Genome‐wide association study of cell‐mediated immune response in chicken

Cell‐mediated immunity (CMI) causes the intracellular destruction of the antigen or elimination of the host cell to make animals resistant against exogenous antigens and cancers. In this study, a genome‐wide association study (GWAS) was carried out to identify genomic regions associated with CMI in chicken using chicken 60k high‐density single nucleotide polymorphism (SNP) array. Genomic relationships were taken into account to adjust for population structure. In order to account for multiple testing, chromosome‐wise false discovery rate was controlled at 5% and 10% levels. Moreover, a comparison of the power of fixed and mixed linear models based on genomic inflation factor was carried out. Mixed linear model (MLM) had better inflation rate, and therefore the results from MLM were used for subsequent analysis. Three significantly associated SNPs (FDR < 0.05) on chromosome 24 and linkage group E22C19W28_E50C23, and three suggestively associated SNPs (FDR < 0.1) on chromosome 1, 5 and 16 were identified. Pathway analysis showed that two biological pathways, which are related to immune response, were strongly associated with the candidate genes surrounding identified SNPs, and their influences were mostly on antigen processing and presentation, and cellular structure.     

A genome‐wide SNP scan in a porcine Large White × Minzhu intercross population reveals a locus influencing muscle mass on chromosome 2

A high‐density single nucleotide polymorphism (SNP) array containing 62 163 markers was employed for a genome‐wide association study (GWAS) to identify variants associated with lean meat in ham (LMH, %) and lean meat percentage (LMP, %) within a porcine Large White × Minzhu intercross population. For each individual, LMH and LMP were measured after slaughter at the age of 240 ± 7 days. A total of 557 F2 animals were genotyped. The GWAS revealed that 21 SNPs showed significant genome‐wide or chromosome‐wide associations with LMH and LMP by the Genome‐wide Rapid Association using Mixed Model and Regression‐Genomic Control approach. Nineteen significant genome‐wide SNPs were mapped to the distal end of Sus Scrofa Chromosome (SSC) 2, where a major known gene responsible for muscle mass, IGF2 is located. A conditioned analysis, in which the genotype of the strongest associated SNP is included as a fixed effect in the model, showed that those significant SNPs on SSC2 were derived from a single quantitative trait locus. The two chromosome‐wide association SNPs on SSC1 disappeared after conditioned analysis suggested the association signal is a false association derived from using a F2 population. The present result is expected to lead to novel insights into muscle mass in different pig breeds and lays a preliminary foundation for follow‐up studies for identification of causal mutations for subsequent application in marker‐assisted selection programs for improving muscle mass in pigs.     

A genome‐wide association study for fat‐related traits computed by image analysis in Japanese Black cattle

The objective of this study was to identify genomic regions associated with fat‐related traits using a Japanese Black cattle population in Hyogo. From 1836 animals, those with high or low values were selected on the basis of corrected phenotype and then pooled into high and low groups (n = 100 each), respectively. DNA pool‐based genome‐wide association study (GWAS) was performed using Illumina BovineSNP50 BeadChip v2 with three replicate assays for each pooled sample. GWAS detected that two single nucleotide polymorphisms (SNPs) on BTA7 (ARS‐BFGL‐NGS‐35463 and Hapmap23838‐BTA‐163815) and one SNP on BTA12 (ARS‐BFGL‐NGS‐2915) significantly affected fat percentage (FAR). The significance of ARS‐BFGL‐NGS‐35463 on BTA7 was confirmed by individual genotyping in all pooled samples. Moreover, association analysis between SNP and FAR in 803 Japanese Black cattle revealed a significant effect of SNP on FAR. Thus, further investigation of these regions is required to identify FAR‐associated genes and mutations, which can lead to the development of DNA markers for marker‐assisted selection for the genetic improvement of beef quality.     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

Genome‐wide association study for feedlot average daily gain in Nellore cattle (Bos indicus)

The genome‐wide association study (GWAS) results are presented for average daily gain (ADG) in Nellore cattle. Phenotype of 720 male Bos indicus animals with information of ADG in feedlots and 354 147 single‐nucleotide polymorphisms (SNPs) obtained from a database added by information from Illumina Bovine HD (777 962 SNPs) and Illumina BovineSNP50 (54 609) by imputation were used. After quality control and imputation, 290 620 SNPs remained in the association analysis, using R package Genome‐wide Rapid Association using Mixed Model and Regression method GRAMMAR‐Gamma. A genomic region with six significant SNPs, at Bonferroni‐corrected significance, was found on chromosome 3. The most significant SNP (rs42518459, BTA3: 85849977, p = 9.49 × 10^?8) explained 5.62% of the phenotypic variance and had the allele substitution effect of ?0.269 kg/day. Important genes such as PDE4B, LEPR, CYP2J2 and FGGY are located near this region, which is overlapped by 12 quantitative trait locus (QTLs) described for several production traits. Other regions with markers with suggestive effects were identified in BTA6 and BTA10. This study showed regions with major effects on ADG in Bos indicus in feedlots. This information may be useful to increase the efficiency of selecting this trait and to understand the physiological processes involved in its regulation.     

