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Asian-Australas J Anim Sci > Volume 30(1); 2017 > Article
Li, Gao, Kim, Iqbal, and Kim: A whole genome association study to detect additive and dominant single nucleotide polymorphisms for growth and carcass traits in Korean native cattle, Hanwoo

Abstract

Objective

A whole genome association study was conducted to identify single nucleotide polymorphisms (SNPs) with additive and dominant effects for growth and carcass traits in Korean native cattle, Hanwoo.

Methods

The data set comprised 61 sires and their 486 Hanwoo steers that were born between spring of 2005 and fall of 2007. The steers were genotyped with the 35,968 SNPs that were embedded in the Illumina bovine SNP 50K beadchip and six growth and carcass quality traits were measured for the steers. A series of lack-of-fit tests between the models was applied to classify gene expression pattern as additive or dominant.

Results

A total of 18 (0), 15 (3), 12 (8), 15 (18), 11 (7), and 21 (1) SNPs were detected at the 5% chromosome (genome) - wise level for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area (LMA) and marbling score, respectively. Among the significant 129 SNPs, 56 SNPs had additive effects, 20 SNPs dominance effects, and 53 SNPs both additive and dominance effects, suggesting that dominance inheritance mode be considered in genetic improvement for growth and carcass quality in Hanwoo. The significant SNPs were located at 33 quantitative trait locus (QTL) regions on 18 Bos Taurus chromosomes (i.e. BTA 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 20, 23, 26, 28, and 29) were detected. There is strong evidence that BTA14 is the key chromosome affecting CWT. Also, BTA20 is the key chromosome for almost all traits measured (WWT, YWT, LMA).

Conclusion

The application of various additive and dominance SNP models enabled better characterization of SNP inheritance mode for growth and carcass quality traits in Hanwoo, and many of the detected SNPs or QTL had dominance effects, suggesting that dominance be considered for the whole-genome SNPs data and implementation of successive molecular breeding schemes in Hanwoo.

INTRODUCTION

In recent years, whole genome association study (WGAS) or genomic selection (GS) has been a main focus on livestock breeding due to the expectation of increased accuracy of selection and reduced generation interval, compared to traditional breeding approaches such as progeny testing. WGAS or GS has been enabled by high-density single nucleotide polymorphism (SNP) chips, which allows identification of causative SNPs for economic traits and improves reliability of breeding value prediction.
Single-marker association analysis with density SNP array has similar or greater power to detect quantitative trait loci (QTLs) and provides more precise QTL locations than haplotype-based or identity-by-descent based models [1]. Further, the method offers a greater flexibility of including dominance or epistatic effects in the model, enabling better characterization of the QTL in terms of inheritance mode of gene action. Indeed, some WGAS approaches have been routinely practiced in dairy and beef cattle to detect SNPs for milk production, or meat and carcass, respectively [24].
A Korean native cattle, Hanwoo (Bos taurus coreanae), was used as draft animals and suppliers of organic fertilizer. However, as agricultural technologies became improved and beef consumption increased, Hanwoo became more important as beef cattle. The Korean cattle had low meat production efficiency because of low milk production and slow growth rate, while having a relatively high reproductive rate and high marbling, e.g. 15% to 23% of intramuscular fat at final slaughter [5]. It has been about 30 years since Hanwoo improvement program was implemented, in order to increase meat production and quality to meet the growing demand for beef in Korea [6].
Interactions between genes at the same locus are called dominance. Dominance effect has often been ignored or treated as a nuisance parameter in genetic evaluations of livestock and quantification of variance components [7]. However, quantification of dominance effects underlying complex traits is needed due to increasing evidence of the major effects of dominant QTL on human disease and agricultural traits of economic importance [8,9]. Recently, a number of studies showing that accounting for dominance effects increased the accuracy and reduced the bias of genomically-predicted breeding values in comparison to an additive model. Su et al [10] reported that in a purebred Duroc population the dominance variance accounted for 6% of the total phenotypic variance in daily gain, emphasizing the relevance of dominance [10]. Therefore, an increasing need for WGAS methodology to routinely investigate the non-additive effects such as dominance. Herein, we performed a WGAS to identify SNPs with dominance and/or additive effects that were associated with growth and carcass traits, specifically to investigate the importance of dominance in Korean beef cattle, Hanwoo.

MATERIALS AND METHODS

Animals, phenotypes and SNPs

The Hanwoo data were collected from 486 steers that were used for progeny testing in Hanwoo Improvement Center of National Agricultural Cooperative Federation in Seosan, Korea. The steers were born between spring of 2005 and fall of 2007, sired by 61 Hanwoo bulls (paternal halfsibs). The number of steers for each sire ranged from two to 13 with the average of eight steers.
The steers were weaned at 5 or 6 months of age, and each group of 10 steers were raised in a pen. The feeding program was divided into early, middle, and late stage, each with a six months interval. In the early and middle stages, the steers were fed with concentrates with the amount of 1.8% of the body weight and ad libitum in the late stage. The concentrates were composed of 15%, 13%, and 11% of crude protein, and 71%, 72%, and 73% total digestible nutrients (TDN), in the respective feeding stages. Roughages with 4.5% crude protein and 37.5 TDN were offered ad labium with other additives such as vitamin and minerals. All steers were slaughtered at approximately 24 months of age. Traits measured were weaning weight (WWT) at approximately six months of age, 365-d yearling weight (YWT) and carcass weight (CWT) at 24 hours after slaughter. The carcasses were dissected at the last rib and the first lumber vertebra according to the Animal Product Grading System of Korea to measure backfat thickness (BFT), longissimus dorsi muscle area (LMA), and marbling score (Marb). The Marb score was numbered as 1 through 9 according to the Korean Beef Marbling Standard (1 = trace, 9 = very abundant). Details about measurement of the traits and management practices were described in Lee et al [2]. The statistics for phenotypic data used in this study are summarized in Table 1.
DNA of the sires and their steers were quantified and genotyped using the Illumina bovine SNP50K beadchip. Details on the SNP genotyping procedure were described in Lee et al [2]. Between the sires and their sons (steers), Mendelian consistency was checked, i.e. any discordance of SNP genotypes between them. If more than 1,000 SNPs were discordant, then the steer was removed. Also, the individual with more than 10% missing genotypes was discarded.
The Illumina bovine SNP50 beadchip assay contained 54,001 SNPs with an average distance interval of 51.5 kb. Criteria for SNP removal were: i) more than 5% pedigree discordant (e.g. cases where a sire was homozygous for one allele and progeny were homozygous for the other allele), ii) less than 90% call rate, iii) monomorphic SNPs or when the minor allele frequency was smaller than 0.05, iv) proportion of individuals with genotype completeness was smaller than 90%, v) markers with significant departure from Hardy-Weinberg equilibrium (p<0.00001), vi) assigned to X chromosome or not assigned to any chromosome on Bovine Genome Build 4.0.
Missing genotypes were imputed using Druet method [11]. Haplotypes were first partially reconstructed based on genotypes of relatives, i.e. halfsib steers and their sires (based on Mendelian segregation rules and linkage information) with the program, LinkPHASE (Liège, Belgium). Then, DAGPHASE (Liège, Belgium) and Beagle (Seattle, WA, USA) with scale and shift parameter set equal to 2.0 and 0.1, respectively, were iteratively used to estimate the parameters of a directed acyclic graph. The programs were run first with genotypes from animals genotyped for all markers for 10 iterations and then with genotypes from all animals for 10 more iterations. The accuracy of imputing genotypes was ranged from 0.81 to 0.96 [11].

