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BMC Genetics

, 20:8 | Cite as

Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle

  • Camila U. BrazEmail author
  • Jeremy F. Taylor
  • Tiago Bresolin
  • Rafael Espigolan
  • Fabieli L. B. Feitosa
  • Roberto Carvalheiro
  • Fernando Baldi
  • Lucia G. de Albuquerque
  • Henrique N. de OliveiraEmail author
Open Access
Research article
  • 221 Downloads
Part of the following topical collections:
  1. Complex traits and quantitative genetics

Abstract

Background

Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness.

Results

The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes.

Conclusions

GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.

Keywords

Additive genetic variance Beef cattle Haplotype GWAA Meat tenderness 

Abbreviations

SNP

Single nucleotide polymorphism

GWAA

Genome-wide association analysis

LD

Linkage disequilibrium

QTL

Quantitative trait locus

CG

Contemporary group

WBSF

Warner-Bratzler shear force

SW3

Sliding window haplotypes of three SNPs

SW5

Sliding window haplotypes of five SNPs

SW7

Sliding window haplotypes of seven SNPs

SW9

Sliding window haplotypes of nine SNPs

SW11

Sliding window haplotypes of eleven SNPs

GRM

Genomic relationship matrix

VEP

Variant effect predictor

BTA

Bos taurus autosome

NO

Nitric-oxide

nNOS

Neuronal nitric oxide synthase

HDL

High-density lipoprotein

GO

Gene ontology

Background

Following the completion of the first draft bovine reference genome assembly, a high-density single nucleotide polymorphism (SNP) genotyping assay was developed [1], enabling genome-wide association analyses (GWAA), which are useful in understanding the underlying biology of traits [2, 3]. Several GWAA have identified SNP associated with meat tenderness in cattle [4, 5, 6, 7], which is one of the most important attributes impacting consumer satisfaction and the price of beef [8]. Moreover, meat tenderness is the most important trait requiring improvement in Nelore cattle in order to increase beef quality and ensure consumer acceptability. However, studies have shown that the traditional single-SNP GWAA can be inefficient because single-SNPs provide limited information about the content of flanking genomic regions [9, 10]. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and also potentially capture epistastic interactions between variants within a haplotyped locus, providing increased power and robustness for association studies [10, 11, 12, 13, 14, 15, 16].

The use of haplotypes in GWAA analysis has not been widely exploited because there is no consensus on how many adjacent SNPs should be haplotyped, or on what is the best methodology for the definition and analysis of the haplotype blocks [17]. Different criteria have been proposed, but the most widely used approach is based on the extent of LD between markers as described by [18], which combines SNPs into haplotype blocks in genomic regions of high LD, i.e., with low evidence for recombination. However, the use of this approach can result in “orphan” SNPs that fall outside of predefined LD blocks, leading to a loss in ability to dissect genetic variation in these regions in the association analysis [14]. Thus, haplotype block approaches may not be the most efficient approach for association studies [19]. An alternative strategy for performing haplotype-based association analyses is based on overlapping sliding windows, in which several neighboring contiguous SNPs are included in a haplotype analysis, spanning the entire genome regardless of the extent of LD between the markers [10, 14, 20]. This approach has been shown to be more powerful than single-SNP and LD block–based haplotype analyses, particularly in genomic regions with high recombination and low LD [10, 21, 22]. Furthermore, according to [10], the use of sliding window haplotype analysis increases the likelihood of QTL detection and identification of the genomic regions harboring the causal variants.

We performed a GWAA using haplotypes defined by variable-sized sliding windows and compared the results to single-SNP GWAA to ascertain the optimal window size for identifying QTL regions that influence meat tenderness in Nelore cattle. The results of this study provide a better understanding of the genetic basis of meat tenderness in zebu cattle.

Methods

Animals

The 3161 Nelore bulls used in this study were born between 2008 and 2013 and were sourced from three different animal breeding programs. These animals were raised on pasture, finished in a feedlot for approximately 90 days, and then slaughtered in commercial slaughterhouses at a mean age of 691 ± 102 days. Contemporary groups (CG) were defined by the combination of farm of origin, year of birth, management group as long-yearlings and month and year of slaughter. The animals belonged to 116 CG with at least three animals per group.

Phenotypic and genotypic data

The animals were slaughtered in commercial slaughter houses and the carcasses were identified by tags and chilled for 24 to 48 h post-mortem. Steaks of 2.54 cm thickness were then collected from the longissimus thoracis muscle between the 12th and 13th ribs from the left half of the carcasses. The steaks were vacuum sealed and aged in a cold chamber for 150 h at 1 °C and then were frozen at − 20 °C. Next, the steaks were cooked in an oven to an internal temperature of 71 °C as proposed by [23]. Warner-Bratzler shear force (WBSF), a mechanical measurement of tenderness, was measured 24 h after cooking using a Salter shearing device (G-R Electric, Manhattan, KS). Eight 1.27 mm meat cores were obtained from each sample and the average shear force of the eight cores was used in analysis. The mean WBSF for the 3161 Nelore bulls was 5.9 ± 1.80 kg varying from 1.6 kg to 11.9 kg.