Detection of candidate polymorphisms around the QTL for fat area ratio to rib eye area on BTA7 using whole‐genome resequencing in Japanese Black cattle

In our previous study, we performed genome‐wide association study (GWAS) to identify the genomic region associated with Fat area ratio to rib eye area (FAR) and detected a candidate in BTA7 at 10–30 Mbp. The present study aims to comprehensively detect all polymorphisms in the candidate region using whole‐genome resequencing data. Based on whole‐genome resequencing of eight animals, we detected 127,090 polymorphisms within the region. Of these, 31,945 were located within the genes. We further narrowed the polymorphisms to 6,044 with more than five allele differences between the high and low FAR groups that were located within 179 genes. We subsequently investigated the functions of these genes and selected 170 polymorphisms in eight genes as possible candidate polymorphisms. We focused on SLC27A6 K81M as a putative candidate polymorphism. We genotyped the SNP in a Japanese Black population (n = 904) to investigate the effect on FAR. Analysis of variance revealed that SLC27A6 K81M had a lower p‐value (p = .0009) than the most significant SNP in GWAS (p = .0049). Although only SLC27A6 K81M was verified in the present study, subsequent verification of the remaining candidate genes and polymorphisms could lead to the identification of genes and polymorphisms responsible for FAR.     

Genotypes and allele frequencies of buried SNPs in a bovine single‐nucleotide polymorphism array in Japanese Black cattle

Single nucleotide polymorphism (SNP) arrays are widely used for genetic and genomic analyses in cattle breeding; thus, data derived from SNP arrays have accumulated on a large scale nationwide. Commercial SNP arrays contain a considerable number of unassigned SNPs on the chromosome/position on the genome; these SNPs are excluded in subsequent analyses. Notably, the position‐unassigned SNPs, or “buried SNPs” include some of the markers associated with genetic disease. In this study, we identified the position of buried SNPs using the Basic Local Alignment Search Tool against the surrounding sequences and characterized the relationship between SNPs and genetic diseases in Online Mendelian Inheritance in Animals based on the genomic position. We determined the position of 285 buried SNPs on the genome and surveyed the genotype and allele frequencies of these SNPs in 5,955 individual Japanese Black cattle. Eleven SNPs associated with genetic disease, which contained five buried SNPs, were found in the population with the risk allele frequency ranging from 0.00008396 to 0.46. These results indicate that buried SNPs in the bovine SNP array can be utilized to identify associations with genetic disorders from large scale accumulated SNP genotype data in Japanese Black cattle.     

Genetic and genomic analyses for predicted methane‐related traits in Japanese Black steers

The objectives of this study were to estimate genetic parameters and to perform a genome‐wide association study (GWAS) for predicted methane‐related traits in Japanese Black steers. The methane production and yield traits were predicted using on‐farm measurable traits, such as dry matter intake and average daily gain. A total of 4,578 Japanese Black steers, which were progenies of 362 sires genotyped with imputed 551,995 single nucleotide polymorphisms (SNPs), had phenotypes of predicted methane‐related traits during the total fattening period (52 weeks). For the estimation of genetic parameters, the estimated heritabilities were moderate (ranged from 0.57 to 0.60). In addition, the estimated genetic correlations of methane production traits with most of carcass traits and feed‐efficiency traits were unfavorable, but those of methane yield traits were favorable or low. For the GWAS, no genome‐wide significant SNP was detected, but a total of four quantitative trait locus (QTL) regions that explained more than 5.0% of genetic variance were localized on the genome, and some candidate genes associated with growth and feed‐efficiency traits were located on the regions. Our results suggest that the predicted methane‐related traits are heritable and some QTL regions for the traits are localized on the genome in Japanese Black steers.     

Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle

Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.     

Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms

There is increasing use of dense single nucleotide polymorphisms (SNPs) for whole‐genome association studies (WGAS) in livestock to map and identify quantitative trait loci (QTL). These studies rely on linkage disequilibrium (LD) to detect an association between SNP genotypes and phenotypes. The power and precision of these WGAS are unknown, and will depend on the extent of LD in the experimental population. One complication for WGAS in livestock populations is that they typically consist of many paternal half‐sib families, and in some cases full‐sib families; unless this subtle population stratification is accounted for, many spurious associations may be reported. Our aim was to investigate the power, precision and false discovery rates of WGAS for QTL discovery, with a commercial SNP array, given existing patterns of LD in cattle. We also tested the efficiency of selective genotyping animals. A total of 365 cattle were genotyped for 9232 SNPs. We simulated a QTL effect as well as polygenic and environmental effects for all animals. One QTL was simulated on a randomly chosen SNP and accounted for 5%, 10% or 18% of the total variance. The power to detect a moderate‐sized additive QTL (5% of the phenotypic variance) with 365 animals genotyped was 37% (p < 0.001). Most importantly, if pedigree structure was not accounted for, the number of false positives significantly increased above those expected by chance alone. Selective genotyping also resulted in a significant increase in false positives, even when pedigree structure was accounted for.     

Identification of the putative ancestral allele of bovine single‐nucleotide polymorphisms

Identifying the action of natural selection from patterns of standing genetic variation has long been of interest to the population genetic community. Thanks to the availability of large single‐nucleotide polymorphism (SNP) data sets for many species and of high‐throughput SNP genotyping methods, whole‐genomic surveys to detect selective sweeps are now possible. Knowing the ancestral allele increases the power to detect selection. We present here a comparative genomic approach to determine the putative ancestral allele of bovine SNPs deposited in public databases. We analysed 19 551 488 SNPs and identified the putative ancestral allele for 14 339 107 SNPs. Our predicted ancestral alleles were in agreement with ancestral alleles detected by genotyping outgroup species for 97% SNPs from the BovineSNP50 BeadChip. This comparison indicates that our comparative genomic‐based approach to identify putative ancestral alleles is reliable.     

A genome‐wide association study for canine cryptorchidism in Siberian Huskies

Cryptorchidism is a condition whereby one or both testes fail to descend into the scrotal sac. Here, we performed a genome‐wide association study (GWAS) with both a case–control analysis using the GEMMA software accounting for population structure and a BayesB approach in the GenSel software applied to every 1 Mb window of SNPs or haplotypes. The haplotypes were constructed from a genealogical tree using the population of 204 Siberian Huskies. The BayesB analyses identified six putative genomic candidate regions on CFA6, 9, 24, 27 and X. These regions explained a high percentage of genetic variance when compared with other genomic regions. The positional candidate genes Q9TSI5_CANFA (matrix metalloproteinase 9 precursor) on CFA24, ADAMTS20 (ADAM metallopeptidase with thrombospondin type 1 motif, 20) on CFA27 and MID1IP1 (MID1 interacting protein 1) on CFAX are known to be functionally related to extracellular matrix remodelling, which might be important for gubernaculum elongation and thus interrupting normal testicular descent. Further mutation screening in these candidate regions on CFA6, 9, 24, 27 and X is needed. Next generation sequencing will help to uncover rare variants associated with cryptorchidism in this dog population.     

An imputation‐based genome‐wide association study on traits related to male reproduction in a White Duroc × Erhualian F2 population

Boar reproductive traits are economically important for the pig industry. Here we conducted a genome‐wide association study (GWAS) for 13 reproductive traits measured on 205 F₂ boars at day 300 using 60 K single nucleotide polymorphism (SNP) data imputed from a reference panel of 1200 pigs in a White Duroc × Erhualian F₂ intercross population. We identified 10 significant loci for seven traits on eight pig chromosomes (SSC). Two loci surpassed the genome‐wide significance level, including one for epididymal weight around 60.25 Mb on SSC7 and one for semen temperature around 43.69 Mb on SSC4. Four of the 10 significant loci that we identified were consistent with previously reported quantitative trait loci for boar reproduction traits. We highlighted several interesting candidate genes at these loci, including APN, TEP1, PARP2, SPINK1 and PDE1C. To evaluate the imputation accuracy, we further genotyped nine GWAS top SNPs using PCR restriction fragment length polymorphism or Sanger sequencing. We found an average of 91.44% of genotype concordance, 95.36% of allelic concordance and 0.85 of r² correlation between imputed and real genotype data. This indicates that our GWAS mapping results based on imputed SNP data are reliable, providing insights into the genetic basis of boar reproductive traits.     