Whole genome association analysis

A mixed-inheritance animal model was used to detect SNP with additive or dominance effects for growth and carcass quality of Hanwoo. The ‘snp_a’, ‘snp_d’, and ‘snp_ad’ options of Qxpak software (Barcelona, Spain) were applied for each SNP. For each trait, appropriate fixed factors or covariates were fitted in the models (p<0.05) using a general linear model procedure in SAS (SAS 9.1, SAS Institute Inc., Cary, NC, USA).
Firstly, the additive and dominance expression model (Add+Dom) was chosen as a base model:
yi=μ+jβjcij+SNPad_i+ui+ei
Where yi is the phenotypic record of animal i, μ is the average phenotypic performance, cji is the value of the jth covariate or fixed effect for the animal i, βj is an estimate of the jth fixed effect or covariate, SNPad_i is the additive and dominance effect of SNP for animal i (e.g. individuals with marker genotypes ‘11’, ‘12’, and ‘22’ are assumed to have genetic values μkAA, μkAB, and μkBB), ui is the infinitesimal genetic effect of animal i, which is distributed as N(0,A σu2) (the numerator relationship matrix A and the additive genetic variance σu2) and ei is a random residual for animal i, which is distributed as N(0,Iσe2)( identity matrix I and residual variance σe2). Two fixed effects were fitted in the models: year and season of birth (5 levels) for all the traits and region where the steers were born (41 levels) for WWT, YWT, and Marb, and a covariate was fitted; weaning age for WWT, yearling age for YWT, slaughter age for CWT, BFT, and LMA, was also fitted. Pedigrees of the base population animals were traced back for two generations to form the numerator relationship matrix, and 1,033 animals were included in the pedigree analysis.
The next models are the additive (Add) and dominance (Dom) expression models, and the null model:
Additive expression model:   yi=μ+jβjcij+SNPa_i+ui+eiDominance expression model:   yi=μ+jβjcij+SNPd_i+ui+eiNull model:   yi=μ+jβjcij+ui+ei
Where yi, μ, cji, βj, ui as described above, SNPa_i is the additive(a) effect of the SNP genotype values for animal i (e.g. individuals with marker genotypes ‘11’, ‘12’, and ‘22’ are assumed to have genetic values μkAA, 0, and μkBB), and SNPd_i is the dominance effect of the SNP for animal i (e.g. individuals with marker genotypes ‘11’, ‘12’, and ‘22’ are assumed to have genetic values 0, μkAB and 0).
A series of tests was applied to classify gene expression pattern; additive or dominant, following the decision trees (Figure 1):
If the Add+Dom Model vs the Null model was significant at the 5% chromosome-wise (ChW) level:
  1. If the Add+Dom model vs the Add model was significant and the Add+Dom model vs the Dom model was not significant at the 5% comparison wise level, then the SNP was defined to have dominance inheritance mode of gene action.

  2. If the Add+Dom model vs the Add model was not significant and the Add+Dom model vs the Dom model was significant at the 5% comparison wise level, then the SNP was defined to have additive inheritance mode of gene action.

  3. If the Add+Dom model vs the Add model and the Add+Dom model vs the Dom model were both significant or both not significant at the 5% comparison wise level, then the SNP was defined to have additive and dominance expressed.

If the Add+Dom model vs the Null model was not significant at the 5% ChW level:
  1. If the Add model vs the Null model was significant at the 5% ChW level, then the SNP was classified as additive expressed SNP.

  2. If the Dom model vs the Null model was significant at the 5% ChW level, then the SNP was classified as dominance expressed SNP.

All the models were fitted for each of the available SNPs. The log likelihood ratio tests (LRT) statistic was applied by comparing the log likelihoods between the full model and the reduced model:
LRT=-2(LogLikehoodreduced_model-LogLikehoodfull_model)
The LRT test statistics approximately followed chi-squared distributions with degree of freedom equal to the number of extra parameters (one or two) estimated in the full model compared with the reduced model.
False discovery rate (FDR) was used to set significant thresholds to account for multiple testing, by calculating q-value based on nominal p-value from LRT test statistic for each trait using the R packages ‘qvalue’. The FDR threshold was set at 5% genome-wise (GW) or a 5% ChW level.

Marking the quantitative trait loci region

Generally, clusters of significant SNPs are expected to locate in the vicinity of a QTL, because there is linkage disequilibrium between adjacent markers that are closely located or clusters of transcription factor binding sites potentially indicating groups of co-regulated genes. In this study, the ChromoScan software (Ann Arbor, USA) was used to get statistical evidence of both a clustering of the SNP locations and a clustering of P-values from the above three mixed-inheritance animal model [12]. In the QTL region, one or more SNPs must be detected, and the variance that was explained by the QTL was estimated with the most significant SNP. The genetic variance was obtained from the estimated genotype effects from the corresponding model and the observed genotype frequencies. The percentage of the total genetic variance explained by the significant SNP was also obtained.

RESULTS AND DISCUSSION

Available single nucleotide polymorphisms

Among the 54,001 SNPs in the Illumina bovine beadchip array, 35,968 SNPs were available for GWA tests. The SNPs covered 2543.6 Mb of the bovine genome with 70.6±69.0 kb average adjacent marker interval (Table 2). The largest gap between adjacent SNPs (2,081.5 kb) was located on BTA 10. Average observed heterozygosity for SNPs was 0.37±0.12, and average minor allele frequencies (MAF) of all SNPs before (after) editing was 0.20 (0.27). The SNPs genotyped showed an almost uniform distribution across the common frequency classes.

