List of Heritablilty Traits in Beef Cattle

• Journal List
• Asian-Australas J Anim Sci
• v.27(11); 2014 November
• PMC4213694

Asian-Australas J Anim Sci. 2014 Nov; 27(eleven): 1521–1525.

Genomic Heritability of Bovine Growth Using a Mixed Model

Received 2014 Apr 17; Revised 2014 Jun 24; Accepted 2014 Jul 8.

Abstruse

This written report investigated heritability for bovine growth estimated with genomewide single nucleotide polymorphism (SNP) information obtained from a Deoxyribonucleic acid microarray chip. Iii hundred lx seven Korean cattle were genotyped with the Illumina BovineSNP50 BeadChip, and 39,112 SNPs of 364 animals filtered by quality balls were analyzed to estimate heritability of body weights at 6, 9, 12, xv, xviii, 21, and 24 months of age. Restricted maximum likelihood gauge of heritability was obtained using covariance construction of genomic relationships amidst animals in a mixed model framework. Heritability estimates ranged from 0.58 to 0.76 for body weights at different ages. The heritability estimates using genomic data in this study were larger than those which had been estimated previously using full-blooded information. The results revealed a trend that the heritability for body weight increased at a younger age (6 months). This suggests an early genetic evaluation for bovine growth using genomic data to increase genetic merits of animals.

Keywords: Bovine Genome, Growth, Heritability, Mixed Model, Unmarried Nucleotide Polymorphism

INTRODUCTION

Genetic improvement of bovine growth is 1 of the almost important tasks for a higher productivity in the beef cattle industry, and thus its heritability as the portion of genetic variability to phenotypic variability has been a peachy concern to cattle geneticists. Heritability for growth traits of beef cattle has been estimated in a variety of breeds and populations. Traditionally, they were estimated based on pedigree information. This heritability estimated by the traditional method is hereafter called "classical heritability".

Nowadays high throughput genotyping has been bachelor. The apply of commercial Dna microarray bit is currently a routine in identifying genomewide clan signals for bovine circuitous phenotypes (Van Tassell et al., 2008; Snelling et al., 2010; Kim et al., 2011; Lu et al., 2013). Arable data on tens of thousands of markers from the chip experiment actually enables us not only to exam their genetic associations with phenotypes but also to assess genetic variability amidst individuals. Incorporating the genetic variability into the genetic association analysis in a mixed model would control population stratification and result in heritability estimate explained by polygenic effects (Yang et al., 2011). The objective of this study was to examine heritability estimates for growth traits of Korean cattle using genomic data of single nucleotide polymorphism (SNP) genotypes obtained with a Dna microarray chip.

MATERIALS AND METHODS

Animals and phenotypes

An indigenous beef cattle breed chosen Korean cattle (also named Hanwoo or Bos taurus coreanae) was used to estimate heritability of trunk weight. For characteristics and historical details of the breed, see Kim and Lee (2000) and Lee and Pollak (2002). Body weights of 367 steers under a controlled feeding program were collected from Korean Cattle Improvement Center, National Agronomical Cooperative Federation in Korea. The feeding program was described in Kim et al. (2011). Body weights were available at 6, 9, 12, 15, xviii, 21, and 24 months. The descriptive statistics for body weights are summarized in Table i. Their meat samples were obtained at slaughter in spring and fall of 2007 through 2009.

Table 1

Basic statistics for body weights (kg) of Korean cattle analyzed in this study

Age in months	Hateful	SD	CV	Minimum	Maximum
6	167.2	28.vii	17.1	92.0	262.5
9	231.1	28.3	12.3	153.7	314.5
12	326.0	39.viii	12.2	221.2	451.6
xv	395.1	37.1	ix.iv	292.two	510.six
18	489.2	44.4	9.ane	370.0	617.5
21	567.7	53.0	nine.iii	422.3	723.0
24	650.1	61.8	9.v	454.3	818.6

Genotyping

Genomic Dna was extracted from the tissue samples of animals using a commercial kit from Qiagen (QIAGEN Inc., Valencia, CA, U.s.a.). The bovine Illumina Infinium assay (BovineSNP50, Illumina, San Diego, CA, USA) with 54,001 SNPs across the whole bovine genome was used for genotyping. Genotypes were determined based on custom clusters developed with more 7,000 samples using Beadstudio software provided by Illumina. Every signal cluster was manually reviewed and appropriately fitted.

