). Other SNPs suggestively associated with DL nifcantly overrepresented (p < 0.05) by the set of genes. were also identifed within the TMEM178A gene on Enrichment analyses among the suggestive and signif- BTA11 and within the UCP3 and CHRDL2 genes on cant SNPs were performed to estimate if the number BTA15. of SNPs in each annotation class was greater than that An intergenic SNP located on BTA29, rs109229230, was −7 expected by chance for each trait per breed [32]; this was the most strongly associated (p = 1.82 × 10 ) with DIT done separately per trait and per breed and was calcu- (Table 3). Ninety-eight SNPs were suggestively associated lated as: with TW. Te strongest QTL association with TW was a c −1 on BTA13, on which 10 SNPs of suggestive signifcance Enrichment = , were identifed in a 1-Mb region (Table 4); rs137458299 b d −7 displayed the strongest association (p = 2.99 × 10 ) where a is the number of suggestive and/or signifcant and explained 0.9% of the genetic variation in TW. One SNPs in the annotation class of interest, b is the total hundred and seventy-three SNPs were associated with number of suggestive and/or signifcant SNPs that were WOW in the AA population; among these 29.4% were associated with the trait of interest, c is the total number located on BTA14 (Table 5) and the most strongly asso- −9 of SNPs in the annotation class in the association analy- ciated SNP, rs468048676, (p = 2.34 × 0 ), was an inter- sis, and d is the overall number of SNPs included in the genic variant on BTA6. association analysis.

Results Hereford Summary statistics of the fve linear type traits for each No signifcant SNPs were detected for any of the mus- breed are in Additional fle 1: Table S1. Signifcant cularity linear type traits in the HE population, although −8 −5 suggestive SNPs were identifed for all fve traits. How- (p ≤ 1 × 10 ) and/or suggestive (p ≤ 1 × 10 ) SNPs were detected in all traits for the fve breeds but the exact loca- ever, no genomic window was common to all fve type tions of these SNPs and the direction of the efects of traits (see Additional fle 3: Figure S6); six 1-kb windows these SNPs difered by breed. Manhattan plots for all the i.e. on BTA5 (n = 1), BTA7 (n = 4), and BTA25 (n = 1) analyses are available in Additional fle 2: Figures S1–S5. were shared between DHQ and DIT with three 1-kb regions on BTA20 shared between DIT and TW. Within‑breed analyses Tree hundred and eleven SNPs were suggestively asso- Angus ciated with DHQ. Te strongest association with DHQ Whereas no signifcant SNPs were detected for any of was located within a 1-Mb QTL on BTA7 where 26 SNPs the muscularity linear type traits in the AA population, of suggestive signifcance were identifed (Table 1). Te −7 −5 intergenic SNP, rs446625612 (p 1.16 10 ) was the suggestive SNPs (p ≤ 1 × 10 ) were identifed for all fve = × traits. No genomic region was common to all fve type most strongly associated with DL and located within a Doyle et al. Genet Sel Evol (2020) 52:2 Page 5 of 18 , f , , f , f f , f , e , PMS1 , ANKAR f f , MYO7B , SLC40A1 f f , ORMDL1 f , MSTN f , COL5A2 f , ANKAR f , ARHGEF4 , PMS1 f , IWS1, NAB1 , MYO7B f f , MFSD6 f e e , COL3A1 e f , COL5A2 , MSTN f , SLC06A1 e e e , ASNSD1 , NAB1 , PPP1R1C , ITGB5 f , POT1 e , MRAP2 e e e e SLC40A1 IWS1 ZNF804A ORMDL1 COL3A1 MFSD6 , OSTN GHSR , APOD OPA1 , FAM43A DLG1 SFXN1 , DRD1 ADAM28 PTCHD4 ENOSF1 , ADCYAP1 WDR75 GPR37 PREX2 UBL7 , SEMA7A PML ANK3 , CDK1 RHOBTB1 KALRN , ATG10 ACOT12 KLF12 MRPL33 , GLP1R GLO1 WDR75 , ASNSD1 PDE1A AMDHD1 , ZNF638 , DYSF CYP26B1 ENSBTAG00000043492 SLCO4C1 TBX18 JARID2 , ZKSCAN2 AQP8 Candidate genes within this QTL Candidate 0.951 0.036 0.687 0.005 0.903 0.004 0.992 0.982 0.451 0.520 0.480 0.910 0.000 0.695 0.100 0.799 0.523 0.996 0.163 0.993 0.005 0.996 0.990 0.998 SI 0.949 0.073 0.332 0.000 0.941 0.028 0.000 0.054 0.000 0.416 0.317 0.000 0.983 0.269 0.885 0.043 0.394 0.997 0.231 0.000 0.005 0.003 0.962 0.993 LM + allele 0.061 0.000 0.264 0.000 0.958 0.000 0.002 0.054 0.594 0.580 0.237 0.913 0.996 0.795 0.094 0.250 0.374 0.000 0.000 0.000 0.000 0.000 0.994 0.000 HE 0.940 0.133 0.565 0.977 0.050 0.079 0.007 0.026 0.521 0.827 0.531 0.851 0.995 0.761 0.000 0.447 0.562 0.000 0.283 0.993 0.000 0.000 0.008 0.993 CH 0.940 0.032 Allele frequencyAllele of 0.276 0.995 0.918 0.000 0.000 0.013 0.617 0.574 0.764 0.177 0.020 0.681 0.220 0.491 0.567 0.000 0.486 0.997 0.000 0.000 0.009 0.985 AA − 6 − 6 − 6 − 7 − 7 − 49 − 6 − 7 − 7 − 8 − 7 − 7 − 6 − 7 − 6 − 30 − 8 − 7 − 6 − 6 − 6 − 7 − 7 − 7 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 value - p 3.69 3.25 7.60 3.34 8.07 9.07 4.67 2.20 9.70 2.95 5.23 3.16 1.84 7.44 1.09 3.22 4.83 1.58 1.56 1.28 1.78 4.56 1.48 1.00 a b b b a a a b a b a a b bc a a a a a a d a a a 33855595 60112855 41747427 65712927 Most signifcant SNP Most signifcant 36567715 89799122 84122153 21041063 72569526 5655837 35062974 16273851 49465278 3261897 12571991 12676690 103695804 23400365 120584401 69348458 76612104 6808074 6622189 14446207 gene contained at least one signifcant SNP least one signifcant at gene contained f downstream gene variant downstream d 4 7 9 4 2 4 2 5 7 3 6 8 16 12 26 16 11 47 77 16 113 228 Number of suggestive Number of suggestive SNPs and signifcant 5128 2610 upstream gene variant gene variant upstream c gene contained at least one suggestive SNP, SNP, least one suggestive at gene contained 34360874 60696179 42287354 66303927 14987913 End 21541072 37107434 90487119 84673367 73103211 6179062 35572213 18516719 50129513 3761897 13111217 13181229 104247192 23937019 130071811 10711228 73614763 12640428 77238886 e intron, intron, b intergenic, intergenic, 33353270 59612855 41204249 65068999 13916060 Start 20541063 35913368 89299122 83591608 72017409 5155837 34538609 15669176 48965278 2761800 12056019 12163298 103195804 22649471 72069526 35194 68813144 4293223 76112104 a Location of the mostLocation to the top limited QTL, signifcant with development of hind associated per breed, fve which were quarter and the genes located 14 5 23 9 2 Chr 23 24 4 7 8 10 21 28 12 13 23 11 7 25 1 2 1 2 6 1 Table within each breed within these QTL Breed SNP classifcation: AA CH HE LM SI Signifcance of SNPs within genes: of SNPs Signifcance Doyle et al. Genet Sel Evol (2020) 52:2 Page 6 of 18 d , ORMDL1, d , ORMDL1 e e d ­ PMS1 , , PMS1 d e , SLC40A1 , PPME1 NEU3 MSTN , SLC40A1 e d e , MSTN e , COQ6 , DNAL1 ZNF410 FAM161B , ANKAR , ANKAR e , MFSD6, ­ , KBTBD8 d , MFSD6 d e e , NABP1 d , ACOT4 , COL5A2 e ­ ASNSD1 , , ASNSD1 d d e , KIF26B EFCAB2 d COL3A1 , COL5A2 COL3A1 GCNT4 , HMGCR ​ CDKL8 , MAP4K3 RAB6A , MRPL48 UCP2 UCP3 ​ , ACACA , MYO19 USP32 , RIPK4 PRDM15 C2CD2 BACE2 WDR75 PSEN1 , ACOT2 SMYD3 PSTPIP2 , ST8SIA5 , MYO1B STAT1 ENSBTAG00000044810 CRIM1 ENSBTAG00000044497 EED , SYTL2 , CREBZF TMEM126A TMEM126B WDR75 , TIMP3 MGC137211 MGC137014 LARGE1SYN3 HS3ST1 , SHISA2 USP12 SH3RF2 SPIN1 , FBXW12 E2F5 PITPNB , SUCLG2 FAM19A1 , CYP4A22 CYP4X1 Candidate genes within this QTL Candidate SI 0.976 0.872 0.068 0.996 0.044 0.993 0.004 0.024 0.093 0.019 0.997 0.952 0.988 0.937 0.801 0.511 0.830 0.211 0.975 0.990 0.291 0.070 