Whole genome association study

A total of 92 (37) significant SNPs were detected at 5% ChW (GW) level; 18 (0), 15 (3), 12 (8), 15 (18), 11 (7), and 21 (1) SNPs for WWT, YWT, CWT, BFT, LMA, and Marb respectively (Table 3). Among the significant SNPs, 45 (11) SNPs had additive mode of gene action, 18 (2) SNPs dominance, and 29 (24) had both additive and dominance mode of gene action. The detail description of the significant SNPs for the six growth and carcass quality traits, including their positions in the genome, additive and dominance effects, the estimated proportion of the phenotypic variance and known genes near the SNPs, are in Table 4 through 9.

Weaning weight (WWT)

Eighteen SNPs were detected at 5% ChW level (Table 4). Of the SNPs, nine, two and seven SNPs were declared to be additive, dominant and both additive and dominant, respectively. Two, five and four SNPs were detected in BTA1, 5 and 12, respectively. Six SNPs were located within the QTL regions for WWT identified in Angus cattle (McClure et al [13]), i.e. BTB-01747944, BTB-02105769, ARS-BFGL-NGS-35164, ARS-BFGL-NGS-5482, ARS-BFGL-BAC-11115, and ARS-BFGL-NGS-39382. Among the 18 SNPs, three SNPs on BTAs 5, 16 and 20 were located within the regions of known genes, i.e. LOC505861, PLCH2, and ADAMTS12 (Table 4).

Yearling weight (YWT)

A total of 18 significant SNP were detected for YWT, of which three SNPs at the 5% GW level (Table 5). Of the SNPs, six, seven, and five were declared as additive, dominant and both additive and dominant SNP, respectively. Five SNPs were located within the QTL regions for YWT in Angus cattle [13], i.e. ARS-BFGL-NGS-105590, ARS-BFGL-NGS-17747, ARS-BFGL-NGS-38840, Hapmap50009-BTA-50200, ARS-BFGL-NGS-42226. Among the 18 significant SNPs, seven SNPs were harbored within the regions of known genes (Table 5).

Carcass weight (CWT)

A total of 20 significant SNPs were detected, of which eight SNPs at 5% GW level. Of the significant SNPs for CWT, ten, one and nine SNPs had additive, dominance and both additive and dominance effects, respectively (Table 6). Fourteen SNPs for CWT were located within a 17.55 Mb (between 18.65 Mb and 36.10 Mb) on BTA14. Fifteen significant SNPs were located within the chromosomal regions where QTL for CWT were reported in previous studies of beef cattle [1317], and five SNPs for CWT were harbored within the regions of known genes (Table 6). McClure et al [13] reported a CWT QTL at 46 cM of BTA8 (95% confidence interval (CI) 41.6 to 66.0 cM) in Angus cattle, near which the SNP, BFGL-NGS-110568, was detected at 56.6 Mb in this study [13]. McClure et al [13] also found another CWT QTL at 84 cM of BTA10 (95% CI 79.0–100.0 cM), while we detected one CWT SNP, ARS-BFGL-NGS-54787, at 84.17 Mb of the same chromosome (Table 6) [13]. In the BTA14 region (18.7 to 36.1 Mb) where clusters of CWT SNP were detected, 13 SNPs that were associated with CWT were previously reported in Japanese black (Wagyu) cattle [4,15] as well as in Hanwoo [16]. Casas et al [14] reported one CWT QTL on BTA18 in a halfsib family sired by Brahman×Hereford cross, near which a GW significant SNP, ARS-BFGL-NGS-39866, was detected at 18.18 Mb (Table 6) [14].

Backfat thickness (BFT)

Thirty three SNPs for BFT were detected at 5% ChW level, of which 18 SNPs at 5% GW level. Of the significant SNPs for BFT, 16, one and 16 SNPs had additive, dominance and both additive and dominance effects, respectively (Table 7). There were three SNP clusters for BFT, i.e. four, five and three SNPs that were located within a 16.4 Mb segment (between 21.0 Mb and 37.4 Mb) on BTA14, a 44.1 Mb segment (between 28.3 Mb and 72.4 Mb) on BTA 16, and a 10.2 Mb segment (between 26.4 and 36.6 Mb) on BTA29, respectively. Eleven SNPs were harbored within the regions of known genes (Table 7), and in the chromosomal regions where twelve SNPs that were detected in this study, BFT QTL also reported in Hanwoo, Angus or composite breeds [13,14,18,19]; Hapmap51248-BTA-51337 on BTA1; BFGL-NGS-119673, ARS-BFGL-NGS-8401, and BTB-00236217 on BTA5; BTB-00236217 and BTB-01744782 on BTA6; Hapmap34906-BES11_Contig369_1053 on BTA11; BTB-01493007 on BTA13; BTB-00566332 on BTA14;BTB-00634483 and Hapmap42533-BTA38667 on BTA16; ARS-BFGL-NGS-39535 on BTA29 (Table 7).

Longissimus dorsi muscle area (LMA)

A total of 18 SNPs were detected, of which seven SNPs at 5% GW level. Seven, two and nine SNPs were declared to be additive, dominance and both additive and dominance SNP, respectively (Table 8). Six LMA SNPs (BTA-99819-no-rs on BTA3; BTB-01321253 on BTA7; BFGL-NGS-110568 on BTA8; BFGL-NGS-112221 and BFGL-NGS-113227 on BTA20; ARS-BFGL-NGS-35298 on BTA24) were located within the QTL regions for LMA that were reported in a WGAS of Angus cattle [13].

Marbling score (Marb)

A total of 22 SNPs were detected of which, eight, seven and seven had additive, dominance and both additive and dominance effects, respectively (Table 9). One SNP, BTB-00137937, was detected at 5% GW level. Of the significant SNPs for Marb, five SNPs were detected within a 19.2 Mb segment (between 61.5 Mb and 80.7 Mb) on BTA7. Twelve SNPs were located within the Marb QTL regions that were reported in previous studies [13,18,20]. Nine SNPs were harbored within the regions of known genes (Table 9). In particular, one SNP, Hapmap39048-BTA-99764 was located within EDG1 gene on BTA3, which was reported to be a positional function candidate gene responsible for marbling [20].