We excluded the samples with call rate <98% from the analysis of estimating heritability. Exclusion criteria for SNPs were call rate <98%, minor allele frequency <0.05, and deviation from Hardy-Weinberg equilibrium. The Hardy-Weinberg disequilibrium was tested using the chi-square statistic with ii degrees of liberty (p<0.001).

Statistical analysis

A mixed model was employed to estimate genetic and environmental variance components as following:

where y is a vector of body weights, β is a vector of fixed effects for contemporary group (yr and flavor, 6 levels) furnishings, Ten is the corresponding incidence matrix, yard is a vector of random polygenic furnishings nether the assumption of North ( 0 , G σ 1000 ii ) and e is a vector of random ecology furnishings under the supposition of N ( 0, ∣ σ east 2 ). Thou denotes a genetic relationship matrix with elements of pairwise human relationship coefficient (VanRaden, 2008; Yang et al., 2011), and the pairwise relationship was estimated by the following formula:

ψ - one ∑ i = 1 ψ ( north ij - 2 p i ) ( northward ik - 2 p i ) ii p i ( i - p i )

where ψ is the number of SNPs, n_ij(n_ik) is the number (0, 1, or 2) of the reference allele for the ith SNP of the jth (kth) individuals, and p_i is the frequency of the reference allele of the ith SNP. The σ g ii and σ eastward 2 are polygenic and environmental variance components, and | is an identity matrix.

The genetic and environmental variance components were estimated using restricted maximum likelihood (REML). Genome-wide Circuitous Trait Analysis (GCTA, version ii.0) which is a freely available program at http://www.complextraitgenomics.com/software/gcta was used to estimate the variance components (Yang et al., 2011). Heritability was calculated using the REML estimates as σ g 2 σ g 2 + σ e 2, and its variance was estimated using the negative changed of the average information matrix.

Further analysis was conducted to examine chromosomal contribution to heritability of body weight. Nosotros start calculated genetic human relationship coefficients using genomic information past chromosomes so to estimate heritability with the genetic relationship matrix for each chromosome.

We as well conducted a genetic association analysis to guess regression coefficients in testing associations of SNPs with body weights. Correlation of the regression coefficients between torso weights at different historic period was estimated.

RESULTS

Heritability was estimated using 364 samples and 39,112 SNPs remained subsequently the quality assurance filtering. The number of SNPs used in the analysis indicated that reasonably informative SNPs were included in the BovineSNP50 BeadChip for Korean cattle as reported earlier (Kim et al., 2011). Genetic association of torso weight with any individual SNP was not identified by the Bonferroni multiple test (p>1.83×ten⁻⁷).

Heritability estimates for trunk weights of Korean cattle ranged from 0.58 to 0.76 (Effigy ane). They showed a tendency that the heritability was larger at a smaller historic period. For case, the heritability estimate was largest for torso weight at historic period of 6 months and smallest for trunk weight at age of 2 years.

Genomic heritability for torso weight of Korean cattle by age. The genomic heritability was estimated using bovine genomic information of 39,112 single nucleotide polymorphisms.

The chromosomal contribution to heritability of torso weight was heterogeneous by historic period in months (Effigy ii). The contribution of chromosome 24 to heritability decreased with a larger age whereas the contribution of chromosome thirteen increased with a larger age.

Heritability for torso weight of Korean cattle using data partitioned past chromosome.

Regression coefficients were obtained from testing genetic associations of SNPs with body weights. Their correlation estimates betwixt body weights at different historic period ranged from 0.66 to 0.98 (Effigy 3). They decreased with a large difference in age.

Correlation of examination statistics (regression coefficients) between trunk weights at different age. Ages in months for body weight are presented on the diagonal. Besprinkle plots for the regression coefficients are upper-diagonal. Correlation estimates of the regression coefficients between body weights at different age are lower-diagonal. All the correlation estimates were significant (p<0.001).

