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. 2020 Mar 4;11:163. doi: 10.3389/fgene.2020.00163

Identification of Whole-Genome Significant Single Nucleotide Polymorphisms in Candidate Genes Associated With Serum Biochemical Traits in Chinese Holstein Cattle

Kerong Shi 1,*, Fugui Niu 1, Qin Zhang 1, Chao Ning 1, Shujian Yue 1, Chengzhang Hu 1, Zhongjin Xu 1, Shengxuan Wang 1, Ranran Li 1, Qiuling Hou 1,*, Zhonghua Wang 1,*
PMCID: PMC7065260  PMID: 32194633

Abstract

A genome-wide association study (GWAS) was conducted on 23 serum biochemical traits in Chinese Holstein cattle. The experimental population consisted of 399 cattle, each genotyped by a commercial bovine 50K SNP chip, which had 49,663 SNPs. After data cleaning, 41,092 SNPs from 361 Holstein cattle were retained for GWAS. The phenotypes were measured values of serum measurements of these animals that were taken at 11 days after parturition. Two statistical models, a fixed-effect linear regression model (FLM) and a mixed-effect linear model (MLM), were used to estimate the association effects of SNPs. Genome-wide significant and suggestive thresholds were set up to be 1.22E−06 and 2.43E−06, respectively. In the Chinese Holstein population, FLM identified 81 genome-wide significant (0.05/41,092 = 1.22E−06) SNPs associated with 11 serum traits. Among these SNPs, five SNPs (BovineHD0100005950, ARS-BFGL-NGS-115158, BovineHD1500021175, BovineHD0800028900, and BTB-00442438) were also identified by the MLM to have genome-wide suggestive effects on CHE, DBIL, and LDL. Both statistical models pinpointed two SNPs that had significant effects on the Holstein population. The SNP BovineHD0800028900 (located near the gene LOC101903458 on chromosome 8) was identified to be significantly associated with serum high- and low-density lipoprotein (HDL and LDL), whereas BovineHD1500021175 (located in 73.4Mb on chromosome 15) was an SNP significantly associated with total bilirubin and direct bilirubin (TBIL and DBIL). Further analyses are needed to identify the causal mutations affecting serum traits and to investigate the correlation of effects for loci associated with fatty liver disease in dairy cattle.

Keywords: GWAS, serum biochemical traits, cattle, Chinese Holstein, SNPs, QTL

Genome-wide association study was proven to be a powerful tool for detecting genetic variants associated with economically important traits, such as production (Jung et al., 2013; Yue et al., 2017; Yan et al., 2019), reproduction (Sahana et al., 2011), and disease traits (Pant et al., 2010). This study was to identify SNPs with significant association effects on serum traits in Chinese Holstein and Jersey cattle through the use of GWAS.

The experimental population consisted of 399 Chinese Holstein dairy cows, all of which were raised on the same farm. The phenotypes were the measured values for 23 serum traits with the serum being sampled from each cow at 11 days after parturition within a month (between September and October). The serum traits were adenosine deaminase (ADA), serum albumin (ALB), alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate aminotransferase (AST), β-hydroxybutyric acid (BHB), cholinesterase (CHE), creatine kinase (CK), serum creatinine (CR), direct bilirubin (DBIL), glucose (GLU), high density lipoprotein (HDL), L-lactate dehydrogenase (LDHL), low density lipoprotein (LDL), non-esterified fatty acid (NEFA), serum urea nitrogen (SUN), total bilirubin (TBIL), total cholesterol (TCHO), triglyceride (TG), total protein (TP), urea acid (UA), very low density lipoprotein (VLDL), and γ-glutamyltransferase (γ-GT). The statistical summary of these phenotypes is listed in Supplementary Table S1.

All animals were genotyped with a bovine 50K SNP chip (49,663 SNPs). SNPs from the X chromosome were counted due to the overall majority of female individuals in the study population. After the data quality control procedure (Yue et al., 2017; Yan et al., 2019), 361 animals with 41,092 SNP genotypes were finally retained for the subsequent GWAS analysis. Physical map length, the number of SNPs, and the SNP density on each chromosome, before and after the data cleaning procedure, are shown in Supplementary Table S2.