Characterization of single nucleotide polymorphism for growth and carcass quality

Many SNPs influencing growth and carcass quality in Hanwoo had dominance effects. Of the 129 significant SNPs, 45 (11) SNPs had additive expression, 18 (2) dominance expression, and 29 (24) SNPs had both additive and dominant expression at 5% ChW (GW) level, of which 27 SNPs had overdominance effects, i.e. five for WWT, three for YWT, five for CWT, five for BFT, six for LMA, three for Marb, respectively (Tables 4 to 9). Using additive plus dominance effects, rather than additive effects only, led to more significant SNPs. These results indicated that the goodness of fit was improved by including dominance. Estimates of dominance effects have not been widely used in livestock breeding because it is difficult to estimate these effects accurately based on pedigree. The development of dense SNP panels offered new opportunities for detection and use of dominance at individual loci. Boysen et al [21] reported that significant dominance effects on milk production traits in cattle were identified in a WGAS [21].
The effectiveness of selection procedures that are based on genomic information can be increased by correctly characterization of inheritance mode of desired variants [7]. Heterosis that is caused by heterozygosity in single loci is responsible for survival rate by increasing reproductive fitness [22]. Hayes and Miller (2000) showed that dominance effect was needed in mate selection to exploit previously untapped genetic variation [23], while Dekkers and Chakraborty (2004) stressed maximization of crossbred performance by incorporating information from overdominant QTL [24]. Kim et al [8] found that many QTL for growth and carcass quality had a (over) dominant mode of gene action in a cross between Angus and Brahman cattle [8]. The use of dominance effects in a scenario of genomic selection increases the accuracy of estimated breeding values and still offers the opportunity of applying mate-allocation.
These result has important implication that dominance could be routinely included the whole-genome SNPs data in Hanwoo and implementation of successive molecular breeding schemes.

Quantitative trait locus region

Thirty three QTL regions were identified on 18 BTAs for the significant SNPs for growth and carcass quality (Table 10). Of the QTL, 20, one, and 12 QTL had additive, dominance, and both additive and dominance expression modes, respectively. All the QTL explained small to moderate proportions (2.2% to 10.9%) of the phenotypic variance with an average of 4.5%±1.6%. However, the estimates may be overestimated, especially when the SNP effect was small, partly due to so called Beavis effect [25].
Some significant SNPs were located in close proximity, e.g. the two SNPs BTB-01747944 and BTB-02105769 for WWT at 4.07 Mb and 4.15 Mb on BTA1, and ARS-BFGL-BAC-28936 and ARS-BFGL-BAC-30072 for YWT at 1.66 Mb and 1.69 on BTA23, respectively (Tables 4 and 5). These SNPs might not be causal mutation, but so close to the causal SNP as to form high LD with the causal mutation for the trait, and the effect of a single QTL could be spread over multiple SNPs.
A QTL for CWT was detected at 24.1 to 25.7 Mb of BTA14 with a significant evidence (−log10P = 10.7). Also, four QTL for CWT were clustered between 19. 7 and 36.1 Mb of BTA14 (Table 10). In the region, several QTL were reported that were associated with milk production, growth and carcass traits in cattle [8,15,26]. Especially in beef cattle, a majority of the QTL were located at 18 to 36 Mb of the chromosome [27]. The QTL region corresponded to a human chromosome homo sapiens (HSA) region in which genes responsible for growth characteristics reside. Mizoshita et al [15] reported a CWT-QTL in Wagyu within a 1.1 Mb chromosomal segment flanked by DIK7012 and DIK7020 on BTA14, in which four genes, including CA8 and RAB2 resided in the corresponding HSA region or six bovine expressed sequence tag (EST) assemblies, which may be a source of additive genetic variation for carcass weight [15]. Pausch et al [3] recently reported a QTL at 24.1 to 25.4 Mb of BTA14 that was associated with polymorphisms for a polyadenylation signal of the gene encoding for ribosomal protein S20 (RPS20), explaining major genetic variation of growth-related traits in a sample of 1,800 bulls of the German Fleckvieh breed [3]. In the similar region, Karim et al [28] identified causative variations influencing bovine stature in the PLAG1-CHCHD7 inter-genic region in a Holstein-Friesian×Jersey F2 population [28]. At the same region, a QTL for carcass weight was identified in a population of 1,156 Japanese Black steers [4]. In conclusion, the evidence of CWT QTL around 25 Mb of BTA14 in this study supports the QTL or causal mutation for growth, carcass, or stature on the proximal region of BTA14 in cattle.
On BTA20, several QTL for WWT, YWT, and LMA were identified (Table 10). The QTL for WWT and YWT were located at 42.74 to 42.87 Mb, near which a gene, GHR (33.90 to 34.07 Mb) was located. Growth hormone receptor (GHR) has been proposed as a candidate gene for growth and fat metabolism [29]. Another three QTL for LMA were located at 4.04 to 4.75 Mb, 8.12 to 8.81 Mb and 75.35 to 75.53 Mb, respectively. At the 8 Mb, a QTL for LMA was reported by Garcia et al [30] in F1 crossbred steers of Bos taurus breeds (Angus, Hereford, Gelbvieh, Simmental, Charolais, Limousin, and Red Angus) comprising Cycle 7 of the Germplasm Evaluation project [30]. Also, at 75 Mb, a LMA QTL was reported in a commercial Angus population [13].
Some QTL that affected two traits were located in close distance, e.g. the QTL for WWT and YWT at 82.5 and 83.0Mb of BTA11, or the QTL for CWT and BFT at 28.1 and 28.9 Mb of BTA14 (Table 10), suggesting the nature of gene characteristics, i.e. pleiotropy or clustering of multi-genes.

IMPLICATIONS

This study described a novel approach to model additive and dominance genetic effect using information of genome-wide SNP markers. Implementation of various additive and dominance SNP models enabled characterization of inheritance mode for QTL, and we found significant evidence of additive and/or dominance QTL for growth and carcass quality in a Hanwoo steer population. The QTL regions can be fine-mapped to find causal mutations, which would provide valuable information for subsequent quantitative trait loci analyses and genomic selection schemes. Many dominant QTL influencing growth and carcass traits in Hanwoo were detected in this study. This has the important implication that dominant expression for growth and carcass composition in beef cattle is not rare and thus dominance effect needs to be considered in marker-assisted breeding schemes in Hanwoo. Validation tests for the detected SNPs (QTL) are in progress and the QTLs that are confirmed in commercial Hanwoo populations can be applied to better improve Hanwoo production.

Notes

CONFLICT OF INTEREST

We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

ACKNOWLEDGMENTS

This work was supported by Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi (Yi Li), the National Natural Science Foundation of China No.31501002 & 31470070 (Yi Li), Shanxi Scholarship Council of China No.2013-072 (Yi Li) and the Natural Science Foundation of Shanxi No.2014011030-4 (Yi Li). Dr. Jong-Joo Kim is supported by a grant (715003-07) from the Research Center from Production Management and Technical Development for High Quality Livestock Products through Agriculture, Food and Rural Affairs Research Center Support Program (2016).

Figure 1
Decision trees to determine classification of QTL type using Add+Dom, Add and Dom models. S*, significant at a chromosome-wise level; NS*, non-significant at a chromosome-wise level; S, significant (p<0.05); NS, non-significant (p<0.05).
ajas-30-1-8f1.gif
Table 1
Summary statistics for 486 observations on growth and carcass traits in a Hanwoo steer population
Trait Average SD Minimum Maximum CV
Weaning weight (kg) 170 30.7 84.5 271 18.0
Yearling weight (kg) 312 34.2 220 414 11.0
Carcass weight (kg) 357 40.0 158 481 11.2
Backfat thickness (cm) 1.01 0.41 0.30 3.50 40.9
Longissimus dorsi muscle area (cm2) 78.9 9.54 22 109 12.1
Marbling score (1–9) 3.38 1.77 1 9 52.5

SD, standard deviation; CV, coefficient of variation (%).