Using a DNeasy Blood & Tissue Kit (Qiagen GmbH, Hilden, Germany), tissues from the longissimus thoracis muscle were used to extract DNA according to the manufacturer’s instructions. Genotyping was performed by high-density BeadArray technology (777 K) using the Illumina (San Diego, CA) BovineHD Infinium Assay® with an Illumina HiScan System® for 1405 animals and the 1756 remaining animals were genotyped with a lower density bead array (GGP75Ki). Genotypes were imputed to the content of the BovineHD assay and phased using FImpute software [24] including available pedigree information. The average imputation accuracy from GGP75Ki to Illumina® BovineHD was 98.93%, as reported by [25]. Samples for which the genotype call rate was less than 90% and SNP markers with a call rate of less than 95%, or that had a minor allele frequency of less than 5%, or Hardy Weinberg Equilibrium test statistic probability of less than 10− 5 or that were unmapped to autosomes or sex-linked were removed. A total of 413,355 SNP markers remained for analysis. The genomic coordinates for each SNP marker were based on the Bos taurus UMD3.1 reference assembly.

Construction of haplotypes

Five different haplotype sizes were constructed based on overlapping sliding windows methodology spanning the entire genome: three (SW3), five (SW5), seven (SW7), nine (SW9) or eleven (SW11) SNPs. Given an ordered set of markers (SNP1, SNP2, SNP3, ..., SNPn), where n is the number of SNP markers on the chromosome, sliding windows of overlapping haplotypes are tested in sequence. i.e. for SW3: haplotype 1 (SNP1-SNP2-SNP3), haplotype 2 (SNP2-SNP3-SNP4), haplotype 3 (SNP3-SNP4-SNP5), ..., haplotype n (SNPn-2, SNPn-1, SNPn) [14, 20]. The haplotypes were estimated for each locus and the diplotype of each animal was estimated as the combination of haplotypes i and i’ at locus j. Thus, the dummy variable for each haplotype were coded as: 0 = no copies of the haplotype, 1 = one copy of the haplotype (paternal or maternal), and 2 = two copies of the haplotype (paternal and maternal).

Genome-wide association analysis

The WBSF was pre-adjusted for fixed effect of CG and age at slaughter as covariate (linear effect). Fixed effects were estimated using a single-trait animal model implemented in AIREMLF90 [26]. The univariate linear mixed model analysis was performed for pre-adjusted WBSF using single-SNPs and each size of haplotype (three, five, seven, nine or eleven SNPs) individually in GEMMA [27] using the model: y = 1μ +  + Zu + e; u~MVNn(0, GVg); e~MVNn(0, IVe); where: y is an n-vector of pre-adjusted WBSF; μ is the overall mean; X is the incidence matrix for single SNP or haplotype; β is the single SNP allele or haplotype effects; Z is an n x n identity matrix; u is an n-vector of random residual additive genetic effects; G is a n x n genomic relationship matrix (GRM); e is a vector of residuals; Vg is the residual additive genetic variance component; Ve is the residual variance component; and I is an n x n identity matrix. MVNn denotes the n-dimensional multivariate normal distribution. The GRM was estimated using the standardized genotypes for all 413,355 SNP markers retained for analysis following filtering, using GEMMA. The same GRM was used for the single-SNPs and haplotype-based association analyses.

Estimating additive genetic variance

The additive genetic variance (VA) explained by SNP markers was estimated as: \( {\sigma}_i^2=2{p}_i\left(1-{p}_i\right){a}_i^2 \), where: \( {\sigma}_i^2 \) is the additive genetic variance for the ith SNP; pi is the allele frequency of one of the alleles at the ith SNP; and ai is the additive effect of the ith SNP. An equivalent equation was used to estimate VA explained by the haplotyped loci [28], as follows: \( {\sigma}_j^2=\sum \limits_{i=1}^{k_j-1}\sum \limits_{l=2}^{k_j}{\left({a}_{ij}-{a}_{lj}\right)}^2{p}_{ij}{p}_{lj} \), for l > i, where: \( {\sigma}_j^2 \)is the additive genetic variance for the jth haplotype, aij is the additive effect of the ith allele at the jth haplotype, alj is the additive effect of the lth allele at the jth haplotype, pij is the allele frequency for the ith allele at the jth haplotype, plj is the allele frequency for the lth allele at the jth haplotype, and kj is the number of existing alleles at the jth haplotype. The additive effects and frequencies of the SNP and haplotype alleles were calculated using GEMMA software as described in section “Genome-wide association analysis”.