A pair-wise linkage disequilibrium (LD) analysis was conducted for the Holstein population. The results showed high genome-wide similarity of LD patterns among the cattle populations (Supplementary Figure S1). The similarity might reflect the sharing of breeding histories among the cattle. Multi-dimensional scaling (MDS) analysis of 12,380 independent SNP markers (Purcell et al., 2007; Yue et al., 2017; Yan et al., 2019) with r2 < 0.2 (Wang et al., 2009), using the first and the second components, indicating that there was slight population stratification (Supplementary Figure S2). To better correct cryptic population stratification, the first MDS component was used to be the covariate in the following genome-wide association analysis (Supplementary Figure S3).

According to the previous method (Yue et al., 2017), a GWAS analysis was carried out by two statistical models, a fixed-effect linear model (FLM) and a mixed-effect linear model (MLM), implemented by the PLINK software package V1.07 (Purcell et al., 2007) and the GCTA (v1.2.4) software package (Yang et al., 2011), respectively. FLM is of the form:

y=W⁢α+x⁢β+e

where y is a vector of phenotypic values; α is a vector of fixed effects including the population mean and the first MDS component; W is the designed matrix for fixed effects; β is the marker effect; x a vector of marker genotypes; and e is the random errors with distribution of N⁢(0,I⁢σe2). Here, σe2 is the residual variances. For MLM, an additive genomic relatedness matrix is included to control the type I error, which is of the form

y=W⁢α+x⁢β+Zu+e

where Z is the designed matrix, and u is the vector of random effects with the distribution of N⁢(0,K⁢σa2). Here, σa2 is the additive genetic variances and K is the additive genomic relatedness matrix. The other symbols are the same as the FLM. Bonferroni corrections for the genome-wide significance and suggestive thresholds (Mapholi et al., 2016; Kerr et al., 2017) were computed to be 1.22E−06 (=0.05/41,092) and 2.43E−06 (=0.1/41,092), respectively.

A GWAS based on the FLM identified 81 SNPs with genome-wide significant (1.22E−06) association effects on 11 serum traits (Table 1) in the Holstein cattle population. A GWAS based on the MLM identified 15 SNPs as having genome-wide suggestive effects on 11 serum traits (Table 2). Among these SNPs, five SNPs (BovineHD0100005950, ARS-BFGL-NGS-115158, BovineHD1500021175, BovineHD0800028900 and BTB-00442438) were identified by both the FLM and MLM to have genome-wide suggestive effects on CHE, DBIL, and LDL.

TABLE 1.

Genome-wide significant SNPs that were identified to be associated with serum indexes in Chinese Holstein cattle using a fixed linear model.