0.003 0.004 0.883 LM 0.967 0.773 0.000 0.000 0.102 0.972 0.028 0.000 0.898 0.000 0.987 0.884 0.964 0.000 0.378 0.073 0.934 0.251 0.830 0.997 0.000 0.346 0.114 0.992 0.000 + allele HE 0.000 0.090 0.904 0.000 0.969 0.005 0.000 0.000 0.135 0.012 0.996 0.025 0.011 0.030 0.304 0.617 0.047 0.163 0.898 0.000 0.046 0.064 0.981 0.987 0.946 CH 0.015 0.105 0.928 0.000 0.088 0.010 0.079 0.007 0.884 0.006 0.994 0.034 0.011 0.000 0.417 0.409 0.000 0.819 0.868 0.000 0.016 0.627 0.052 0.010 0.960 AA Allele frequencyAllele of 0.003 0.181 0.032 0.993 0.045 0.000 0.000 0.000 0.975 0.000 0.000 0.014 0.876 0.911 0.650 0.198 0.041 0.000 0.126 0.000 0.002 0.957 0.908 0.975 0.000 − 6 − 6 − 6 − 6 − 6 − 6 − 26 − 7 − 7 − 6 − 6 − 7 − 6 − 6 − 6 − 10 − 7 − 6 − 7 − 7 − 7 − 7 − 7 − 7 − 9 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 value - P 1.06 3.45 6.26 1.43 2.23 3.50 1.19 4.45 4.12 1.20 1.42 1.16 1.11 1.94 1.80 6.69 5.78 2.70 3.30 5.12 3.37 5.24 3.45 1.04 9.77 c a a c a a a a b b a a b b b a a b b b a a a a a gene contains at least one signifcant SNP least one signifcant at gene contains e 143205947 6241208 39655188 22049725 55096343 6808074 80168803 6747317 59590500 32558878 18442324 76312761 9733633 33461156 80091780 69646862 46437392 Most signifcant SNP Most signifcant 72238658 85423776 99509887 111176155 91313081 34044822 3851559 13430257 3 4 3 3 7 4 5 6 2 5 4 9 78 54 27 15 10 26 18 11 14 14 748 127 1728 Number of suggestive Number of suggestive SNPs and signifcant downstream gene variant downstream c gene contains at least one suggestive SNP, SNP, least one suggestive at gene contains d intron, intron, 143712126 6752759 40155196 22560915 55635294 8714844 81037622 8287013 60243933 33120555 18942324 76823259 10275049 33961156 81070931 70268366 46937392 End 72751064 85960329 100073842 112106706 91857894 34644069 4351559 13934964 b intergenic, intergenic, a 142705947 5741208 39155170 21539414 53716499 37387 79648265 5545383 58665425 32025893 17934758 75812761 9233633 32803688 79527472 69082585 45933369 Start 71738658 84923776 99009887 110629080 90813081 33231032 3339555 12894306 Location of the most signifcant QTL, limited to the top 5 per breed which were associated with development of loin, and the genes within these located with development associated to the top 5 per breed which were limited of the most QTL, Location signifcant 1 10 1 11 15 2 2 2 7 16 11 16 29 12 14 17 24 Chr 5 10 3 6 8 22 4 19 2 CH AA HE LM SI Table within each breed QTL Breed SNP classifcation: Signifcance of SNPs within genes: of SNPs Signifcance Doyle et al. Genet Sel Evol (2020) 52:2 Page 7 of 18 ,

e e , ORMDL , ORMDL1 f f , PMS1 , PMS1 f f f , COL6A2 , COL6A1 f e , MSTN , MSTN f e , SLC40A1 f , SLC40A1 f , MFSD6 f , MFSD6 f , ANKAR f , ANKAR f , NAB1 , MYL12B , MYL12A , MYOM1 f , NAB1 f e , ALKBH4 COL26A1 , ARHGEF4 e , COL5A2 e , KBTBD8 1 , ASNSD ­ EMILIN2 e f , PPP1R1C NEUROD1 , MEOX2 e e , ADRA1B , FANCF e COL3A1 , COL5A2 COL3A1 , HOXA13 , HOXA11 , HOXA10 HOXA9 Candidate genes within this QTL Candidate , PCBP3 , SLC19A1 TRAPPC10 , COL18A1 , , HOXA7 , HOXA6 , HOXA5 , HOXA4 , HOXA3 , HOXA2 HOXA1 NDC80 , POLR2J , MYL10 , PRMT3 SLC6A5 WDR75 , PREX2 ARFGEF1 , CPA6 STK3 , KCNS2 POP1 RPL30 MATN2 , ABL2 SOAT1 , TOR3A , ANGPTL1 RASAL2 GAS2 ENSBTAG00000023806 EBF1 , FLRT3 ESF1 , NDUFAF5 TRHR , PMS2 EIF2AK1 USP42 LMTK2 , NIPA2 NIPA1 WDR75 , ASNSD1 ZNF804A PDE1A AGMO ENSBTAG00000046369 E2F5 ENSBTAG00000044703 ENSBTAG00000045960 SUCLG2 SI 0.631 0.900 0.983 0.002 0.919 0.005 0.018 0.000 0.821 0.000 0.939 0.995 0.000 0.000 0.071 0.057 0.490 0.556 0.524 0.010 0.870 0.072 0.297 0.997 0.002 LM 0.179 0.014 0.036 0.000 0.939 0.028 0.054 0.000 0.853 0.978 0.000 0.000 0.004 0.104 0.056 0.066 0.074 0.195 0.394 0.994 0.060 0.886 0.359 0.000 0.987 + allele HE 0.073 0.990 0.014 0.000 0.937 0.000 0.054 0.000 0.063 0.000 0.082 0.046 0.002 0.758 0.097 0.997 0.617 0.231 0.374 0.996 0.866 0.980 0.639 0.000 0.000 CH 0.712 0.015 0.000 0.000 0.043 0.079 0.026 0.062 0.230 0.989 0.032 0.998 0.000 0.122 0.928 0.032 0.409 0.402 0.562 0.027 0.043 0.052 0.619 0.010 0.995 AA Allele frequencyAllele of 0.988 0.991 0.977 0.998 0.048 0.000 0.013 0.000 0.904 0.000 0.755 0.000 0.000 0.262 0.160 0.907 0.802 0.308 0.433 0.096 0.949 0.104 0.474 0.003 0.000 − 7 − 7 − 7 − 7 − 7 − 49 − 7 − 6 − 6 − 6 − 7 − 7 − 7 − 7 − 7 − 10 − 28 − 8 − 6 − 7 − 6 − 7 − 6 − 8 − 6 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 value - 9.55 4.19 2.24 3.50 1.82 9.07 2.20 2.69 6.72 3.58 4.05 9.09 1.84 7.98 2.68 5.94 2.20 1.36 1.67 2.52 5.21 2.33 1.47 5.30 2.08 P b a bd a a a a a b a c a bc b a b a a b a a a a a a gene contains at least one signifcant SNP least one signifcant at gene contains f 7003978 23630609 23117106 6747317 35542698 33855595 12116324 70373241 Most signifcant SNP Most signifcant 147187440 6808074 37530390 6834924 60943313 21917306 4424012 72608186 57757740 38505834 3226165 14447892 80090294 22115210 17836523 34236342 24290699 downstream gene variant downstream d 9 6 4 7 4 4 4 3 3 3 8 7 59 49 36 38 38 67 55 17 18 10 102 2441 Number of suggestive SNPs and signifcant 5075 upstream gene variant, gene variant, upstream c gene contains at least one suggestive SNP, SNP, least one suggestive at gene contains e intron, intron, b 8341939 24137261 23622641 11101064 36122096 34360874 12618550 70999401 End 147685998 38030390 10711228 68850085 62320499 22460213 5000928 73679002 58269158 39005834 3758925 14957932 80610485 22651417 18336523 34737653 24826548 intergenic, intergenic, a 6038290 23124509 22558133 4973733 35042698 33353270 11556240 69229353 Start 146687440 36998437 7850 67849241 60443313 21313583 3924012 72100887 57257740 37995664 313343 13935604 79492849 21615210 17057917 33706576 23787949 Location of the mostLocation to the top limited QTL, signifcant 5 per breed, with development associated which were of inner thigh, and the genes located 13 4 1 2 25 14 2 4 Chr 1 24 2 14 16 29 4 7 14 25 2 2 14 17 21 22 29 3 SI Table within each breed within these QTL Breed SNP classifcation: Signifcance of SNPs within genes: of SNPs Signifcance AA CH HE LM Doyle et al. Genet Sel Evol (2020) 52:2 Page 8 of 18 , ORMDL1 g , DDIT4 , , AIFM2 f f , ORMDL1 f , PMS1 g , PMS1 f g , COL13A1 , ANAPC16 f f g , MSTN g , MSTN f , SLC40A1 g , SLC40A1 g , MFSD6 , UBE2V1 g , MFSD6 f f f , ANKAR , SRGN , SLC25A16 g f , NAB1 , NAB1 g g , SGLP1 PCBD1 SPOCK2 f , DNA2 f , COL5A2 ­ ADAMTS12 g , EEF1A1 , DAP FAM173B f , ASNSD1 g f , SLC2A5 , ASNSD1 f g f , PPP1R1C f , TGFB3 , EGR2 , CDK1 RHOBTB1 f f COL3A1 , ANKAR , COL5A2 COL3A1 ADAMTS14 MYOZ1 , STX6ACBD6 , MR1 WDR75 CTNNA2 , SLC9A8 ZNFX1 , B4GALT5 , RGS20 OPRK1 , ATP6V1H , PPP1RI5B PIK3C2B NFASC ETNK2 , GOLT1A GABRA6 TTLL5 LIFR , EGFLAM , GDNF C1QTNF3, WDR75 ZNF804A PDE1A HS3ST5 , ANK1 , GPAT4 SFRP1 , GOLGA7 NEPN , GOPC MMP16 , ENSBTAG00000011094 ENSBTAG00000045320 , ABLIM1 ATRNL1 AFAP1L2 , FGF19 FGF4 CCND1 KHDC3L CTNND2 ANK3 , MYPN SIRT1 C5H12orf50 , C5H12orf29 , CEP290 Candidate genes within this QTL Candidate SI 0.000 0.004 0.994 0.108 0.971 0.979 0.000 0.033 0.328 0.003 0.642 0.809 0.477 0.286 0.000 0.019 0.085 0.378 0.995 0.108 0.000 0.000 0.523 0.486 0.000 LM 0.072 0.028 0.997 