Table 2
SNP distributions after quality control and average distances between adjacent SNPs on Bos taurus autosomal chromosomes
BTA No. SNPs Average intervals (kb)
1 2,376 67.75
2 1,930 72.91
3 1,832 69.40
4 1,736 71.37
5 1,472 85.52
6 1,818 67.38
7 1,577 70.87
8 1,673 69.93
9 1,399 77.21
10 1,487 71.03
11 1,567 70.27
12 1,145 74.47
13 1,228 68.52
14 1,219 66.71
15 1,160 72.96
16 1,101 70.62
17 1,092 69.95
18 930 71.08
19 993 65.59
20 1,114 67.75
21 997 69.42
22 872 70.75
23 792 67.37
24 918 70.81
25 699 61.98
26 764 67.18
27 694 70.28
28 652 70.62
29 731 70.82
Total 35,968

SNP, single nucleotide polymorphism; BTA, Bos taurus autosom.

Table 3
Number of significant SNPs with additive and/or dominance effects that were detected for growth and carcass quality traits in Hanwoo1)
Trait Additive Dominance Partial Total
Weaning weight (kg) 9(0) 2(0) 7(0) 18(0)
Yearling weight (kg) 5(1) 6(1) 4(1) 15(3)
Carcass weight (kg) 6(4) 1(0) 5(4) 12(8)
Backfat thickness (cm) 10(6) 1(0) 4(12) 15(18)
Longissimus dorsi muscle area (cm2) 7(0) 2(0) 2(7) 11(7)
Marbling score (1–9) 8(0) 6(1) 7(0) 21(1)
Total 45(11) 18(2) 29(24) 92(37)

SNPs, single nucleotide polymorphism; QTL, quantitative trait loci.

1) The numbers were for the QTL that were detected at the 5% chromosome-wise level (genome-wise level).

Table 4
Position, SNP alleles, estimated effects of SNPs for weaning weight (kg) that were detected at 5% chromosome-wise level
SNP1) Chr Position (bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
BTB-01747944 1 4,070,974 T/G G(0.19) 12.1±2.91 −15.2±3.33 4.68 LOC785105 40,376
BTB-02105769 1 4,149,602 T/C C(0.26) 8.74±2.29 −12.3±2.67 4.73 LOC785105 119,004
ARS-BFGL-NGS-35164 3 55,278,469 G/A G(0.46) - 8.86±2.02 4.78 CDC7 14,260
ARS-BFGL-NGS-5482 4 61,908,479 G/A G(0.26) −7.65±1.74 - 4.79 THAP5 17,976
BTA-29483-no-rs 5 4,963,140 C/A C(0.17) −8.92±2.04 - 5.02 CAPS2 85,827
Hapmap52787-rs29024515 5 33,277,668 T/C C(0.21) 11.6±2.36 −8.63±2.9 4.8 LOC505861 within
BTA-73718-no-rs 5 66,075,729 T/G G(0.07) −12.0±3.01 - 4.06 NEDD1 249,756
ARS-BFGL-NGS-73495 5 90,096,456 G/A G(0.31) −6.46±1.64 - 4.01 SSPN 53,061
ARS-BFGL-NGS-34063 5 125,275,403 G/A G(0.35) - −9.06±2.03 5 FAM19A5 324,968
BTB-00317489 7 64,536,896 G/A G(0.27) −8.10±1.71 - 5.45 GRIA1 40,884
ARS-BFGL-BAC-11115 11 82,938,260 T/C T(0.28) −6.87±1.60 - 4.92 MSGN1 11,224
ARS-BFGL-NGS-103169 12 39,576,130 T/C T(0.14) −18.6±4.29 −21.0±4.71 4.05 PCDH9 313,077
Hapmap26937-BTA-127685 12 54,656,928 G/A G(0.34) 6.49±1.56 - 4.64 RBM26 194,009
BTB-00497582 12 54,690,096 T/G T(0.37) 6.31±1.51 - 4.65 RBM26 160,841
Hapmap57428-rs29016058 12 78,783,490 T/C C(0.11) −21.9±4.99 22.7±5.44 3.81 LOC787750 583,453
ARS-BFGL-NGS-39382 16 47,648,555 G/A A(0.30) 8.45±1.83 −5.86±2.38 4.63 PLCH2 within
ARS-BFGL-BAC-33668 20 42,739,808 T/G G(0.16) 3.58±1.56 6.93±2.05 4.4 ADAMTS12 within
Hapmap40017-BTA-65421 29 33,632,380 T/C T(0.05) −14.2±3.53 - 4.27 ETS1 86,263

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

Table 5
Position, SNP alleles, estimated effects of SNPs for yearling weight (kg) that were detected at 5% chromosome-wise level
SNP1) Chr Position (bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
ARS-BFGL-NGS-98401 2 41,281,224 G/A G(0.39) - −9.54±2.47 4.14 GPDM 63,447
Hapmap36520-SCAFFOLD290052_3670 2 41,325,780 T/C T(0.42) - −10.1±2.45 4.60 NR4A2 4,484
BTA-21857-no-rs 2 72,889,807 T/G T(0.46) - 12.4±2.44 6.56* LOC523484 within
ARS-BFGL-NGS-105590 7 62,525,451 G/A A(0.28) −8.19±2.00 - 4.66 FAT2 within
BFGL-NGS-118165 11 60,747,467 T/C T(0.39) −9.56±2.06 6.09±2.58 4.90 LRRTM4 489,704
ARS-BFGL-BAC-11115 11 82,938,260 T/C C(0.28) −8.77±1.98 - 5.17 MSGN1 11,224
ARS-BFGL-BAC-23724 14 72,289,416 G/A G(0.35) −8.69±1.97 9.00±2.70 5.30* CALB1 24,824
ARS-BFGL-NGS-17747 15 13,517,697 T/C C(0.35) 7.74±1.84 - 4.82 SESN3 21,628
ARS-BFGL-NGS-38840 15 31,874,189 T/A T(0.48) 8.98±1.81 - 6.43* UBASH3B 86,347
ARS-BFGL-NGS-39866 18 18,183,364 G/A G(0.39) - −11.0±2.5 5.29 CYLD within
Hapmap50009-BTA-50200 20 29,598,818 G/A G(0.09) 35.3±9.40 −26.3±9.77 4.41 LOC100138060 42,816
ARS-BFGL-BAC-33668 20 42,739,808 T/G T(0.46) 3.26±1.92 9.70±2.51 4.49 ADAMTS12 within
ARS-BFGL-BAC-28936 23 1,664,787 G/A G(0.26) - −10.2±2.61 4.28 HMGCLL1 within
ARS-BFGL-BAC-30072 23 1,694,992 T/C T(0.33) - −9.68±2.53 4.15 HMGCLL1 within
Hapmap48951-BTA-94291 26 14,828,964 T/C C(0.25) −8.86±2.21 - 4.28 EXOC6 within
ARS-BFGL-NGS-42226 26 31,316,453 G/A G(0.19) −9.23±2.37 - 4.23 DUSP5 18,449
BTA-98192-no-rs 27 32,786,807 G/A G(0.28) 8.38±2.29 −12.3±2.99 4.73 LOC783570 184,156
Hapmap36817-SCAFFOLD245829_8774 29 22,927,228 G/A A(0.21) - −11.9±2.60 5.59 LOC100139055 185,099