Trait1 SNP-name Chr Position Model2 P-value Nearest gene Distance3
AST BovineHD2600000138 26 1234319 FLM 1.29E−08 – –
ALP chr5 113679525# 5 113679525 FLM 1.13E−07 TCF20 Within
BovineHD0500032827# 5 113679789 FLM 1.27E−07 TCF20 Within
chr5 113680107# 5 113680107 FLM 1.13E−07 TCF20 Within
chr5 113680281# 5 113680281 FLM 1.25E−07 TCF20 Within
chr5 113682858# 5 113682858 FLM 8.33E−08 TCF20 Within
ARS-BFGL-NGS-33155 5 113787757 FLM 1.43E−07 LOC104972595 U 37356
ARS-BFGL-NGS-5845 9 99978975 FLM 2.79E−07 LOC100336821 Within
BovineHD1700011465 17 41431300 FLM 2.68E−07 RXFP1 Within
BovineHD2300007148 23 25747610 FLM 5.28E−07 LOC101903077 U 50184
BovineHD2800010983 28 39619766 FLM 4.66E−07 CCSER2 Within
BTB-00990573 28 39950373 FLM 2.35E−08 – –
BovineHD2800012663 28 44105256 FLM 2.03E−07 SLC18A3 U 18412
TCHO BovineHD0500019371 5 69065329 FLM 7.41E−07 APPL2 Within
BovineHD2600013701 26 47604960 FLM 7.46E−08 CLRN3 U 30491
Hapmap28862-BTA-149586 30 125860499 FLM 2.11E−07 PDK3 Within
CHE BovineHD0100005653 1 18939484 FLM 3.44E−07 CXADR Within
BovineHD0100005950* 1 20036999 FLM 7.81E−08 – –
BovineHD0800022235 8 74000163 FLM 1.01E−06 – –
Hapmap52146-ss46526966 9 95952795 FLM 3.63E−07 SNX9 Within
ARS-BFGL-NGS-115719 19 48801884 FLM 1.17E−06 SCN4A Within
BovineHD2900006235 29 21755373 FLM 6.96E−07 – –
ARS-BFGL-NGS-115158* 29 21828399 FLM 3.02E−07 – –
γ-GT BTB-01806486 26 4754531 FLM 1.31E−10 PCDH15 Within
TBIL BovineHD0200031819 2 110407486 FLM 1.48E−07 EPHA4 D 2123
BovineHD0700001976 7 6718398 FLM 3.72E−07 LOC100336881 D 13347
BovineHD0700003730 7 14112320 FLM 4.05E−07 LOC520104 Within
BovineHD0700018347 7 63443211 FLM 3.58E−07 CDX1 U 14171
ARS-BFGL-NGS-41157 7 73155944 FLM 5.56E−07 TRNAC-ACA U 85437
BovineHD0700021500 7 73162347 FLM 5.56E−07 TRNAC-ACA U 79034
ARS-BFGL-BAC-20850 14 9542083 FLM 7.54E−07 PHF20L1 Within
BovineHD4100011001 14 9854232 FLM 1.05E−06 KCNQ3 Within
ARS-USMARC-Parent-DQ846690-no-rs 14 10171919 FLM 1.06E−07 EFR3A Within
BovineHD1400002967 14 10512600 FLM 5.33E−07 – –
BovineHD1400003397 14 11737590 FLM 7.54E−07 FAM49B U 24208
BovineHD1500011673 15 42127831 FLM 1.83E−07 – –
BovineHD1500014738 15 51303719 FLM 4.17E−07 OR52K2 U 12321
BovineHD1500015826 15 54790260 FLM 1.19E−06 CHRDL2 Within
BovineHD1500021175*§ 15 73378270 FLM 2.80E−07 – –
BovineHD1800005102 18 16301576 FLM 1.10E−06 – –
BovineHD1800014694 18 49879114 FLM 1.19E−06 MAP3K10 Within
BovineHD1800017510 18 60710597 FLM 5.13E−07 LOC788928 Within
BovineHD2200007568 22 26040853 FLM 9.60E−07 CHL1 U 57003
Hapmap50029-BTA-55899 23 24181053 FLM 1.64E−07 PKHD1 Within
BovineHD2800006539 28 25438915 FLM 1.13E−07 KIF1BP Within
BovineHD2800006565 28 25578865 FLM 9.04E−07 LOC104976190 D 4819
BovineHD3000030626 30 110483950 FLM 1.20E−06 RPGR Within
BovineHD3000033677 30 119764357 FLM 2.51E−07 IL1RAPL1 Within
Hapmap38597-BTA-41420 30 119781376 FLM 4.50E−08 IL1RAPL1 Within
Hapmap56389-rs29012404 30 141044156 FLM 1.01E−06 TLR7 Within