0.157 0.962 0.056 0.000 0.981 0.230 0.994 0.116 0.097 0.394 0.247 0.997 0.985 0.916 0.506 0.994 0.850 0.003 0.000 0.430 0.591 0.985 + allele HE 0.991 0.000 0.000 0.866 0.074 0.985 0.997 0.020 0.421 0.985 0.000 0.057 0.374 0.547 0.998 0.981 0.052 0.299 0.004 0.886 0.000 0.000 0.476 0.424 0.003 CH 0.948 0.079 0.047 0.232 0.994 0.980 0.000 0.034 0.589 0.985 0.571 0.205 0.565 0.649 0.000 0.980 0.107 0.449 0.006 0.197 0.013 0.010 0.829 0.810 0.996 AA 0.027 0.000 Allele frequencyAllele of 0.017 0.191 0.026 0.986 0.990 0.959 0.342 0.015 0.617 0.561 0.434 0.245 0.057 0.969 0.712 0.461 0.000 0.790 0.000 0.997 0.581 0.388 0.000 − 6 − 7 − 6 − 6 − 6 − 25 − 9 − 9 − 9 − 9 − 6 − 6 − 6 − 6 − 6 − 15 − 7 − 6 − 7 − 6 − 7 − 7 − 7 − 6 − 7 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 value - P 2.65 2.99 1.99 5.14 4.07 4.09 8.65 7.78 4.17 3.87 1.75 4.24 1.72 1.30 1.01 2.88 1.01 1.92 6.31 8.03 4.82 1.65 8.99 1.97 5.42 synonymous gene variant synonymous e a b a d b a b a b a c b a a a a e b a a a d gene contains at least one signifcant SNP least one signifcant at gene contains a a a g 54349531 6808074 78722523 40272197 14450953 77271966 47898869 2169248 63066185 36267035 35541033 33604527 75539465 18095093 Most signifcant SNP Most signifcant 7772897 62296495 12970065 12083258 87980123 18015356 26134688 37216988 36340468 16275379 23116129 downstream gene variant, gene variant, downstream d 2 4 2 7 3 5 2 3 5 3 3 4 5 3 10 10 55 13 30 11 43 1860 Number of suggestive SNPs and signifcant 1526 1322 1025 upstream gene variant, gene variant, upstream c gene contains at least one suggestive SNP, SNP, least one suggestive at gene contains f intron, intron, b 54849531 10186234 79223584 2669694 64073838 41162914 14952210 36785436 78193017 36071133 48688066 34105290 75039465 18595093 End 10670961 63246384 13731582 88487031 12640428 18515510 40249741 37719425 37412173 24087892 23619374 intergenic, intergenic, a 53741952 7850 78082912 1654734 63066185 39512041 13916060 35767035 76746937 35041033 47398869 32996000 75039465 17326996 Start 4973607 61727533 12470065 87480123 11570479 17511696 24331178 36716988 35840468 9234253 22612620 Location of the mostLocation to the top limited QTL, signifcant 5 per breed, with thigh associated were which width, and the genes within these located QTL 11 2 13 16 16 20 2 27 14 26 29 9 7 5 Chr 2 20 9 10 2 24 28 9 20 28 14 4 AA CH SI Signifcance of SNPs within genes: of SNPs Signifcance HE Table within each breed Breed SNP classifcation: LM Doyle et al. Genet Sel Evol (2020) 52:2 Page 9 of 18 , ORMDL1 e , ORMDL1 d , PMS1 e e e , MSTN e , ANXA6 , SLC40A1 d , SLC40A1 e d , MFSD6 e , ANKAR , ANKAR e e , NAB1 , NAB1 MFSD6 MSTN PMS1 d d d d d d , COL5A2 e , LRRN3 , ASNSD1 d , PPP1R1C NEUROD1 e d d , ZC3H15 d , KCNQ3 , COL5A2 COL3A1 COL3A1 HOXD13 Candidate genes within this QTL Candidate ITGAV NT5C3A , KBTBD2 STK3 , KCNS2 POP1 RPL30 MATN2 ANK3 , CDK1 RHOBTB1 KCNMA1 , ZNF300 GPX3 RPS14 , MYOZ3 RNF144A , RSAD2 GSE1 , IRF8 FOXC2 FST , ACSL5 GPAM WDR75 , ASNSD1 PDE1A , , HOXD12 , HOXD11 , HOXD10 , HOXD9 , HOXD4 , HOXD3 HOXD1 NPNT , GSTCD LPP IMMP2L NEPN , GOPC PDS5B , ADCY2 FASTKD3 EPHA6 EREG , AREG RCHY1 TG , KLHL38 FBXO32 ANXA13 APIP , PDHX WDR75 0.456 0.991 0.024 0.729 0.468 0.009 0.217 0.446 0.480 0.000 0.800 0.479 0.000 0.000 0.004 0.229 0.019 0.998 0.007 SI 0.000 0.005 0.951 0.977 0.992 0.004 0.000 0.053 0.690 0.511 0.011 0.800 0.532 0.000 0.000 0.043 0.127 0.394 0.998 0.995 0.964 0.248 0.985 0.016 0.977 LM 0.991 0.995 0.969 0.000 0.003 0.028 + allele 0.000 0.965 0.525 0.664 0.008 0.163 0.507 0.559 0.998 0.250 0.213 0.626 0.000 0.008 0.000 0.457 0.019 0.084 0.000 HE 0.009 0.009 0.144 0.977 0.930 0.000 0.003 0.060 0.868 0.889 0.949 0.280 0.565 0.000 0.000 0.447 0.504 0.564 0.000 0.000 0.040 0.261 0.979 0.020 0.009 CH 0.998 0.019 0.936 0.007 0.996 0.079 0.000 0.028 0.640 0.367 0.022 0.664 0.479 0.532 0.000 0.490 0.464 0.433 0.000 0.000 0.022 0.902 0.969 0.008 0.000 Allele frequencyAllele of AA 0.997 0.008 0.016 0.957 0.004 0.000 − 7 − 7 − 8 − 7 − 7 − 6 − 6 − 6 − 6 − 12 − 6 − 8 − 10 − 9 − 7 − 8 − 8 − 7 − 7 − 6 − 9 − 6 − 6 − 7 − 21 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 × 10 value - 3.32 2.98 1.52 1.61 1.46 5.13 4.85 3.38 2.78 8.77 1.53 6.10 6.85 8.15 3.81 6.35 5.67 7.49 1.76 2.49 2.34 3.85 1.59 3.42 2.02 P a a a a b a a a b b a a b d b d a b a b b gene contains at least one signifcant SNP least one signifcant at gene contains a a b a e 33604527 21039862 90079990 9559686 64680447 58084434 28561051 91513217 18244843 79604503 Most signifcant SNP Most signifcant 14450953 64309256 6622189 19817910 68370173 16943776 32634467 12047513 25929649 33310942 65668943 6808074 41142399 9914135 66535436 3 5 6 5 3 3 7 6 7 5 9 3 4 3 9 4 16 26 10 16 68 15 13 725 Number of suggestive SNPs and signifcant 1227 downstream gene variant downstream c gene contains at least one suggestive SNP, SNP, least one suggestive at gene contains d intron, intron, b 21539862 34471196 90599173 65180447 10527711 58584434 29073791 92064889 18889304 80104503 End 14957655 64814905 8495179 68870239 17454059 33148059 12549350 27124758 33810987 21121181 66168943 8714844 41670931 10414890 67113172 intergenic, intergenic, a 20539841 32996000 89579990 63740415 9053737 57566849 28061051 90992982 17744843 79028842 Start 13916060 63750754 5547713 67870173 15669176 31765108 11547513 25429449 32810942 19014612 65137216 218127 40642399 9414135 66035436 Location of the mostLocation to the top limited QTL, signifcant 5 per breed, with width associated which were of withers, and the genes within these located 2 9 11 4 2 4 12 6 14 1 Chr 2 7 2 14 28 28 18 20 26 6 20 2 1 14 15 5 SI Table within each breed QTL Breed SNP classifcation: Signifcance of SNPs within genes: of SNPs Signifcance HE LM CH AA Doyle et al. Genet Sel Evol (2020) 52:2 Page 10 of 18

QTL on BTA4 encompassing the ENSBTAG00000044810 −21 for DIT and DHQ to 2.02 × 10 for WOW). In the con- gene. Most interestingly, the strongest association within ditional analyses, this SNP was non-signifcant for DL, the QTL on BTA2 with DL was an intronic variant, which TW, and WOW but remained suggestive for both DIT explained 0.7% of the genetic variance and was located −6 −6 (p = 4.02 × 10 ) and DHQ (p = 4.62 × 10 ). Te most within the muscle related gene MYO1B. signifcant SNP in the conditional analyses of DHQ, DL, In total, 155 SNPs were suggestively or signifcantly DIT, and TW was rs41638272, which is an intergenic associated with DIT, and 43% of these were located SNP located 10 kb from the SLC40A1 gene; this SNP within a 1-Mb QTL on BTA7 (Table 3) where a num- was signifcant in the original analyses but its signif- ber of signifcant SNPs were located within the EBF1 cance actually increased when the Q204X mutation was gene. For TW, four putative candidate genes were iden- included as a fxed efect. Te most signifcant SNP in the tifed (Table 4): GABRA6 on BTA7, TTLL5 on BTA10, conditional analysis of WOW was an intergenic variant, and both ADAMTS12 and GDNF on BTA20. Te SNP, −10 rs457456302 (p = 4.78 × 10 ) that was located 0.1 Mb rs380761563, which displayed the strongest association from the MSTN gene. with