For the identification of the SNPs and haplotyped loci that explained the greatest amounts of VA for WBSF, the estimated VA were assumed to follow a gamma distribution with parameters shape (α) and rate (β) [29]. The parameters (α and β) were predicted using the VA explained by the SNPs and haplotyped loci which allowed establishing the value of gamma distribution quantile corrected for multiple testing by Bonferroni method (α ≤ 0.05). For the SNPs, the α and β were predicted using an approximation of the Newton-Raphson method [30] as: \( \widehat{\alpha}\approx \frac{3-s\sqrt{{\left(3-s\right)}^2+24s}}{12s} \), where: \( s=\ln \left(\frac{1}{N}\sum \limits_{i=1}^N{\sigma}_i^2\right)-\frac{1}{N}\sum \limits_{i=1}^N\mathit{\ln}\left({\sigma}_i^2\right) \); \( \widehat{\beta}=\frac{\widehat{a}}{\mu_{\sigma_i^2}} \), where: \( {\mu}_{\sigma_i^2} \) is the mean of estimated VA explained by the SNPs. For the haplotyped loci, the estimated VA were dependent on the number of alleles at each haplotyped locus. Therefore, a cubic regression model was used to predict α as a function of the number of alleles. For the \( \widehat{\beta} \) parameter, the Brody non-linear regression model [31] was applied, as follows: \( \widehat{\beta}=A\left(1-{Be}^{- kt}\right)+\varepsilon \), where: \( \widehat{\beta} \) is a predicted β parameter; A is the asymptotic limit for the β parameter; B is the integration constant; k is the curve parameter representing the ratio of maximum growth rate to asymptotic limit of \( \widehat{\beta} \); t is the number of alleles at the haplotyped locus; and ε is the residual. From these equations, it was possible to estimate the gamma distribution in order to establish the thresholds that allowed identifying haplotyped loci (regarding the number of alleles) and SNP markers that explained the greatest amounts of VA for WBSF.

Linkage disequilibrium analysis

The LD between each pair of the SNPs (413,355), measured as r2, was calculated using Haploview [32]. The average r2 values according to distance between markers are displayed in Fig. 1. The regions that explained the greatest amounts of VA for WBSF were classified as strong (r2 > 0.6), moderate (0.2 < r2 < 0.6), weak (0.1 < r2 < 0.2), and not in LD (r2 < 0.1) based on the average r2 values between the SNPs.
Fig. 1

Linkage disequilibrium (r2) values according to distance between pairs of markers

SNP and haplotype annotation and gene networks

The SNPs and the genomic regions harboring all of the haplotyped loci identified were annotated using the Variant Effect Predictor (VEP) Ensembl API [33]. The identified genes were used to predict a gene interaction network using the GeneMANIA Cytoscape plug-in [34], based on the source organism Homo sapiens.

Results

Genome-wide association analysis

The single-SNP GWAA identified eight variants influencing WBSF on BTA 3, 4, 9, 10 and 11 (Table 1). The SNP rs134499129 (BTA3) explained the greatest amount of VA (0.072 kg2) and rs41623448 (BTA10) had the largest allele substitution effect (0.73 ± 0.09 kg). Four of the detected variants (rs109294639, rs134499129, rs41595711 and rs42732955), located in NOS1AP and SUCLG1 were intronic, and four variants (rs43490295, rs137367597, rs41623448 and rs136174419) were intergenic. The intronic variant rs109294639 was in moderate LD with rs134499129 and rs41595711 (r2 = 0.26 and 0.24, respectively), whereas rs134499129 and rs41595711 were in strong LD (r2 = 0.71). The intergenic variants rs41623448 and rs136174419 are located near TBCD21 and SUCLG1, at a distance of 37 and 68 kb, respectively. The SNPs rs136174419 (near SUCLG1) and rs42732955 (SUCLG1, intron 1) were in strong LD (r2 = 0.79) at a distance of 74 kb. However, rs43490295, rs137367597 and rs41623448 are not in LD with any neighboring SNP markers.
Table 1

SNP markers that explained the greatest additive genetic variance for meat tenderness in Nelore cattle

SNP marker

BTA

Position (bp)

Allele frequencies (effectsa)

Var (kg2)

Gene

rs109294639

3

7,384,182

T = 0.094 (− 0.40 ± 0.08)

C = 0.906 (0.40 ± 0.08)

0.027

NOS1AP (intron1)

rs134499129

3

7,390,035

A = 0.163 (− 0.51 ± 0.06)

G = 0.837 (0.51 ± 0.06)