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

* Significant at the 5% genome-wise level.

Table 6
Position, SNP alleles, estimated effects of SNPs for carcass weight (kg) that were detected at 5% chromosome-wise level
SNP1) Chr Position (bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
ARS-BFGL-NGS-36803 2 63,623,562 G/A G(0.07) 97.8±17.6 101±18.0 5.45* LOC100140701 330,224
BFGL-NGS-110568 8 56,596,924 G/A A(0.06) −97.8±17.6 100±18.0 5.43* CEP78 14,027
ARS-BFGL-NGS-59989 10 84,166,596 T/C C(0.46) - 16.1±3.17 4.90 SLC8A3 within
ARS-BFGL-NGS-54787 14 18,653,617 G/A G(0.09) −14.3±4.14 - 3.32 LOC529047 within
Hapmap41993-BTA-86407 14 19,815,951 T/C C(0.23) 10.3±2.70 - 3.76 LOC100140381 40,312
BTB-01143619 14 24,315,258 T/C C(0.06) 22.9±5.02 - 5.17 FAM110B 79,759
BTB-01143580 14 24,383,627 G/A A(0.06) 27.5±5.02 - 7.20* UBXN2B 32,070
Hapmap30932-BTC-011225 14 24,898,781 T/C T(0.06) −24.7±5.08 - 5.81* TOX within
BTB-01280026 14 25,170,557 T/C C(0.07) 24.0±4.51 - 6.75* TOX 96,111
Hapmap27934-BTC-065223 14 25,288,714 G/A G(0.05) −25.2±5.12 - 5.89* LOC100140360 144,727
UA-IFASA-9679 14 26,428,822 G/A G(0.49) −9.17±2.39 7.08±3.20 4.18 CHD7 101,474
Hapmap49130-BTA-34437 14 28,483,105 T/C C(0.07) 34.0±10.2 −22.30±11.00 3.40 YTHDF3 422,759
BTB-00562658 14 28,536,444 G/A A(0.07) 34.3±10.2 −20.80±11.00 3.75 YTHDF3 476,098
ARS-BFGL-BAC-8496 14 30,543,605 G/A G(0.06) −18.9±4.82 - 4.09 RRS1 35,258
BTB-00564066 14 30,695,187 G/A G(0.21) −5.80±3.97 7.74±4.79 3.19 MYBL1 24,450
ARS-BFGL-NGS-6936 14 33,171,241 G/A A(0.05) 18.7±5.11 - 3.56 PREX2 within
BTB-01640837 14 36,095,273 T/G G(0.43) 8.28±2.33 - 3.30 LOC509345 443,377
ARS-BFGL-NGS-39866 18 18,183,364 G/A A(0.39) −7.96±2.50 −8.52±3.32 5.23* CYLD within
BTB-00752386 19 41,676,085 T/G T(0.13) −26.78±6.40 −32.3±7.19 4.41* MSL1 7,676
BTB-01392802 21 54,287,321 G/A A(0.11) −67.0±13.1 62.9±13.3 4.35 LOC787825 125,238

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

* Significant at the 5% genome-wise level.

Table 7
Position, SNP alleles, estimated effects of SNPs for backfat thickness (cm) that were detected at 5% chromosome-wise level
SNP1) Chr Position(bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
Hapmap24136-BTA-124014 1 55,673,837 T/G G(0.18) 0.09±0.05 0.10±0.06 5.05* CD47 19,151
Hapmap51248-BTA-51337 1 128,511,629 G/A G(0.09) −0.18±0.04 - 4.57 PLSI within
Hapmap27170-BTA-142780 5 6,666,948 T/C T(0.07) −1.11±0.18 −1.20±0.19 8.47* ZDHHC17 within
BFGL-NGS-119673 5 38,109,780 T/C T(0.47) −0.10±0.03 - 4.29 ANO6 within
ARS-BFGL-NGS-8401 5 76,428,730 T/G T(0.28) 0.13±0.03 - 5.00* SYN3 within
BTB-00236217 5 110,984,106 T/C T(0.41) 0.11±0.03 - 4.13 CD9 2,776
BTB-01384704 6 959,692 A/G G(0.48) −0.12±0.03 - 5.57* LOC100139637 63,038
BTB-01744782 6 1,452,847 T/G G(0.07) 0.61±0.13 −0.56±0.14 4.74* 1-Mar 77,282
BTA-28590-no-rs 9 10,295,707 G/A G(0.24) −0.14±0.03 - 5.61* bta-mir-30a 406,847
BTB-01374666 11 25,076,270 T/G G(0.32) 0.12±0.03 - 4.55 U4 570,076
BTB-00467701 11 31,206,484 T/C T(0.06) −0.21±0.05 - 4.14 MSH6 54,643
Hapmap34906-BES11_Contig369_1053 11 79,443,335 G/A A(0.37) 0.11±0.03 - 4.26 LOC513245 431,096
BTB-01493007 13 52,723,213 T/C T(0.05) −0.56±0.13 −0.36±0.14 5.57* PTPRA within
ARS-BFGL-NGS-34390 14 20,953,071 G/A G(0.12) −0.29±0.07 −0.21±0.08 3.87 ST18 within
Hapmap49130-BTA-34437 14 28,483,105 T/C C(0.07) 0.59±0.11 −0.56±0.11 6.4* YTHDF3 422,759
BTB-00562658 14 28,536,444 G/A A(0.07) 0.59±0.11 −0.56±0.11 6.37* YTHDF3 476,098
BTB-00566332 14 37,352,866 T/C T(0.13) - −0.21±0.04 5.63 PEX2 266,891
ARS-BFGL-NGS-4138 15 39,970,876 T/C T(0.06) −1.13±0.19 −1.10±0.19 7.79* GALNTL4 27,421
BTB-00634483 16 28,329,540 G/A G(0.14) −0.44±0.09 −0.43±0.10 4.76* SMYD3 within
Hapmap42533-BTA-38667 16 31,436,094 G/A A(0.38) −0.11±0.03 - 4.68 CEP170 94,723
BTB-00640968 16 40,694,131 G/C G(0.26) 0.15±0.03 - 6.31* CTNNBIP1 12,174
ARS-BFGL-NGS-17466 16 53,804,655 T/A T(0.26) 0.15±0.04 0.11±0.04 3.81 MRPS14 10,788
ARS-BFGL-NGS-85980 16 72,398,010 T/G G(0.08) 0.54±0.13 −0.57±0.14 3.76 LOC100139930 46,502
ARS-BFGL-NGS-41599 17 74,420,464 G/A A(0.07) 0.49±0.11 −0.54±0.12 4.82* LOC616727 3,592
BTA-54022-no-rs 22 3,844,964 C/A C(0.10) −0.41±0.09 −0.41±0.09 4.81* RBMS3 within
ARS-BFGL-NGS-18665 22 7,042,486 G/A A(0.07) 0.91±0.14 −0.87±0.14 9.25* DYNC1LI1 1,080
BFGL-NGS-116395 23 11,896,040 C/A C(0.40) 0.10±0.03 - 4.15 MDGA1 within
ARS-BFGL-BAC-30052 23 11,964,461 G/T T(0.45) 0.12±0.03 - 5.53* ZFAND3 24,327
BFGL-NGS-114371 26 51,572,874 G/A G(0.08) 0.19±0.05 - 4.2 bta-mir-202 6,161
ARS-BFGL-NGS-28346 26 42,712,420 G/A A(0.13) 0.37±0.08 −0.41±0.09 4.56* TACC2 within
ARS-BFGL-NGS-39535 29 26,364,496 G/A G(0.26) 0.13±0.03 - 4.66 NAV2 88,899
BTB-01020342 29 33,014,346 G/A A(0.49) −0.12±0.03 - 5.40* ETS1 459,952
ARS-BFGL-NGS-23717 29 36,574,648 T/C C(0.19) 0.22±0.05 −0.17±0.06 3.95 NTM within