WOW, explained 1% of the genetic variance and was located in an intron of the gene TNIP1 on BTA7 Limousin (Table 5). Tere were 164 1-kb suggestive genomic regions that were common across all muscularity traits in the LM Charolais population (see Additional fle 3: Figure S6); another 232 Tere were 483 1-kb suggestive genomic windows com- regions were common to the three traits DHQ, DIT, and mon to all fve type traits in the CH population (see TW, while 326 were common to just DHQ and DIT. All Additional fle 3: Figure S6), among which the vast major- fve traits had signifcant QTL located on BTA2, with ity (n = 482) were located on BTA2 in a region encom- four genes common to all traits located within these passing the MSTN gene. Te fnal region that was shared QTL, namely ASNSD1, GULP1, SLC40A1, and ANKAR. between all fve traits was on BTA11. More overlaps were For DHQ, there were 2983 SNPs above the sugges- found for DHQ and DIT with 904 windows being com- tive threshold and most of these (n = 2610) were located mon to just these two traits, 146 windows common to in a single QTL on BTA2. Te most signifcant SNP, DHQ, DIT, and DL, 304 windows common to DHQ, DIT, −30 rs211140207 (p = 3.22 × 10 ), was located within an DL, and TW, and 178 windows common to DHQ, DIT, 8-Mb QTL on BTA2 that contains 20 genes (Table 1). and TW. Te majority of all these windows were also Te Q204X stop-gain mutation (rs110344317) located located on BTA2. within this QTL was signifcantly associated with DHQ For each of the muscularity linear traits, we identifed a and accounted for 2.4% of the genetic variation in this QTL on BTA2 in the CH population. DHQ had the larg- trait, although the allele frequency of the favourable est number of associated SNPs, i.e. 3707 suggestive and mutation was only 0.02% in the LM population. Te well- 1851 signifcant SNPs (Table 1), all of which were located known MSTN mutation in the Limousin breed, F94L on BTA2 within a single QTL between positions 0.35 and (MAF = 0.3798), did not meet the suggestive threshold 9.79 Mb. In total, 41 genes including MFSD6, MSTN, for association with any of the traits. Similar to DHQ, a and MYO7B were located in this QTL. For DIT, a 10-Mb QTL located between 4.9 and 11 Mb on BTA2 was asso- QTL on BTA2 was identifed that contained 5075 SNPs, ciated with both DIT (Table 3) and TW (Table 4). In total, of which 1796 had a p-value that met the signifcance 2441 and 1526 SNPs were above the suggestive threshold threshold (Table 3), whereas 178 SNPs on BTA2 in the within this QTL on BTA2, and the variant rs110344317, region between 54.1 and 86.1 Mb were signifcantly asso- which was signifcantly associated with DHQ, was also ciated with TW (Table 4). Te same SNP, an intergenic signifcantly associated with both DIT and TW. For variant rs799943285, showed the strongest association the DL trait, 748 SNPs were suggestively associated with all traits. Te well-known Q204X mutation within and located between 55.4 and 82.8 Mb on BTA2. Te the MSTN gene was signifcantly associated with DHQ, most signifcant SNP associated with DL (rs379791493; DIT and TW, and this SNP explained 4.9, 0.05, and 0.01% −10 p = 6.69 × 10 ) was also the most signifcantly asso- of the genetic variation of each trait, respectively. −28 ciated SNP with DIT (p = 2.20 × 10 ). Te most sig- In the conditional analyses within the CH population, nifcant SNP associated with WOW, rs211140207, where the Q204X mutation was included as a fxed efect −12 (p = 8.77 × 10 ), was an intergenic SNP that accounted in the model, the most signifcant SNPs from the original for 0.4% of the genetic variance in this trait and was analyses of each trait generally decreased in signifcance. located in a QTL (between 5.9 and 8.4 Mb) that included Te most signifcant SNP for all traits in the original anal- 724 other signifcantly-associated SNPs (Table 5). −49 yses was rs799943285 (p-value ranging from 9.07 × 10 Doyle et al. Genet Sel Evol (2020) 52:2 Page 11 of 18

Suggestive QTL were also detected on autosomes other −24 SNP, rs799943285 (p = 5.51 × 10 ), which was previ- than BTA2 for all traits in the LM population except for ously identifed as the most strongly associated SNP DIT. A small QTL on BTA11 containing seven sugges- in the CH population for each of these traits. Tis vari- tive SNPs was associated with DHQ. Te SNP with the ant, rs799943285, was also the most signifcantly asso- −6 strongest association, rs43666945 (p = 1.56 × 10 ), was ciated with DL in the meta-analysis, whereas the most an intergenic SNP located 2.2 Mb from the DYSF gene. signifcantly associated SNP with DHQ, rs482419628 Both DHQ and DL had suggestively associated QTL −47 (p = 2.06 × 10 ), was located further downstream on on BTA5. Te most strongly associated SNP for DHQ BTA2 within 5 kb of the ASNSD1 gene. −7 (p = 1.58 × 10 ) was an intergenic SNP, rs718375830, Although the QTL on BTA2 was the most strongly located within a QTL between positions 59.6 and associated with each of the traits analysed, we also iden- 60.6 Mb, whereas the most strongly associated SNP tifed several other QTL associated with muscularity. In −6 with DL (p = 2.70 × 10 ) was also an intergenic SNP, the meta-analysis of DHQ, the most strongly associated rs109909829, but was located within a QTL between 71.7 −7 SNP on BTA11, rs43666945 (p = 1.93 × 10 ), was previ- and 72.8 Mb. ously identifed as being associated with DHQ in the LM population, but the level of signifcance increased in the Simmental meta-analysis and the QTL contained three times the For the SI breed, only a few suggestive 1-kb genomic number of suggestive SNPs compared to that found for regions overlapped for more than two traits. Sixteen 1-kb the LM breed only. A 1-Mb QTL on BTA7 containing the windows were suggestively associated with both DHQ SPRY4 and FGF1 genes was associated with both DL and and DL, eight of which were located on BTA6, seven on WOW in the meta-analysis; the most signifcant SNPs in BTA22, and one on BTA18 (see Additional fle 3: Figure this QTL, however, difered according to trait (see Addi- S6). Five 1-kb windows on BTA23 and one on BTA4 were tional fle 4). common to both DHQ and DIT, while another 15 sug- gestive windows were associated with DHQ and WOW, Enrichment of SNPs 12 of which were located on BTA22. With the exception of WOW in the AA population, inter- Te intergenic SNP, rs437686690 on BTA25, was the genic SNPs were the most common annotation class of −7 most strongly associated (p = 1.00 × 10 ) with DHQ in SNPs that were signifcantly associated with all traits in the SI population and accounted for 0.6% of the genetic all breeds. Te 3′ UTR class was enriched for all traits variance in DHQ (Table 1). In total, 199 SNPs were in the CH and LM populations, whereas there were associated with DL in the SI population, among which more downstream gene variants signifcantly associ- four met the signifcance threshold. Te most signif- ated with DHQ and DL in the AA, CH and HE popula- −9 cant SNP, rs482545354 (p = 9.77 × 10 ), was located tions, and with TW in the CH, HE, and SI populations in an intronic