0.072

NOS1AP (intron1)

rs41595711

3

7,391,544

T = 0.185 (− 0.30 ± 0.06)

C = 0.815 (0.30 ± 0.06)

0.027

NOS1AP (intron1)

rs43490295

4

1,008,553

G = 0.120 (− 0.37 ± 0.07)

A = 0.880 (0.37 ± 0.07)

0.028

rs137367597

9

1,116,610

C = 0.135 (−0.34 ± 0.07)

A = 0.865 (0.34 ± 0.07)

0.028

rs41623448

10

20,486,971

C = 0.064 (−0.73 ± 0.09)

T = 0.936 (0.73 ± 0.09)

0.063

near TBCD21

rs136174419

11

50,332,078

A = 0.112 (−0.37 ± 0.07)

G = 0.888 (0.37 ± 0.07)

0.028

near SUCLG1

rs42732955

11

50,406,682

A = 0.124 (−0.39 ± 0.07)

G = 0.876 (0.39 ± 0.07)

0.032

SUCLG1 (intron1)

SNP single nucleotide polymorphism, BTA Bos taurus autosome, Var SNP marker additive genetic variance

aAllele substitution effects from GEMMA software (kg)

Using the haplotype-based analysis we identified haplotyped loci that explained the greatest amounts of VA for WBSF (Table 2). Thirty-three putative QTL regions for WBSF were detected, with 19 being identified by at least two different haplotype-based analyses and five QTLs were identified in all of the haplotype-based analyses. The SW3, SW5, SW7, SW9 and SW11 analyses identified 57, 61, 42, 39, and 21% of all 33 putative QTL regions, respectively, whereas, the analyses using SW3 and SW5 jointly detected 88% of all 33 QTL regions. Increasing the number of SNPs included in the haplotyped loci did not always lead to an increase in the amount of VA that was explained by the haplotypes. Some QTLs appeared to only be captured using larger haplotypes while other QTLs could only be detected using smaller haplotypes.
Table 2

QTL regions for meat tenderness detected using haplotype-based analysis on variable-sized sliding windows

BTA

Region

Position (bp)

Dist

LD

Additive genetic variance (kg2)