DBIL BovineHD0400011958 4 43673293 FLM 1.18E−06 PHTF2 Within
BovineHD1400003397 14 11737590 FLM 1.12E−06 FAM49B U 24208
BovineHD1500021175*§ 15 73378270 FLM 2.18E−07 – –
BovineHD1800017510 18 60710597 FLM 2.91E−07 LOC788928 Within
BovineHD2600013030 26 46078929 FLM 5.88E−07 ADAM12 Within
ALT chr26 38656980 26 38656980 FLM 1.77E−10 RAB11FIP2 Within
LDHL BovineHD0100046573 1 117801064 FLM 1.17E−06 MED12L Within
HDL BovineHD0800028900*§ 8 97883896 FLM 4.65E−07 LOC101903458 D 72327
ARS-BFGL-NGS-110774 23 29305663 FLM 5.91E−07 LOC516273 D 4789
LDL BovineHD0100031530 1 111405782 FLM 1.22E−06 LEKR1 U 38747
Hapmap51041-BTA-72970# 5 22943453 FLM 6.79E−07 EEA1 D 11000
BovineHD0500034561 5 118742365 FLM 8.42E−07 LOC104972610 D 70630
BovineHD0700003251 7 12515656 FLM 3.96E−07 LOC107132604 U 41618
BovineHD0700027357 7 93754227 FLM 3.08E−07 LOC104968990 D 31838
BovineHD0700027362 7 93771183 FLM 4.78E−07 LOC104968990 D 48794
BovineHD0800016421 8 54526025 FLM 9.14E−08 PSAT1 Within
ARS-BFGL-NGS-24437 8 54528592 FLM 9.14E−08 PSAT1 Within
BTB-01066770 8 97834727 FLM 1.87E−08 LOC101903458 D 23158
BovineHD0800028900*§ 8 97883896 FLM 3.65E−10 LOC101903458 D 72327
BovineHD0800029109 8 98540784 FLM 5.57E−08 LOC104969466 D 86018
BovineHD0800029198 8 98839161 FLM 1.54E−07 LOC101903599 D 3281
Hapmap38716-BTA-100681 8 98861495 FLM 3.86E−07 KLF4 D 13170
ARS-BFGL-NGS-115765 9 99936460 FLM 1.06E−07 LOC100336821 Within
BTB-00442438* 10 89826995 FLM 1.94E−08 SPTLC2 Within
ARS-BFGL-NGS-17218 10 89905548 FLM 1.23E−07 ALKBH1 Within
BTB-00442692 10 89923736 FLM 4.81E−07 SNW1 Within
BovineHD1000025642 10 89947904 FLM 1.20E−07 SNW1 Within
BovineHD1300011342 13 39397076 FLM 5.32E−07 SLC24A3 Within
BovineHD2200004015 22 13825372 FLM 4.72E−07 LOC104975498 Within
BovineHD2200004029 22 13889811 FLM 3.78E−07 CTNNB1 Within
UA-IFASA-9518 27 15447004 FLM 4.84E−08 MTNR1A Within
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1AST, aspartate aminotransferase; ALP, alkaline phosphatase; TCHO, total cholesterol; CHE, cholinesterase; γ-GT, gamma-glutamyltransferase; TBIL, total bilirubin; DBIL, direct bilirubin; ALT, alanine transaminase; LDHL, lactate dehydrogenase-L; HDL, high-density lipoprotein; LDL, low-density lipoprotein. 2FLM, fixed linear model. 3The distance from the SNP locus to the gene (unit: bp); U indicates that the SNP site is located in the downstream of the gene; D indicates that the SNP site is located in the downstream of the gene; Within indicates that the SNP locus is located within the gene. *Genome-wide significant SNPs that identified to have suggestive effects on serum biochemical traits through mixed-effect linear model (MLM) in Chinese Holstein cattle. §Genome-wide significant SNPs that were identified to be associated with multiple serum biochemical traits through a fixed linear model (FLM) in Chinese Holstein cattle. #Genome-wide significant SNPs identified in the study was also previously identified to be associated with serum biochemical traits.