Give-and-take

The current written report showed heritability estimates of 0.58 to 0.76 for body weights of beef cattle using genomic information of SNP genotypes obtained with the BovineSNP50 BeadChip. They were larger than classical heritability estimated using pedigree information in previous studies. For instance, the classical heritability estimates for body weights of beef cattle ranged from 0.32 to 0.52 for Angus cattle (Davis and Simmen, 2006), from 0.18 to 0.49 for Korean cattle (Kim et al., 2000), from 0.19 to 0.25 for Simmental cattle (Lee et al., 1997), and from 0.26 to 0.35 for the Usa Meat Animal Research Eye Three composite breed (Rolfe et al., 2011). We doubtable that the larger heritability might be produced at least partially by the utilize of genomic relationships although it might be influenced by the heterogeneous breeds, selection programs, and environments.

Since the SNP markers of the DNA chip used in this study were sparsely located in the bovine genome, heritability estimated with an increasing number of SNP markers would take been probably even larger than those estimated in the current study as suggested past simulation studies (Zhang et al., 2011; Ryoo and Lee, 2014). Most markers significantly associated with body weights were expected to link to functional genetic variants, and moreover both functional variant itself and variants in strong linkage with it might take been excluded from the analysis.

Thus, the phenotypic variability for growth of beef cattle might be explained by genomic information improve than by pedigree information. This is opposite from a general tendency for human heritability. That is, heritabilities for human being complex traits take been underestimated with genomic information comparison to those with pedigree information (Manolio et al., 2009). It has been called 'missing heritability', and this missing heritability might be attributed to the genetic relationships diluted by noncontributing markers (Ryoo and Lee, 2014) and to the inflated heritability from shared ecology factors (Zuk et al., 2012). In practice, heritability might be underestimated by diluted genetic relationships with genomic information or with pedigree information. Furthermore, the underestimation becomes more serious with wrong information, specially with wrong full-blooded or misidentification (Lee and Pollak, 1997).

The electric current study showed a trend that the heritability estimates for body weight decreased at older ages. This suggests a necessity of an early genetic evaluation for the growth traits of animals because their genetic merits might be more explained at an earlier age, 6 months in the current written report. Additional association studies of body weight at an early age with previously identified variants for growth of Korean cattle might be useful for identifying stronger signals. The genomic sequence variants identified for association with growth or growth-related traits of Korean cattle might include intragenic SNPs of the genes encoding dishevelled segment polarity protein 1 for eye musculus area (Kim et al., 2011), mitogen-activated protein kinase kinase 6 (MAP2K6) and uncoupling protein 2 for carcass weight (Ryu et al., 2012), and phosphodiesterase 1B (PDE1B) for Longissimus dorsi muscle area (Shin et al., 2012) and intergenic SNPs of rs110228023 for centre muscle area (Kim et al., 2011) and rs29012331 for yearling weight (Kim et al., 2014). Furthermore, associations of variants identified for meat quality with an early torso weight might be useful to make up one's mind whether their alleles are benign or conflicting to both quantity and quality of meat in breeding strategy. For example, sequence variants of homo immunodeficiency virus blazon I enhancer binding protein 3 (Kim et al., 2011) and MAP2K6 (carcass weight) (Ryu et al., 2012) were associated with marbling score of Korean cattle, and variants of thyroid hormone responsive (La et al., 2013), peroxisome proliferator-activated receptor gamma (Oh et al., 2012b), PDE1B (Shin et al., 2012), and fatty acrid synthase (Oh et al., 2012a) were associated with fat acid composition. We doubtable their heterogeneous effects on body weights past historic period because the correlation of regression coefficients for genetic associations decreased with a large difference in historic period.

This study showed a novel heritability judge of bovine growth using genomic information, which was larger than those traditionally estimated with pedigree data. Also, the heritability estimates by age revealed a tendency that the heritability increased for body weight at an earlier age. An early genetic evaluation using genomic data might be suggested to improve genetic merits for growth of Korean cattle.

ACKNOWLEDGMENTS

This study was supported by the Bones Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Applied science (Grant No. 2012002096).

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213694/

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