SNP

SW3

SW5

SW7

SW9

SW11

1

rs43761056 – rs137207255

6,317,864 – 6,324,488

6625

M

0.032

0.036

1

rs42493480 – rs42493494

125,981,534 – 126,011,455

29,922

M

0.031

0.034

0.038

3

rs109872503 – rs110244139

7,371,235 – 7,460,489

89,255

M

0.072

0.098

0.071

0.085

0.086

0.089

3

rs132795858 – rs134534136

29,474,206 – 29,610,884

136,679

S

0.038

0.049

0.051

0.051

0.050

3

rs132763845 – rs109427593

90,594,180 – 90,664,268

70,089

M

0.036

0.045

3

rs134111725 – rs133976586

93,949,186 – 94,067,553

118,368

S

0.040

4

rs43490295

1,008,553

N

0.028

4

rs137252969 – rs43385178

24,143,662 – 24,225,606

81,945

W

0.037

4

rs110513194 – rs135604613

27,834,564 – 27,894,006

59,443

S

0.042

5

rs136407209 – rs110430223

7,525,121 – 7,583,435

58,315

S

0.030

5

rs41587994 – rs109349300

18,434,417 – 18,465,179

30,763

S

0.036

0.034

8

rs133253155 – rs135484797

92,216,117 – 92,289,515

73,399

S

0.033

0.034

0.033

9

rs137367597

1,116,610

N

0.028

9

rs110938040 – rs135589084

12,339,368 – 12,406,450

67,083

M

0.048

0.053

9

rs136867942 – rs135252570

21,441,424 – 21,497,606

56,183

M

0.037

9

rs133390891 – rs43600182

61,168,282 – 61,197,874

29,593

S

0.037

9

rs110730224 – rs109847831

66,977,872 – 67,045,614

67,743

M

0.040

0.039

0.040

0.040

9

rs135160781 – rs110936646

100,716,451 – 100,721,452

5002

S

0.034

10

rs137812088 – rs133615734

20,448,593 – 20,541,764

93,172

M

0.063

0.040

0.041

0.038

0.044

0.052

10

rs134006987 – rs136312573

43,692,759 – 43,726,489

33,731

S

0.033

0.031

11

rs109122230 – rs41655045

5,751,331 – 5,768,629

17,318

M

0.033

11

rs43755797 – rs137032372

43,129,117 – 43,135,745

6629

M

0.032

11

rs136174419 – rs42731923

50,332,078 – 50,417,494

85,417

S

0.032

0.069

15

rs132639440 – rs136091960

72,439,829 – 72,470,564

30,736

M

0.036

0.040

0.051

17

rs110304377 – rs42926409

70,788,438 – 70,935,589

147,152

M

0.035

0.043

0.054

0.056

18

rs137081181 – rs134146295

14,849,540 – 14,991,573

142,034

S

0.039

0.041

0.043

0.043

0.045

24

rs135709192 – rs136715705

47,570,379 – 47,596,815

26,437

S

0.033

0.033

0.034

0.034

0.034

24

rs110779214 – rs109148899

48,585,933 – 48,632,298

46,366

S

0.038

0.038

24

rs136382747– rs135235176

55,868,854 – 55,975,740

106,887

M

0.040

25

rs110607501 – rs108984883

17,590,025 – 17,598,054

8030

S

0.033

0.034

0.034

26

rs133176306 – rs109605337

19,035,737 – 19,066,768

31,032

S

0.034

0.038

28

rs134774253 – rs42146826

26,876,269 – 26,885,074

8806

S

0.033

29

rs136755211 – rs42192064

43,980,089 – 44,042,363

62,275

S

0.032

0.032

BTA Bos taurus autosome, Dist distance, LD linkage disequilibrium, SNP single nucleotide polymorphism, SW Sliding window haplotype, SW3 SW of three SNPs, SW5 SW of five SNPs, SW7 SW of seven SNPs, SW9 Haplotype SW of nine SNPs, SW11 SW of eleven SNPs, S strong LD (r2 > 0.6), M moderate LD (0.2 < r2 < 0.6), W weak LD (0.1 < r2 < 0.2), N not LD (r2 < 0.1)

Putative QTLs identified are located on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29. SNPs within most of the QTL regions found in this study were in strong or medium pair-wise LD and the length of the QTL regions varied from 5 to 147 kb with an avarage of 63 kb, as shown in Table 2. The QTL with the largest effect was identified in all of the haplotype window size analyses, however, the window for the SW3 analysis explained the greatest amount of VA (0.098 kg2). This QTL region harbors NOS1AP (BTA3) and includes three SNPs (rs109294639, rs134499129 and rs41595711) identified in the single-SNP GWAA.

The SNPs rs43490295 and rs137367597 on BTA4 and BTA9 (Table 2), respectively, were only detected by the single-SNP GWAA. On the other hand, the haplotype-based association analyses captured many QTL regions that were not identified by the single-SNP GWAA. Full results for single-SNP and haplotype-based association analyses for each number of alleles at haplotyped loci are shown in Additional files 1, 2, 3, and 4.

Gene network analysis

Figure 2 illustrates the interactions among the genes located in genomic regions detected as being influencing WBSF in this study. The constructed gene interaction network revealed that 66.7% of the constituant genes are known to be co-expressed in humans. Moreover, 26.7% of the genes co-localize indicating that they are expressed in the same tissue or that their proteins are both identified in the same cellular locations. In addition, 70% of the genes interact, suggesting that they are functionally associated. The gene interaction network also revealed 20 genes, presented as grey circles, that interact with genes in the genomic regions that cause variation in WBSF. Among these, CAPN1 is related to genes that were found to be influencing WBSF in this study. CAPN1 interacts with NEFH, NIN, PTPRK and THOC5 and is co-expressed with MRPL49, SUCLG1, TM7SF2 and NF2.
Fig. 2

Gene interaction network for genes in QTL regions for meat tenderness (WBSF). Genes presented as black circles were located in the QTL regions and genes that interact with those as grey circles. Edges in purple, green and red represent co-expression relationships, genetic interactions and co-localizations, respectively

Discussion

We performed a single-SNP GWAA and investigated a strategy for haplotype-based analysis using variable-sized sliding windows to detect genomic regions that influence WBSF. The desirable alleles (negative effects) for all eight SNPs identified influencing WBSF using single-SNP GWAA were in low frequency (Table 1), indicating that selection for these desirable alleles could improve tenderness in Nelore cattle. We found haplotypes affecting WBSF that were not detected in the single-SNP analysis (Table 2). This is consistent with previous studies that have shown that haplotype-based analysis provides a greater power for QTL detection than does single SNP analysis [15, 16, 35, 36]. The most likely reason for this finding is that QTL are more likely to be in strong LD with a multi-marker haplotype than with a single biallelic SNP, thus haplotype-based association methods have the opportunity to capture greater numbers of associations [10, 37, 38].

Genome-wide association analysis

The single-SNP analysis detected two SNPs (rs43490295 and rs137367597) that were not identified in the haplotype-based analyses. These SNPs explained the smallest amounts of QTL variance among the detected SNPs (0.028 kg2) and they were not in LD with neighboring SNPs. Using simulated data, [21] showed that haplotypes did not provide an advantage for detecting QTL with small effect sizes. In addition, haplotype-based analysis may also not have captured these signals because haplotype-based tests tend to be more powerful when moderate to high levels of LD exist in a chromosome region [39].