TABLE 2.

SNPs identified to have genome-wide suggestive effects on serum biochemical traits in Holstein cattle using a mixed-effect linear model.

Trait1 SNP-name Chr Position Model2 P-value Nearest gene Distance3
NEFA BovineHD2300011114 23 38421269 MLM 9.63E−06 – –
AST Hapmap24000-BTA-150203 11 71811673 MLM 2.01E−05 BRE Within
TCHO ARS-BFGL-NGS-65263 8 1.07E + 08 MLM 1.16E−05 PAPPA Within
CHE BovineHD0100005950* 1 20036999 MLM 1.85E−05 – –
BovineHD0200000997 2 3780881 MLM 6.33E−06 – –
ARS-BFGL-NGS-115158* 29 21828399 MLM 1.30E−05 – –
DBIL BovineHD1500021175* 15 73378270 MLM 1.73E−05 – –
CR ARS-BFGL-NGS-1888 23 20839913 MLM 1.74E−05 OPN5 D 5595
CK chr17 71438606 17 71438606 MLM 1.39E−05 LOC104974701 Within
BHB ARS-BFGL-NGS-113393 6 14179168 MLM 1.31E−05 ZGRF1 Within
SUN BovineHD2900002496 29 8779426 MLM 1.21E−05 PRSS23 U 9262
LDL BovineHD0400034698 4 1.18E + 08 MLM 1.39E−05 – –
BovineHD0800028900* 8 97883896 MLM 6.29E−06 LOC101903458 D 72327
BTB-00442438* 10 89826995 MLM 2.21E−05 SPTLC2 Within
VLDL ARS-BFGL-NGS-114594 5 22599252 MLM 1.76E−05 LOC107132468 U 31230
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1NEFA, non-esterified fatty acid; AST, aspartate aminotransferase; TCHO, total cholesterol; CHE, cholinesterase; DBIL, direct bilirubin; CR, serum creatinine; CK, creatine kinase; BHB, β-hydroxybutyric acid; SUN, serum urea nitrogen; LDL, low-density lipoprotein; VLDL, very low-density lipoprotein. 2MLM, mixed-effect linear model. 3The distance from the SNP locus to the gene (unit: bp); U indicates that the SNP site is located in the downstream of the gene; D indicates that the SNP site is located in the downstream of the gene; Within indicates that the SNP locus is located within the gene. *SNPs identified to have genome-wide significant effects on serum biochemical traits in Chinese Holstein cattle using FLM (fixed linear model).

The SNPs identified through the MLM displayed lower overlapping than those identified through the FLM. However, the set of significant SNPs from the MLM in the study was almost a subset of SNPs from the FLM. The SNPs identified through the MLM were more conservative because the MLM took into account the additive genetic effects of each animal, and the false positive rate was expected to be lower than with the FLM. In the GWAS, the FLM with the population structure fitted as covariates may not control the type I error well, while the MLM can lead to false negatives, thus missing some potentially important discoveries (Liu et al., 2016; Supplementary Figure S3). The FLM and MLM are the most popular models in the field of GWAS (Yu et al., 2006; Purcell et al., 2007; Kang et al., 2008, 2010). On the other hand, the low overlapping genome-wide significant SNPs identified from the FLM and MLM also suggest low heritability (h2) of biochemical serum traits, which could be genetically affected by minor genes.

Interestingly, both statistical models pinpointed two SNPs (BovineHD0800028900 and BovineHD1500021175) that displayed genome-wide significant (1.22E−06) association effects on serum traits in the Holstein population. The SNP BovineHD0800028900, located at the downstream of LOC101903458 gene on chromosome 8, was identified to be significantly associated with serum high- and low-density lipoprotein (HDL and LDL). The SNP of BovineHD1500021175 on chromosome 15 was found to have significant association effects on serum bilirubin (TBIL and DBIL). Further analyses are needed to understand the mechanism for the association effects of these SNPs on serum biochemical traits (Du et al., 2013; Hu et al., 2015).

Additionally, several candidate genes or DNA regions that we found to be significantly associated with serum biochemical traits in Holstein cattle coincided with reported association effects on other traits in the literature. For example, six SNPs at the DNA region from 113.6 to 113.7 cM of chromosome 5, closely associated with TCF20 gene, were identified to have a significant effect on the serum ALP level (Table 1). The same DNA region was reported to have a QTL associated with blood triglyceride (TAG) levels (Wu et al., 2014). As another example, Hapmap51041-BTA-72970, located at the downstream region of EEA1 (early endosome antigen 1), was identified to be significantly associated with serum low-density lipoprotein (LDL) level in both Holstein and Jersey cattle in the study. The same region was found to be a QTL, having an effect on abomasum displacement in German Holstein cattle (Mömke et al., 2013). MNTR1A (melatonin receptor 1A) was previously found associated with intramuscular fat and subcutaneous fat (Yang et al., 2015) in Qinchuan beef cattle, and it was also found to be a candidate gene of serum LDL in our study.