Genome‐wide association studies reveal additional related loci for fatty acid composition in a Duroc pig multigenerational population

The aim of the present study was to detect quantitative trait loci affecting fatty acid composition in back fat and intramuscular fat in a Duroc pig population comprising seventh‐generation pedigrees using genome‐wide association studies (GWAS). In total, 305 animals were genotyped using single nucleotide polymorphisms (SNPs) array and five selected SNPs from regions containing known candidate genes related to fatty acid synthesis or metabolism. In total, 24 genome‐wide significant SNP regions were detected in 12 traits, and 76 genome‐wide suggestive SNP regions were detected in 33 traits. The Sus scrofa chromosome (SSC) 7 at 10.3 Mb was significantly associated with C17:0 in intramuscular fat, while the SSC9 at 13.6 Mb was significantly associated with C14:0 in intramuscular fat. The SSC12 at 1.0 Mb was significantly associated with C14:0 in back fat and the SSC14 at 121.0 Mb was significantly associated with C18:0 in intramuscular fat. These regions not only replicated previously reported loci containing some candidate genes involved in fatty acid composition (fatty acid synthase and stearoyl‐CoA desaturase) but also included several additional related loci.     

Genomewide association for a dominant pigmentation gene in sheep

Most published genomewide association studies (GWAS) in sheep have investigated recessively inherited monogenic traits. The objective here was to assess the feasibility of performing GWAS for a dominant trait for which the genetic basis was already known. A total of 42 Manchega and Rasa Aragonesa sheep that segregate solid black or white coat pigmentation were genotyped using the SNP50 BeadChip. Previous analysis in Manchegas demonstrated a complete association between the pigmentation trait and alleles of the MC1R gene, setting an a priori expectation for GWAS. Multiple methods were used to identify and quantify the strength of population substructure between black and white animals, before allelic association testing was performed for 49 034 SNPs. Following correction for substructure, GWAS identified the most strongly associated SNP (s26449) was also the closest to the MC1R gene. The finding was strongly supported by the permutation tree‐based random forest (RF) analysis. Importantly, GWAS identified unlinked SNP with only slightly lower p‐values than for s26449. Random forest analysis indicated these were false positives, suggesting interpretation based on both approaches was beneficial. The results indicate that a combined analytical approach can be successful in studies where a modest number of animals are available and substantial population stratification exists.     

Assessment of bagging GBLUP for whole‐genome prediction of broiler chicken traits

Bootstrap aggregation (bagging) is a resampling method known to produce more accurate predictions when predictors are unstable or when the number of markers is much larger than sample size, because of variance reduction capabilities. The purpose of this study was to compare genomic best linear unbiased prediction (GBLUP) with bootstrap aggregated sampling GBLUP (Bagged GBLUP, or BGBLUP) in terms of prediction accuracy. We used a 600 K Affymetrix platform with 1351 birds genotyped and phenotyped for three traits in broiler chickens; body weight, ultrasound measurement of breast muscle and hen house egg production. The predictive performance of GBLUP versus BGBLUP was evaluated in different scenarios consisting of including or excluding the TOP 20 markers from a standard genome‐wide association study (GWAS) as fixed effects in the GBLUP model, and varying training sample sizes and allelic frequency bins. Predictive performance was assessed via five replications of a threefold cross‐validation using the correlation between observed and predicted values, and prediction mean‐squared error. GBLUP overfitted the training set data, and BGBLUP delivered a better predictive ability in testing sets. Treating the TOP 20 markers from the GWAS into the model as fixed effects improved prediction accuracy and added advantages to BGBLUP over GBLUP. The performance of GBLUP and BGBLUP at different allele frequency bins and training sample sizes was similar. In general, results of this study confirm that BGBLUP can be valuable for enhancing genome‐enabled prediction of complex traits.     

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium‐density single nucleotide polymorphism (SNP) array. Here, the impact of lower‐density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)‐flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree‐based prediction (0.50–0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL‐flanking SNP (0.65–0.72) was similar to the panel with 35K SNP (0.65–0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r² ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long‐range LD likely contributed to the accurate genomic predictions with the low‐density SNP panels. Population structure analysis supported the hypothesis that long‐range LD in this population may be caused by admixture. Results suggest that lower‐cost, low‐density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.     