region of the SUCGL2 gene (Table 2) on than expected by chance (Table 6). Te intronic class of BTA22. Although 194 SNPs were suggestively associated SNPs was enriched for all fve traits in HE, for four traits −8 with DIT, only one, i.e., rs798946118 (p = 5.30 × 10 ), (DHQ, DL, TW, and DIT) in SI, three traits in both AA achieved the signifcance threshold which was located on (DHQ, DL, and WOW) and CH (DL, TW, and WOW) BTA21 within a 1-Mb block containing 17 other sugges- and two traits in LM (DHQ and DIT). tive SNPs (Table 3) and accounted for 0.6% of the genetic variance of DIT. Te largest 1-Mb QTL associated with Gene ontology and KEGG pathways TW was located on BTA29 and contained 30 suggestive Several GO terms and KEGG pathways were over-repre- SNPs (Table 4). QTL putatively associated with WOW sented by the genes identifed in each analysis, although were located on BTA1, 4, 9, 12, and 20 (Table 5) where this tended to difer per breed and per trait especially −8 the most signifcant SNP, rs801295753 (p = 5.67 × 10 ), in the smaller AA, HE, and SI populations. In CH and was an intronic SNP on BTA9 located within both the LM, fve GO terms were associated with each trait: skin ROS1 and ENSBTAG000000039574 genes. development (GO:0043588), collagen fbril organisation (GO:0030199), extracellular matrix structural constituent Meta‑analyses (GO:0005201), cellular response to amino acid stimulus Within each of the fve meta-analyses (see Additional (GO:0071230), transforming growth factor beta receptor fle 4), a strong association peak on BTA2 around the signalling pathway (GO:0007179). One KEGG pathway, MSTN gene was detected, which is consistent with i.e. protein digestion and absorption (KEGG:map04974), the individual association results identifed in the CH was also signifcantly associated with all traits in CH and LM populations. For DIT, TW, and WOW, the and LM. Apart from this overlap, only a limited num- most signifcantly associated SNP was the intergenic ber of terms and pathways were over-represented across Doyle et al. Genet Sel Evol (2020) 52:2 Page 12 of 18 Upstream Upstream gene variant 0.73 0.39 0.49 0.37 0.39 0.64 0.50 0.39 0.19 1.52 0.66 0.40 0.98 0.55 0.62 0.31 0.83 – 0.28 5.36 1.67 0.60 0.52 0.41 1.03 Synonymous Synonymous variant – 0.97 0.91 0.47 – 3.98 1.07 3.63 – – – 0.96 – 0.59 – 2.91 1.44 – 0.32 – 1.75 1.09 4.95 0.32 – Stop gained Stop – 7.12 – 12.89 – – 18.71 – – – – 7.47 – 13.49 – – 8.40 – 22.05 – – 27.28 – – – Splice Splice region variant – – – 0.67 – – – – – – – – – 0.70 – – – – – 23.85 – – – – – Splice Splice acceptor variant – – – – – – – – 79.49 – – – – – – – – – – – – – – – – Non - coding transcript – – – – – – 1.60 – – – – – – – – – 1.43 – – – – – – – – Missense variant – 0.62 – 1.15 – – 0.72 – – 3.90 – 0.58 2.49 2.28 – – 0.24 – 0.22 0.74 – – – 0.37 2.79 Intron Intron variant 1.02 0.84 2.32 1.09 1.54 0.63 1.00 1.64 0.58 1.13 1.12 0.79 2.43 1.09 1.05 0.64 1.01 1.50 0.61 1.26 1.44 1.00 1.17 0.84 0.65 Intergenic Intergenic variant 0.89 1.09 0.36 1.02 0.86 1.15 0.96 0.77 1.25 0.93 1.03 1.11 0.48 0.99 1.04 1.19 0.99 0.87 1.21 0.55 0.84 1.00 0.93 1.12 1.12 Downstream Downstream gene variant 3.76 1.08 4.43 0.50 0.40 1.11 2.10 1.21 0.20 0.94 0.14 0.94 0.40 0.86 0.86 0.64 1.22 1.10 0.45 3.62 0.18 1.16 1.00 0.58 1.35 Coding Coding sequence variant – – – – – – – – – – – – – 99.15 – – – – – – – – – – – 5 ′ UTR variant – 0.46 – 0.85 – – – – – – – 0.48 – 1.77 – – 0.54 – 1.45 9.65 – – 40.89 2.46 – Fold enrichment/depletion of SNPs in each annotation class for each trait in each breed trait class for each in each annotation of SNPs enrichment/depletion Fold 3 ′ UTR variant – 3.40 1.80 1.30 – – 7.36 – 2.33 – – 3.47 – 1.16 – – 3.07 – 3.17 2.10 3.28 6.90 – 2.15 1.99 AA CH HE LM SI AA CH HE LM SI AA CH HE LM SI AA CH HE LM SI AA CH HE LM SI 6 Table DHQ DL DIT TW WOW Doyle et al. Genet Sel Evol (2020) 52:2 Page 13 of 18

breeds. Te GO term mitochondrial inner membrane ever performed in beef cattle at that time. Tis previous (GO:0005743) was signifcantly over-represented for the across-breed study was undertaken on 12 traits includ- DL trait in AA and the WOW trait in HE, although none ing birth weight, calving ease, carcass weight, and mature of the same genes were signifcantly associated with both weight across 10 breeds and the results were similar to traits. Another GO term collagen trimer (GO:0005581) what we observed here for the muscularity traits. Saatchi was over-represented for DIT in AA and DL in LM. et al. [15] identifed 159 unique QTL associated with 12 traits, but only four QTL had pleiotropic efects and Discussion segregated in more than one breed. Similar results were Whereas a number of across-breed and breed-specifc observed in an across-breed study on dry matter intake, pleiotropic QTL have been documented for carcass growth and feed efciency in four beef cattle breeds [33]. traits, birth weight, weaning weight, and mature weight Te QTL identifed for these traits were also breed-spe- in beef cattle [15], as well as for dry matter intake and cifc with little overlap among the breeds. Tis is compa- growth and feed efciency [33], no study has attempted rable to our fndings that show that the majority of the to detect across-breed or breed-specifc pleiotropic QTL QTL were also trait-specifc and breed-specifc. for muscularity linear type traits. Previous studies have In total, approximately 83% of all QTL that are sug- been conducted on the genetic correlations between the gestively or signifcantly associated with a trait in our linear type traits themselves [7] and between both meat study overlapped with previously reported QTL associ- yield and carcass cuts with the muscularity linear type ated with other production traits in dairy or beef cattle in traits [34]. While these genetic correlations are moder- the Cattle QTLdb (accessed 08 January 2019). Approxi- ate to strong, none is equal to 1, which implies that two mately 36% of all QTL overlapped with other traits that animals that yield a carcass of similar merit could be were specifcally related to muscle in beef cattle such as morphologically diferent. In fact, a shorter and more body weight, carcass weight and marbling score [31], muscular animal or a taller and less muscular animal calving traits [37], Warner–Bratzler shear force [38], could have the same total carcass weight. In turn, these and longissimus muscle area [39]. One QTL on BTA17 animals could yield very diferent carcass values owing to that was associated with DIT in the SI breed was previ- their distribution of primal cuts. For example, the loin of ously associated with ribeye area in a composite beef cat- an animal harbours generally the most valuable cuts [35, tle breed composed of 50% Red Angus, 25% Charolais, 36]. Terefore, selection for a better-developed loin could and 25% Tarentaise [40]. Our study is further validated lead to a more valuable carcass in comparison to a car- by the presence of signifcantly associated QTL regions cass with a lesser-developed loin if that carcass was still on BTA2, which harbours the MSTN gene, with the fve within the factory