The variable-sized sliding window haplotype analysis strategy was used to ensure that every region of the genome was included in the analysis and that causal loci could be spanned by haplotyped regions. However, the number of contiguous SNPs to include in a haplotype window is a variable that requires optimization [9, 10]. The optimal haplotype size will vary according to SNP density, the patterning of LD throughout the genome and the genetic architecture of trait variation [40, 41]. We investigated five window sizes for haplotypes, containing 3, 5, 7, 9 or 11 SNP markers (SW3, SW5, SW7, SW9 and SW11, respectively). The choice of a maximum window length of 11 SNP markers was made based upon computational efficiency. The SW5 analysis detected the largest number of haplotypes loci followed by the SW3 analysis. This result may be because longer haplotype windows are more likely to introduce analytical problems such as high rates of recombination among distal SNPs resulting in excessive numbers of haplotypes, creating noise and computer memory problems [39, 42], and reducing the benefits of improved LD between haplotypes and causal variants [43].

Grapes et al.[35, 44] also found that the power to detect QTL improved as haplotype length increased to 6 SNP markers and decreased thereafter using a simulated bovine data set. However, [45] has shown that haplotypic diversity was best captured by a 20-marker sliding window in U.S. Angus cattle. The extent of LD in Nelore is much less than in Angus [46], suggesting that smaller window sizes will optimally capture haplotypic diversity in indicine cattle breeds. This assumption is supported by the length of all haplotype windows found in this study that ranged from 5 kb to 147 kb, and the SNPs within most of the QTL regions were in strong or medium pair-wise LD (Table 2). As shown in Fig. 1, the useful LD in our population extends for less than 100 kb (r2 < 0.15), which is consistent with the findings of [47]. This suggests that haplotypes based on sliding windows of size no more than 100 kb will capture the LD genome-wide. On the other hand, some putative QTLs were detected only by larger haplotypes. One possible explanation is that such QTL have small to very small effects on WBSF, therefore many informative SNPs grouped would have larger aggregated effects [22]. Other hypothesis is that only the allele frequencies of the larger haplotypes were similar to the QTL allele frequencies in these regions, which allowed their detection [21, 38]. Thus, since the genetic architecture and population history differ across genes and traits, it is not reasonable to expect that one single method would be superior at detection of all QTL [21].

Genes influencing WBSF

The SNP and haplotype-based association analyses identified 37 candidate genes influencing WBSF in Nelore cattle (Table 3). Among these, five genes (GAS8, OLFML3, TWIST1, GPRC5B, and HIPK1) are involved in myogenesis, which is responsible for generating muscle tissue during embryonic development, regeneration of the mature skeletal musculature and maintenance of tissue homeostasis [48, 49, 50, 51, 52].
Table 3

Genes located in QTL regions for meat tenderness in Nelore cattle

BTA

Position (bp)