Combined use of principal component analysis and random forests identify population‐informative single nucleotide polymorphisms: application in cattle breeds

The genetic identification of the population of origin of individuals, including animals, has several practical applications in forensics, evolution, conservation genetics, breeding and authentication of animal products. Commercial high‐density single nucleotide polymorphism (SNP) genotyping tools that have been recently developed in many species provide information from a large number of polymorphic sites that can be used to identify population‐/breed‐informative markers. In this study, starting from Illumina BovineSNP50 v1 BeadChip array genotyping data available from 3711 cattle of four breeds (2091 Italian Holstein, 738 Italian Brown, 475 Italian Simmental and 407 Marchigiana), principal component analysis (PCA) and random forests (RFs) were combined to identify informative SNP panels useful for cattle breed identification. From a PCA preselected list of 580 SNPs, RFs were computed using ranking methods (Mean Decrease in the Gini Index and Mean Accuracy Decrease) to identify the most informative 48 and 96 SNPs for breed assignment. The out‐of‐bag (OOB) error rate for both ranking methods and SNP densities ranged from 0.0 to 0.1% in the reference population. Application of this approach in a test population (10% of individuals pre‐extracted from the whole data set) achieved 100% of correct assignment with both classifiers. Linkage disequilibrium between selected SNPs was relevant (r² > 0.6) only in few pairs of markers indicating that most of the selected SNPs captured different fractions of variance. Several informative SNPs were in genes/QTL regions that affect or are associated with phenotypes or production traits that might differentiate the investigated breeds. The combination of PCA and RF to perform SNP selection and breed assignment can be easily implemented and is able to identify subsets of informative SNPs useful for population assignment starting from a large number of markers derived by high‐throughput genotyping platforms.     

Genomewide analysis of bull sperm quality and fertility traits

Because the priority of AI industry is to identify subfertile bulls, a predictive model that allowed for the prediction of 91% bulls of low fertility was implemented based on seminological (motility) parameters and DNA status assessed both as DNA fragmentation index (DFI) and by TUNEL assay using sperm of 105 Holstein–Friesian bulls (four batches per bull) selected based on in vivo estimated relative conception rates (ERCR). Thereafter, sperm quality and male fertility traits of bulls were explored by GWAS using a high‐density (777K) Illumina chip. After data editing, 85 bulls and 591,988 SNPs were retained for GWAS. Of 12 SNPs with false discovery rate <0.2, four SNPs located on BTA28 and BTA18 were significantly associated (LD‐adjusted Bonferroni <0.05) with the non‐compensatory sperm parameters DFI and TUNEL. Other SNPs of interest for potential association with TUNEL were found on BTA3, in the same chromosome where associations with non‐compensatory in vivo bull fertility were already reported. Further suggestive SNPs for sperm membrane integrity were located on BTA28, the chromosome where QTL studies previously reported associations with sperm quality traits. Suggestive SNPs for ERCR were found on BTA18 in the vicinity of a site already associated with in vivo bull fertility. Additional SNPs associated with ERCR and sperm kinetic parameters were also identified. In contrast to other, but very few GWAS on fertility traits in bovine spermatozoa, which reported significant SNPs located on BTX, we have not identified SNPs of interest in this sexual chromosome.     

Genome‐wide association studies revealed candidate genes for tail fat deposition and body size in the Hulun Buir sheep

Fat‐tailed sheep have a unique characteristic of depositing fat in their tails. In the present study, we conducted genome‐wide association studies (GWAS) on traits related to tail fat deposition and body size in the Hulun Buir sheep. A total number of 300 individuals belonging to two fat‐tailed lines of the Hulun Buir sheep breed genotyped with the Ovine Infinium HD SNP BeadChip were included in the current study. Two mixed models, one for continuous and one for binary phenotypic traits, were employed to analyse ten traits, that is, body length (BL), body height (BH), chest girth (CG), tail length (TL), tail width (TW), tail circumference (TC), carcass weight (CW), tail fat weight (TF), ratio of CW to TF (RCT) and tail type (TT). We identified 7, 6, 7, 2, 10 and 1 SNPs significantly associated with traits TF, CW, RCT, TW, TT and CG, respectively. Their associated genomic regions harboured 42 positional candidate genes. Out of them, 13 candidate genes including SMURF2, FBF1, DTNBP1, SETD7 and RBM11 have been associated with fat metabolism in sheep. The RBM11 gene has already been identified in a previous study on signatures of selection in this specific sheep population. Two more genes, that is, SMARCA5 and GAB1 were associated with body size in sheep. The present study has identified candidate genes that might be implicated in tail fat deposition and body size in sheep.