specifcation for weight and confor- muscularity traits in the CH and LM breeds, and within mation. Here, we have detected several genomic regions the meta-analysis. In a previous study on fve muscularity that are strongly associated with each of the muscular- type traits, which were combined into one singular mus- ity traits analysed. However, most of these regions were cular development trait in CH, a QTL on BTA2, which unique to each trait or each breed, which indicates the contained MSTN, was the only region signifcantly asso- existence of trait-specifc and breed-specifc QTL for ciated with these traits [13]. muscularity traits. Tus, it is plausible to hypothesise In general, the suggestive and signifcant QTL, and that through more precise (i.e., targeting individual QTL) thus genes, associated with each trait and each breed genome-based evaluations and selection, the morphol- were both trait-specifc and breed-specifc. Te low com- ogy of an animal could be targeted to increase the output monality of QTL among the breeds may be due to difer- of high-quality carcass cuts and consequently improve ent genetic architectures underlying the traits in these the proftability of the farm system and the value to the breeds, or to gene-by-environment or epistatic interac- meat processor [36]. While a similar conclusion could be tions [33], or to diferences in the power to detect QTL achieved through traditional breeding means, exploiting due to the large diferences in population sizes between the breed- and trait-specifc QTL could be more efcient. the breeds. In many cases, the signifcant alleles were Tis is the frst published genome study on muscular- simply not segregating in all fve breeds. Te diferences ity linear type traits in beef cattle using sequence data between breeds may also be due to limitations in the and is one of the few genome-based studies that com- imputation process with the imputation accuracy being pare multiple breeds of beef cattle. Te number of ani- too low to determine strong associations between a SNP mals used in our study is comparable to the number of and a trait; consequently, the minor suggestive associa- animals used in a previous across-breed comparison that tions were interpreted with caution because of the pos- focused on carcass and birth traits in 10 cattle breeds [15] sibility of poor imputation. Overall, the largest number of and was thought to be the largest genome based-study overlaps among signifcant genes were found between the Doyle et al. Genet Sel Evol (2020) 52:2 Page 14 of 18

CH and LM breeds for all traits, which is not surprising to difer by breed [50], sex [51], and age [52] of cattle. considering the relative similarities in the origins of these Other collagen genes, COL6A1, COL6A2, and COL18A1, breeds [41] and of the selection pressures they have expe- on BTA1 were also identifed as candidate genes for DIT rienced [42]. in the AA breed. Both type VI collagen genes have previ- ously been linked to various muscle disorders in humans Myostatin since they are known to afect muscular regeneration MSTN was frst observed as a negative regulator of skel- [53]. Type XVIII collagen has previously been proposed etal muscle mass in mice [43] and since then has been as a useful marker for beef marbling because it is involved identifed as responsible for muscular hypertrophy in in fat deposition in ruminants [54]. cattle [44, 45] and is widely known as the causal variant Another QTL on BTA2 located in the region between for many muscularity and carcass traits in cattle [46, 47]. 13.9 and 14.9 Mb and signifcantly associated with four of Te stop-gain mutation Q204X in MSTN was signif- the traits (DHQ, DIT, TW, and WOW) in the LM breed cantly associated with the muscularity traits in both the contained the PDE1A and PPP1R1C genes. Te most sig- CH and LM populations in the present study. Previously nifcant SNP in this region was an intronic SNP within published research showed that CH and LM calves car- PDE1A. Te PDE1A gene is involved in a pathway related rying one copy of this mutated allele scored better for to myofbroblast formation in smooth muscle in humans carcass traits than non-carrier animals and that young [55] while previous genome-wide studies in mice have CH bulls carrying this mutation presented a carcass with identifed the PPP1R1C gene as a possible candidate gene less fat and more tender meat than non-carriers [47]. In for muscle mass [56]. Overall, the allele frequencies of the present study, the CH and LM animals carrying one the favorable alleles in this 1-Mb region were similar in copy of the minor allele scored signifcantly (p < 0.01) all fve breeds, which support a breed-specifc association higher for muscularity type traits. Te Q204X muta- with DHQ, DIT, TW, and WOW in LM rather than an tion was not signifcant in the AA population and it was imputation error. removed during the data-editing step in both HE and SI An additional breed-specifc QTL on BTA2 that con- as it was non-segregating. When Q204X was included as tains numerous HOXD genes was associated with WOW a fxed efect in the model for the CH animals, no SNPs in the LM population. Te HOXD genes are documented located within the MSTN gene itself remained signif- as having a role in limb [57] and digit [58] formation, thus cant. Tis indicates that the signifcant SNPs within this they probably also play a role in skeletal muscle develop- gene were in tight linkage disequilibrium with Q204X, ment. Te most signifcantly associated SNPs with WOW which provides evidence that this mutation may be in this region were only segregating in the LM breed and causative for the muscularity linear type traits in the CH had a very high favorable allele frequency (0.998) in this breed. Other genes on BTA2 that were signifcantly asso- breed. Tese SNPs were fxed or very close to fxation in ciated with some or all of the traits in CH and LM were the four other breeds. ORMDL1, PMS1, MFSD6, and NAB1, all of which are in In the meta-analyses of DHQ, associated variants in all strong linkage disequilibrium with MSTN in mammals the breeds analysed were identifed, which may be benef- [48]. cial for across-breed genomic prediction [59]. Although the associations detected in the meta-analysis corre- Other candidate genes sponded to associations identifed in the CH and LM While the major peaks on BTA2 in the analyses on CH breeds, three of these QTL on BTA5, 11, and 12 increased and LM, and all the meta-analyses contain MSTN, a in signifcance when compared to the within-breed anal- known contributor to muscle development, it is also ysis. Te QTL on BTA5 which contained the AMDHD1 plausible that other candidate genes within the QTL on gene, was located close to a QTL previously associated BTA2 could also contribute to muscle development. Two with carcass composition [43], whereas the QTL on such genes are COL3A1 and COL5A2. Intronic variants BTA11 contains DYSF, a gene known to be linked with in COL3A1 and upstream and downstream gene vari- muscular dystrophy in humans [60]. Te QTL on BTA14 ants in COL5A2 were signifcantly associated with DHQ contained the PREX2 gene which was previously linked in both CH and LM; however, no SNPs within coding or to carcass weight in Hanwoo cattle [61]. non-coding regions of this gene were associated with any Interestingly, in the meta-analyses of DL and WOW, traits in AA, HE, or SI although the SNPs were indeed a 1-Mb QTL on BTA7 containing the SPRY4 and FGF1 segregating. Collagen is abundant in muscle and the genes became suggestively associated, although it was quantity and stability of these intramuscular fbres have not associated in any breed individually. Te SPRY4 gene previously been linked to eating palatability of beef [49]. was reported to be associated with feed intake in cattle Te quantity and stability of muscle collagen are known [62], whereas FGF1, a member of the fbroblast growth Doyle et al. Genet Sel Evol (2020) 52:2 Page 15 of 18