Genes

Gene Name

Gene type

1

6,317,864 – 6,324,488

1

125,981,534 – 126,011,455

3

7,371,235 – 7,460,489

ENSBTAG00000010158

NOS1AP

Protein coding

3

29,474,206 – 29,610,884

ENSBTAG00000024319

LOC107132293

Pseudogene

ENSBTAG00000011322

HIPK1

Protein coding

ENSBTAG00000011327

OLFML3

Protein coding

3

90,594,180 – 90,664,268

3

93,949,186 – 94,067,553

ENSBTAG00000002910

ECHDC2

Protein coding

ENSBTAG00000003746

SCP2

Protein coding

ENSBTAG00000026032

FERD3L

Protein coding

4

1,008,553

4

24,143,662 – 24,225,606

ENSBTAG00000010168

4

27,834,564 – 27,894,006

ENSBTAG00000039955

ZYG11A

Protein coding

ENSBTAG00000046922

TWIST1

Protein coding

5

7,525,121 – 7,583,435

ENSBTAG00000009852

NAV3

Protein coding

5

18,434,417 – 18,465,179

8

92,216,117 – 92,289,515

9

1,116,610

9

12,339,368 – 12,406,450

-

-

-

9

21,441,424 – 21,497,606

9

61,168,282 – 61,197,874

ENSBTAG00000020713

BACH2

Protein coding

9

66,977,872 – 67,045,614

ENSBTAG00000020829

PTPRK

Protein coding

9

100,716,451 – 100,721,452

10

20,448,593 – 20,541,764

ENSBTAG00000007800

TBC1D21

Protein coding

10

43,692,759 – 43,726,489

ENSBTAG00000020281

NIN

Protein coding

11

5,751,331 – 5,768,629

ENSBTAG00000042179

U6

SnRNA

ENSBTAG00000046971

LOC107131213

Protein coding

11

43,129,117 – 43,135,745

ENSBTAG00000016534

BCL11A

Protein coding

11

50,332,078 – 50,417,494

ENSBTAG00000006075

SUCLG1

Protein coding

ENSBTAG00000042444

U6

SnRNA

15

72,439,829 – 72,470,564

ENSBTAG00000037580

MSANTD3

Protein coding

17

70,788,438 – 70,935,589

ENSBTAG00000013150

THOC5

Protein coding

ENSBTAG00000013153

NF2

Protein coding

ENSBTAG00000013152

NIPSNAP1

Protein coding

ENSBTAG00000013147

NEFH

Protein coding

18

14,849,540 – 14,991,573

ENSBTAG00000007096

GAS8

Protein coding

ENSBTAG00000025283

LOC101904595

Pseudogene

  

ENSBTAG00000029640

U1

SnRNA

ENSBTAG00000033441

SHCBP1

Protein coding

24

47,570,379 – 47,596,815

24

48,585,933 – 48,632,298

24

55,868,854 – 55,975,740

25

17,590,025 – 17,598,054

ENSBTAG00000019596

GPRC5B

Protein coding

ENSBTAG00000044092

IQCK

Protein coding

26

19,035,737 – 19,066,768

28

26,876,269 – 26,885,074

29

43,980,089 – 44,042,363

ENSBTAG00000005069

TM7SF2

Protein coding

ENSBTAG00000005073

ZNHIT2

Protein coding

ENSBTAG00000005075

MRPL49

Protein coding

ENSBTAG00000005076

SYVN1

Protein coding

ENSBTAG00000015499

VPS51

Protein coding

ENSBTAG00000020807

FAU

Protein coding

Several genes located in the QTL regions for WBSF, such as NEFH, FERD3L, NAV3, BCL11A, ZNHIT2, NOS1AP and SHCBP1, participate in neurogenesis processes (GO:0022008) or the structure and function of neurons. Neurogenesis is essential for skeletal muscle development and regeneration [53]. Motor neuron, a neuromuscular junction component, regulates skeletal muscle contraction [54] and also has a role in the development and differentiating of muscle fibers [55]. NEFH, SHCBP1 and NOS1AP have previously been associated with WBSF in Nelore steers [6]. Moreover, NOS1AP has been associated with longissimus muscle area and marbling score in cattle [56]. BCL11A has been associated with marbling score in Canchim beef cattle [57] and a QTL region for meat tenderness harbored ZNHIT2 in pigs [58].

The NIN and NF2 genes putatively affect skeletal muscle structure and composition. NIN encodes a microtubule nucleation protein, which has previously been associated with skeletal muscle differentiation [59]. NF2 participates in actin cytoskeleton organization (GO:0030036), which may explain the association with WBSF after 7 days of aging in Nelore steers found by [6]. This process results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins (GO:0030036). Actin is a myofibril protein, one of the major components of the sarcomere, which has been reported to affect meat tenderness [60].

Genes involved in lipid and fatty acid metabolism, such as SUCLG1, THOC5, NIPSNAP1, IQCK, TM7SF2, VPS51, PTPRK, ECHDC2 and SCP2, were also found to influence meat tenderness. Positive effects of lipid on meat tenderness are likely due to the lipid within cells in the perimysium, which separate muscle fiber bundles [61]. Furthermore, [62] found a genetic correlation between meat tenderness and fatty acid abundance using animals from the same population as used in the present study. THOC5 was found to be differentially expressed between low and high marbling beef cattle in a longissimus dorsi muscle transcriptome analysis [63]. [58] reported that TM7SF2 and VPS51 were located within a QTL region for meat tenderness in pigs. The protein encoded by PTPRK has been associated with marbling in beef cattle [64]. ECHDC2 expression is negatively correlated with non-esterified fatty acid abundance in pigs [65]. SCP2 was down-regulated in pork with high intramuscular fat content [66] and was associated with WBSF after 7 and 14 days of aging in Nelore steers [6].

There was no evidence of a biological link or biological mechanism connecting TBC1D21, MSANTD3, MRPL49, SYVN1, FAU, ZYG11A or BACH2 genes to meat tenderness. These genes may play a role in the regulation of transcription or translation, or may interact with important genes for meat tenderness as shown in Fig. 2. TBC1D21 encodes a GTPase-activating protein (GO:0090630) for Rab family proteins, which are involved in spermatogenesis [67]. Its association with meat tenderness appears to be through interactions with BACH2 and PTPRK. MSANTD3 is a member of the MSANTD3 family which contains DNA binding domains for Myb proteins and the SANT domain family. [68] speculated that MSANTD3 may be a transcription factor. In addition, MSANTD3 interacts with BCL11A, NIN, NOS1AP, SCP2 and TWIST1. A QTL region for meat tenderness in pigs harbors MRPL49, SYVN1 and FAU [58]. ZYG11A encodes a protein that plays an important role in the regulation of ubiquitin-protein transferase activity (GO:0051438). A ZYG11A family member has been related to marbling in beef cattle [63]. BACH2 is a transcription repressor and plays essential roles in the regulation of B cell development. B cells function in the humoral immunity component of the adaptive immune system by secreting antibodies [69]. BACH2 has been related to intramuscular fat content in bulls [70] and was associated with WBSF in Nelore steers [6].