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

* Significant at the 5% genome-wise level.

Table 8
Position, SNP alleles, estimated effects of SNPs for longissimus dorsi muscle area (cm2) that were detected at 5% chromosome-wise level
SNP1) Chr Position (bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
ARS-BFGL-NGS-96411 1 99,002,229 G/A A(0.08) 15.8±3.06 15.9±3.20 5.68* LOC100140324 3,400
ARS-BFGL-NGS-36803 2 63,623,562 G/A A(0.07) 26.4±4.29 27.3±4.40 8.13* LOC100140701 330,224
BTA-99819-no-rs 3 84,880,188 T/C T(0.07) −9.60±2.23 11.3±2.42 4.69 PDE4B within
Hapmap43716-BTA-87799 4 25,686,934 G/A A(0.16) 8.32±1.78 9.79±1.92 5.54* LOC100139029 144,805
ARS-BFGL-NGS-11156 6 31,210,287 T/C C(0.06) 15.5±3.07 16.8±3.22 5.84* UNC5C within
BTB-01321253 7 83,625,072 G/A G(0.17) - 3.88±0.90 4.72 XRCC4 321,087
BFGL-NGS-110568 8 56,596,924 G/A G(0.06) −26.6±4.29 26.4±4.40 8.01* CEP78 14,027
ARS-BFGL-NGS-77091 15 76,834,507 T/C C(0.22) - 3.49±0.84 4.47 AMBRA1 within
ARS-BFGL-NGS-25982 20 4,493,745 G/A A(0.40) 2.49±0.64 - 4.00 NEURL1B 9,980
Hapmap53674-rs29025319 20 8,676,696 T/C C(0.12) 4.69±0.96 - 5.84 LOC783819 132,888
ARS-BFGL-NGS-38482 20 37,708,167 T/C C(0.10) 7.25±1.69 6.50±1.90 3.95 RICTOR within
BFGL-NGS-112221 20 75,476,187 T/C T(0.27) −2.80±0.70 - 4.21 ZDHHC11 15,990
BFGL-NGS-113227 20 75,530,515 T/C T(0.24) −2.82±0.72 - 4.02 CEP72 within
BTB-01392802 21 54,287,321 G/A G(0.11) −16.5±3.21 14.8±3.28 5.95* LOC787825 125,238
ARS-BFGL-NGS-35298 24 13,445,705 T/C T(0.27) −2.57±0.66 - 3.94 LOC781724 3,783
BTA-100770-no-rs 24 40,446,177 T/C C(0.46) −2.45±0.60 - 4.25 EPB41L3 within
ARS-BFGL-NGS-16328 26 18,231,319 G/A A(0.13) −3.94±0.91 - 4.78 TLL2 within
ARS-BFGL-NGS-94161 28 35,084,332 G/A G(0.14) −10.8±2.17 11.0±2.27 5.38* SFTPD 3,977

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

* Significant at the 5% genome-wise level.

Table 9
Position, SNP alleles, estimated effects of SNPs for marbling score (1–9) that were detected at 5% chromosome-wise level
SNP1) Chr Position (bp)2) Allele3) MAF Estimates of SNPs effects4) Significance (−log10P5)) Nearest gene6)