factor family, is thought to be involved in embryonic complex traits [67, 68]. One possibility to explain the muscle formation [63]. signifcance of these non-coding SNPs is that the non- Similarly, in the meta-analysis of TW, a 3-Mb QTL on coding regions contain gene regulatory sequences, BTA6 containing the NCAPG/LCORL genes became sug- called enhancers, that act over long distances possibly gestively associated, although it was not associated in altering the expression of a gene nearby [67]. Another any breed individually. Tese genes are associated with possibility is that the folding of DNA into the 3-dimen- variation in body size and height in cattle [32], humans sional nucleus may cause distant loci, such as those in [64], and horses [65], thus they are likely plausible candi- non-coding and coding regions, to become spatially date genes associated with muscularity linear type traits close together thus enabling these regulatory regions to describing the size of the body. come into contact with genes far away or even on dif- ferent chromosomes [69]. Non-coding variants such as 3′ UTR, 5′ UTR and Gene ontology and KEGG pathways intergenic variants were enriched for most of the traits Linear type traits are complex traits that are governed by in each breed. Downstream and upstream gene variants many genes each with a small efect, and hence, are likely were also enriched in some traits. In general, the SNPs involved in many biological systems. Several GO terms located close to and within the genes identifed as candi- were only associated with a single trait or a single breed; date genes were located within non-coding or regulatory hence there was limited commonality among traits or regions. For example, for DHQ in the CH breed, 60 sug- breeds suggesting the absence of a central biological pro- gestively and signifcantly associated SNPs were located cess that links these traits together. Over-represented GO within the MSTN gene; 10 of these were 3′UTR variants, terms in multiple traits and breeds include those related 31 were downstream gene variants and 19 were intronic. to skin development, collagen fbril organisation, and the Whereas regulatory regions may not have an efect on transforming growth factor beta receptor signalling path- the coding sequence of any gene, they are thought to be way. Each of these GO terms was associated with genes particularly important for growth and development in located in the large QTL on BTA2 that contained MSTN. humans [68, 69] and cattle [32, 70]. Tus, similar to pre- Excluding the major MSTN QTL in these breeds, which vious observations in humans and cattle, enrichment of is known to have a large efect on muscularity, the vari- the non-coding classes of SNPs in our study may indicate ous GO terms and KEGG pathways represented by the the importance of regulatory regions for cattle muscle genes associated with the muscularity traits suggest that development. the majority of genes identifed as signifcantly associated with a trait are not only breed-specifc but also trait-spe- cifc in many cases. Conclusions Although we identifed many QTL associated with mus- Regulatory regions involved in the development of muscle cularity in beef cattle, our results suggest that these QTL tend to be not only trait-specifc but also breed-specifc. Although millions of SNPs were tested for association Overall, the signifcant SNPs contained in these QTL with each trait, only 79 of the SNPs suggestively or sig- were more likely located in regulatory regions of genes, nifcantly associated with a trait were located in the which suggest the importance of non-coding regions coding region of a gene; the vast majority of the SNPs that may afect gene expression for muscle development associated with the muscularity traits in any of the in cattle. Some shared regions associated with muscu- breeds were located outside of the coding regions. Tis larity were found between CH and LM, with a large- is consistent with previous genomic studies for complex efect QTL on BTA2 containing MSTN being associated quantitative traits in cattle using HD SNP data [66] or with the fve traits analysed. Tis overlap between these sequence data [32]. While the coverage of the HD study breeds was somewhat expected, because they are sub- [66] may not have included the coding regions required jected to similar selection pressures. Apart from this sin- to identify signifcant associations within these regions, gle QTL, extensive diferences were observed between our study and a previous study on cattle stature [32] the breeds, which may be due to the much smaller sam- used imputed sequence data, and thus, covered the ple sizes for AA, HE, and SI compared to the CH and entire genome. LM populations that result in reduced power to detect Whereas many studies have previously acknowl- QTL or they may be due to diferences in genetic archi- edged the importance of non-coding SNPs to genetic tecture of these traits among the populations. In many variability, little is actually known about the mecha- cases, the strongly associated SNPs in one breed were nisms by which these SNPs contribute to variation in Doyle et al. Genet Sel Evol (2020) 52:2 Page 16 of 18

not segregating in the other breeds, and thus, were miss- Competing interests The authors declare that they have no competing interests. ing from the analyses. Knowledge of any potential difer- ences in genetic architecture among breeds is important Author details 1 to develop accurate genomic prediction equations in Teagasc, Animal and Grassland Research and Innovation Centre, Moore- park, Fermoy, Co. Cork, Ireland. 2 Department of Science, Waterford Institute across-breed analyses. of Technology, Cork Road, Waterford, Co. Waterford, Ireland. 3 Animal Breeding and Genomics Centre, Wageningen University and Research Centre, Livestock Supplementary information Research, Wageningen, The Netherlands. 4 Irish Cattle Breeding Federation, Bandon, Co. Cork, Ireland. Supplementary information accompanies this paper at https​://doi. org/10.1186/s1271​1-020-0523-1. Received: 5 May 2019 Accepted: 17 January 2020

Additional fle 1: Table S1. Number of records, and mean, and standard deviation of each linear type trait within each breed.