Gene network analysis

The gene network analysis (Fig. 2) revealed interactions among the genes within QTL regions for WBSF and other genes that were not detected as influencing WBSF in the present study (grey circles), which are enriched for their functions in the regulation of transcription (GO:0006355), cell differentiation (GO:0030154), lipid metabolic process (GO:0006629), regulation of angiogenesis (GO:0045766), thyroid hormone transport (GO:0070327), proteolysis (GO:0006508), cellular response to insulin stimulus (GO:0032869) and cytoskeleton organization (GO:0007010). Among these, is a well-known gene influencing WBSF, CAPN1, which is responsible for the postmortem breakdown of myofibrillar proteins and seems to be the primary enzyme involved in the postmortem tenderization process [71]. The CAPN1 interacts with TM7SF2, ZNHIT2, MRPL49, SYVN1, VPS51 and FAU genes that are located in a QTL region for meat tenderness in this study. In addition, all of these genes are located near CAPN1 on BTA29 and their SNPs are in strong LD with SNPs located in CAPN1 in this population (with maximum r2 = 0.86).

Conclusions

This study demonstrates that GWAA using haplotypes based on variable-sized sliding windows provides substantially more power to detect QTL than does single-SNP analysis, suggesting that this methodology should be considered for genomic predictions for WBSF and other traits. Analyses performed with smaller haplotype windows (3 and 5 SNPs) detected a higher proportion of QTLs than the analyses that used larger SNP windows. However, no single sliding window analysis identified all of the QTL that were found in the analyses using window sizes from 3 to 11 SNPs. This suggests that haplotype-based GWAA should employ several window sizes in order to detect the largest number of putative QTL. Likewise, the single-SNP analysis found two putative QTL that were not found by the haplotype-based analyses. While these may be type I errors, they may also be regulatory variants.

We identified thirty-seven candidate genes influencing meat tenderness that participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. These findings contribute to a better understanding of the biological mechanisms underlying meat tenderness in Nelore cattle. Further validation of these genes and polymorphisms in different populations would contribute to their use in breeding programs for Nelore cattle.

Notes

Acknowledgements

The authors would like to acknowledge the DeltaGen, PAINT and Cia de Melhoramento breeding programs for providing data. We also would like to acknowledge the members of the Animal Breeding and Genetics Postgraduate Program (PPGMA) of the São Paulo State University (FCAV/Unesp) for their assistance with collecting data.

Funding

This research was supported by the São Paulo Research Foundation (FAPESP) and National Council of Technological and Scientific Development (CNPq) grants: #2009/16118–5 and #480369/2009–7, respectively. São Paulo Research Foundation (FAPESP) also funds the scholarships #2013/00035–9 and 2014/23013–3 to Camila U. Braz.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available because they include confidential information from the companies that provided the samples. The datasets supporting the conclusions of this article are included within the article and its additional file.

Authors’ contributions

CUB conceived the study, analyzed and interpreted the results and, wrote the manuscript. TB, RE and FLBF helped conceive the study. JFT, RC, FB and LGA assisted with preparation of the manuscript and aided with interpretation of results; HNO assisted with analyses, preparation of the manuscript and interpretation of results. All authors read and approved the final manuscript.

Ethics approval

Use of animals was approved by the São Paulo State University (Unesp), School of Agricultural and Veterinary Science Ethical Committee (Approval No. 18.340/16).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12863_2019_713_MOESM1_ESM.pdf (55 kb)
Additional file 1: Manhattan plot for single-SNP genome-wide association analysis. (PDF 55 kb)
12863_2019_713_MOESM2_ESM.pdf (15 kb)
Additional file 2: QQ plot for single-SNP genome-wide association analysis. (PDF 14 kb)
12863_2019_713_MOESM3_ESM.pdf (8.4 mb)
Additional file 3: Manhattan plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 8648 kb)
12863_2019_713_MOESM4_ESM.pdf (2.1 mb)
Additional file 4: QQ plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 2144 kb)

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Animal Science DepartmentSão Paulo State University (Unesp)JaboticabalBrazil
  2. 2.Division of Animal SciencesUniversity of MissouriColumbiaUSA

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