Additive Dominance Name Distance (bp)
BTB-00126406 3 35,420,885 T/C T(0.08) 2.04±0.41 −1.55±0.44 5.78 KCNA3 6,892
Hapmap39048-BTA-99764 3 44,502,770 T/G T(0.30) 0.53±0.12 - 4.81 EDG1 within
BTB-00137937 3 87,445,557 G/A A(0.15) - 0.91±0.18 6.32* ROR1 within
Hapmap3063-BTA-15439 5 109,235,854 C/A C(0.16) - −0.72±0.17 4.57 LOC751811 2,138
UA-IFASA-5392 7 61,523,523 T/C C(0.17) −0.68±0.16 - 4.89 LOC100138980 11,789
BTB-00317097 7 65,652,720 T/C C(0.25) −0.59±0.13 - 4.88 LOC784642 39,676
BFGL-NGS-114649 7 69,609,512 T/G T(0.37) −0.36±0.13 −0.32±0.16 3.91 EPN4 409,625
ARS-BFGL-NGS-63249 7 71,629,520 T/C C(0.47) 0.36±0.11 0.49±0.15 4.06 FABP6 6,896
Hapmap40812-BTA-80049 7 80,713,755 T/C T(0.11) 1.39±0.35 −0.90±0.38 3.97 ODZ2 within
Hapmap25504-BTA-125586 10 53,007,997 G/A G(0.24) 0.90±0.18 −0.77±0.21 5.17 GRINL1A 113,126
ARS-BFGL-NGS-73404 13 54,520,958 T/C C(0.10) 0.84±0.20 * 4.73 PRPF6 within
BTB-00633720 16 27,646,807 T/G T(0.12) 0.73±0.18 - 4.06 EPB41L3 within
Hapmap26379-BTA-130999 16 51,370,098 T/C T(0.37) 0.50±0.12 - 4.71 PDPN 12,031
Hapmap60996-rs29015760 17 25,742,715 C/A C(0.15) - 0.75±0.18 4.45 LOC539187 72,892
BTB-00689316 17 73,276,535 T/C C(0.43) - −0.62±0.15 4.26 MORC2 within
ARS-BFGL-NGS-107813 18 33,279,345 C/A A(0.32) - −0.64±0.15 4.60 TK2 within
ARS-BFGL-NGS-55014 18 39,198,464 G/A A(0.49) 0.31±0.11 −0.53±0.15 4.37 HYDIN within
Hapmap40994-BTA-46361 19 62,959,153 T/C T(0.35) 0.50±0.12 - 4.18 MAP2K6 78,574
ARS-BFGL-NGS-36359 19 63,936,266 T/C C(0.13) −0.68±0.17 - 4.25 RGS9 38,731
ARS-BFGL-NGS-16187 25 8,839,364 G/A A(0.45) −0.33±0.12 −0.59±0.15 4.62 LOC538487 37,048
BTB-111681-no-rs 26 10,857,884 G/A G(0.13) - −0.75±0.19 4.12 LIPK within
BTB-00978135 28 12,940,047 T/G T(0.37) - −0.61±0.15 4.15 BICC1 21,033

SNP, single nucleotide polymorphism; MAF, minor allele frequency; QTL, quantitative trait loci.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Nucleotides of substitution.

4) Estimates of additive and dominance effects with standard errors.

5) Negative logarithm of the comparison-wise p-value of the test-statistic against the null hypothesis of no QTL at the most likely position for the inferred QTL model.

6) The nearest known gene to the significant SNP.

* Significant at the 5% genome-wise level.

Table 10
QTL regions associated with growth and carcass traits and the percentage of the total genetic variance explained
Chr Region type Region start (bp)1) Region end (bp)2) Region size (bp) Marker count Region −log10P Most significant SNP

SNP 2p3)
Weaning wight (kg)
 4 add 61,386,842 61,908,479 521,637 12 2.98 ARS-BFGL-NGS-5482 4.75
 5 add 4,528,722 4,963,140 434,418 11 2.70 BTA-29483-no-rs 4.70
 11 add 82,506,706 82,998,032 491,326 11 2.21 ARS-BFGL-BAC-11115 4.07
 12 add 54,600,867 54,690,096 89,229 4 2.17 Hapmap26937-BTA-127685 4.00
 20 add+dom 42,739,808 42,872,996 133,188 4 2.94 ARS-BFGL-NGS-39382 4.33
Yearling weight (kg)
 11 add+dom 60,503,780 60,747,467 243,687 5 2.25 BFGL-NGS-118165 5.67
 11 add 82,715,271 82,998,032 282,761 7 2.07 ARS-BFGL-BAC-11115 4.39
 20 add+dom 42,739,808 42,819,855 80,047 3 2.17 ARS-BFGL-BAC-33668 4.41
 26 add 14,341,149 14,828,964 487,815 9 2.07 Hapmap48951-BTA-94291 4.16
Carcass weight (kg)
 14 add 19,654,754 19,910,197 255,443 5 2.12 Hapmap41993-BTA-86407 2.96
 14 add 24,100,495 25,707,512 1,607,017 36 10.65 BTB-01143580 6.25
 14 add+dom 28,090,849 28,604,028 513,179 11 2.07 BTB-00562658 3.42
 14 add 36,023,989 36,095,273 71,284 3 2.25 BTB-01640837 2.61
Backfat thickness (cm)
 5 add 37,930,354 38,268,770 338,416 6 2.09 BFGL-NGS-119673 3.25
 5 add 75,805,133 76,714,910 909,777 14 4.73 ARS-BFGL-NGS-8401 4.45
 6 add+dom 454,623 1,496,086 1,041,463 21 5 BTB-01744782 5.34
 9 add 10,080,000 11,411,851 1,331,851 13 3.31 BTA-28590-no-rs 4.70
 14 add+dom 28,344,924 28,938,888 593,694 12 2.61 Hapmap49130-BTA-34437 4.91
 16 add 31,108,917 36,611,548 502,631 11 2.19 Hapmap42533-BTA-38667 3.78
 16 add 39,293,150 40,694,131 1,324,364 18 5.83 BTB-00640968 5.76
 16 add+dom 53,663,332 53,952,476 289,144 6 2.05 ARS-BFGL-NGS-17466 3.49
 17 add+dom 74,292,265 74,738,486 446,221 6 2.34 ARS-BFGL-NGS-41599 3.13
 23 add 11,627,828 12,207,677 579,849 9 2.23 ARS-BFGL-BAC-30052 4.71
 29 add+dom 35,829,053 36,646,213 817,160 12 3.38 ARS-BFGL-NGS-23717 4
Longissimus dorsi muscle area(cm2)
 3 add+dom 84,750,518 85,176,770 426,252 11 2.71 BTA-99819-no-rs 2.97
 7 add+dom 107,689,830 107,950,229 260,399 7 2.10 Hapmap48901-BTA-80577 2.19
 20 add 4,040,334 4,745,147 704,813 17 3.92 ARS-BFGL-NGS-25982 3.65
 20 add 8,115,195 8,810,833 695,638 13 3.12 Hapmap53674-rs29025319 5.52
 20 add 75,346,433 75,530,515 184,082 4 2.54 BFGL-NGS-112221 3.82
 28 add+dom 34,473,261 35,155,406 649,145 11 2.13 ARS-BFGL-NGS-94161 10.94
Marbling score (1–9)
 13 add 54,474,756 54,548,692 73,936 3 2.24 ARS-BFGL-NGS-73404 4.65
 16 add 51,370,098 51,470,017 99,919 5 2.03 Hapmap26379-BTA-130999 4.24
 18 dom 32,990,778 33,279,345 288,567 9 2.05 ARS-BFGL-NGS-107813 3.65

QTL, quantitative trait locus; SNP, single nucleotide polymorphism.

1),2) SNP marker annotations and their positions were based on the bovine reference genome (btau4.0).

3) Proportion of phenotypic variance due to the SNPs.

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