Additional fle 2: Figure S1. Manhattan plots for development of hind References quarters in (a) Angus, (b) Charolais, (c) Hereford, (d) Limousin, (e) Sim- 1. Veerkamp RF, Brotherstone S. Genetic correlations between linear type mental, and (f) meta-analysis. Figure S2. Manhattan plots for develop- traits, food intake, live weight and condition score in Holstein Friesian ment of inner thigh in (a) Angus, (b) Charolais, (c) Hereford, (d) Limousin, dairy cattle. Anim Sci. 1997;64:385–92. (e) Simmental, and (f) meta-analysis. Figure S3. Manhattan plots for 2. Berry DP, Buckley R, Dillon P, Evans RD, Veerkamp RF. Genetic relationships development of loin in (a) Angus, (b) Charolais, (c) Hereford, (d) Limousin, among linear type traits, milk yield, body weight, fertility and somatic cell (e) Simmental, and (f) meta-analysis. Figure S4. Manhattan plots for thigh count in primiparous dairy cows. Ir J Agric Food Res. 2004;43:161–76. width in (a) Angus, (b) Charolais, (c) Hereford, (d) Limousin, (e) Simmental, 3. Kern EL, Cobuci JA, Costa CN, McManus CM, Braccini J. Genetic associa- and f) Meta-Analysis. Description Manhattan plots for thigh width in each tion between longevity and linear type traits of Holstein cows. Sci Agric. of the 5 breeds and the meta- analysis. Figure S5. Manhattan plots for 2015;72:203–9. width of withers in (a) Angus, (b) Charolais, (c) Hereford, (d) Limousin, (e) 4. Mc Hugh N, Fahey A, Evans RD, Berry DP. Factors associated with selling Simmental, and (f) meta-analysis. price of cattle at livestock marts. Animal. 2010;4:1378–89. Additional fle 3: Figure S6. Overlapping 1-kb regions that contain at 5. Mazza S, Guzzo N, Sartori C, Berry DP, Mantovani R. Genetic parameters least one suggestive or signifcant SNP for the fve muscular traits in (a) for linear type traits in the Rendena dual-purpose breed. J Anim Breed Angus, (b) Charolais, (c) Hereford, (d) Limousin and (e) Simmental. Venn Genet. 2014;131:27–35. Diagram of overlapping 1-kb regions containing at least one suggestive or 6. Forabosco F, Bozzi R, Boettcher P, Filippini F, Bijma P, Van Arendonk J. Rela- signifcant SNP in each of the fve breeds. tionship between proftability and type traits and derivation of economic values for reproduction and survival traits in Chianina beef cows. J Anim Additional fle 4. Location of the top fve most signifcant QTL associated Sci. 2005;83:2043–51. with each of the traits in the meta-analysis containing all fve breeds. 7. Doyle JL, Berry DP, Walsh SW, Veerkamp RF, Evans RD, Carthy TR. Genetic covariance components within and among linear type traits difer Acknowledgements among contrasting beef cattle breeds. J Anim Sci. 2018;96:1628–39. This work was in part funded by a research grant from Science Foundation 8. Mukai F, Oyama K, Kohno S. Genetic relationships between performance Ireland award number 14/IA/2576 and as well as a research grant from Science test traits and feld carcass traits in Japanese Black cattle. Livest Prod Sci. Foundation Ireland and the Department of Agriculture, Food and Marine on 1995;44:199–205. behalf of the Government of Ireland under the Grant 16/RC/3835 (VistaMilk; 9. Conroy SB, Drennan MJ, Kenny DA, McGee M. The relationship of various Dublin, Ireland).The authors would also like to thank the 1000 Bull Genomes muscular and skeletal scores and ultrasound measurements in the live consortium for the reference population used to impute genotypes to animal, and carcass classifcation scores with carcass composition and sequence. value of bulls. Livest Sci. 2010;127:11–21. 10. McClure MC, Morsci NS, Schnabel RD, Kim JW, Yao P, Rolf MM, et al. A Authors’ contributions genome scan for quantitative trait loci infuencing carcass, post-natal JLD, DPB, RFV, and DCP participated in the design of the study. JLD performed growth and reproductive traits in commercial Angus cattle. Anim Genet. the analyses and wrote the frst draft of the paper. JLD, DPB, RFV, and DCP 2010;41:597–607. were involved in the interpretation of the results. All authors read and 11. Bolormaa S, Neto LR, Zhang YD, Bunch RJ, Harrison BE, Goddard ME, et al. approved the fnal manuscript. A genome-wide association study of meat and carcass traits in Australian cattle. J Anim Sci. 2011;89:2297–309. Funding 12. Nishimura S, Watanabe T, Mizoshita K, Tatsuda K, Fujita T, Watanabe N, Funding from the Science Foundation Ireland principal investigator award et al. Genome-wide association study identifed three major QTL for grant (SF 14/IA/2576) and Research Centres Award (16/RC/3835) is greatly carcass weight including the PLAG1-CHCHD7 QTN for stature in Japanese appreciated. Black cattle. BMC Genet. 2012;13:40. 13. Vallée A, Daures J, van Arendonk JA, Bovenhuis H. Genome-wide Availability of data and materials association study for behavior, type traits, and muscular development in Sequence variant genotypes were provided by participation in the 1000 Bulls Charolais beef cattle. J Anim Sci. 2016;94:2307–16. consortium and a subset of the sequences can be found at NCBI BioProject 14. Wu X, Fang M, Liu L, Wang S, Liu J, Ding X, et al. Genome wide association PRJNA238491. studies for body conformation traits in the Chinese Holstein cattle popu- lation. BMC Genomics. 2013;14:897. Ethics approval and consent to participate 15. Saatchi M, Schnabel RD, Taylor JF, Garrick DJ. Large-efect pleiotropic or Ethics approval was not obtained for the present study since the data were closely linked QTL segregate within and across ten US cattle breeds. BMC obtained from the existing Irish Cattle Breeding Federation (ICBF) national Genomics. 2014;15:442. database (http://www.icbf.com). 16. Berry DP, Evans RD. Genetics of reproductive performance in seasonal calving beef cows and its association with performance traits. J Anim Sci. Consent for publication 2014;92:1412–22. Not applicable. 17. Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml user guide release 3.0. Hemel Hempstead: VSN International Ltd; 2009. Doyle et al. Genet Sel Evol (2020) 52:2 Page 17 of 18

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