In summary, GWAS was conducted using two statistical models on 23 serum biochemical traits in a Chinese Holstein cattle population. Eighty-one genome-wide significant (1.22E−06) SNPs were identified to have association effects on 11 serum biochemical traits through FLM. Among these SNPs, five SNPs were also identified by the MLM to have genome-wide suggestive effects on CHE, DBIL, and LDL. There were two SNPs, BovineHD0800028900 and BovineHD1500021175, that were found to be associated with multiple serum lipoprotein levels and serum bilirubin traits, respectively. The role of these identified SNPs associated with serum biochemical traits remains to be further investigated and validated in future studies. Understand their roles may increase our understanding of the underlying molecular biology of perinatal metabolic disorder, such as fatty liver disease, in dairy cows.

Data Availability Statement

The dataset generated in this study has been deposited into the Animal QTLdb (https://www.animalgenome.org/cgi-bin/QTLdb/BT/pubtails?PUBMED_ID=ISU0115).

Ethics Statement

All experiments were carried out according to the Regulations for the Administration of Affairs Concerning Experimental Animals published by the Ministry of Science and Technology, China (2004) and approved by the Animal Care and Use Committee in Shandong Agricultural University, Shandong, China.

Author Contributions

KS, QZ, and ZW conceived and designed the experiments. QH, FN, CH, ZX, SW, and RL performed the experiments. KS, CN, and SY analyzed the data. ZW, CH, SW, FN, and RL contributed the reagents, materials, and analysis tools. KS, SY, and CN wrote the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the farm owners who generously allowed us to sample blood from their cattle. We are also thankful to Ms. Zijuan Qin and Ms. Mei Zhao in the Animal Husbandry Laboratory platform of Shandong Agricultural University for their kind laboratory support on the serum biochemical tests.

Footnotes

Funding. This work was financially supported by the Key Project of Agricultural Fine Breeding of Shandong Province (2016LZGC030 and 2019LZGC011), the National Natural Science Foundation of China (31402054), the Natural Science Foundation of Shandong (ZR2013CM013), the Funds of Shandong “Double Tops” Program (SYL2017YSTD08), the Modern Agricultural Industry Technology System (CARS-36), and the Tai Mountain Scholar Innovation Team.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2020.00163/full#supplementary-material

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References

  1. Du H. T., Wang C. Y., Wang X. P., Ma M. W., Li F. C. (2013). The effects of dietary α-linolenic acid on growth performance, meat quality, fatty acid composition, and liver relative enzyme mRNA expression of growing meat rabbits. J. Anim. Feed Sci. 22 122–129. 10.22358/jafs/66002/2013 [DOI] [Google Scholar]
  2. Hu Z. Y., Yin Z. Y., Lin X. Y., Yan Z. G., Wang Z. H. (2015). Effects of feeding fatty acid calcium and the interaction of forage quality on production performance and biochemical indexes in early lactation cow. J. Anim. Physiol. Anim. Nutr. 99 899–904. 10.1111/jpn.12302 [DOI] [PubMed] [Google Scholar]
  3. Jung E. J., Park H. B., Lee J. B., Yoo C. K., Kim B. M., Kim H. I., et al. (2013). Genome-wide association analysis identifies quantitative trait loci for growth in a Landrace purebred population. Anim. Genet. 45 442–444. 10.1111/age.12117 [DOI] [PubMed] [Google Scholar]
  4. Kang H. M., Sui J. H., Service S. K., Zaitlen N. A., Kong S. Y., Freimer N. B., et al. (2010). Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42 348–354. 10.1038/ng.548 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Kang H. M., Zaitlen N. A., Wade C. M., Kirby A., Heckerman D., Daly M. J., et al. (2008). Efficient control of population structure in model organism association mapping. Genetics 178 1709–1723. 10.1534/genetics.107.080101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Kerr K. F., Avery C. L., Lin H. J., Raffield L. M., Zhang Q. S., Browning B. L., et al. (2017). Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts. Heart Rhythm. 14 1675–1684. 10.1016/j.hrthm.2017.06.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Liu X., Huang M., Fan B., Buckler E. S., Zhang Z. (2016). Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 12:e1005767. 10.1371/journal.pgen.1005767 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Mapholi N. O., Maiwashe A., Matika O., Riggio V., Bishop S. C., MacNeil M. D., et al. (2016). Genome-wide association study of tick resistance in South African Nguni cattle. Ticks Tick Borne Dis. 7 487–497. 10.1016/j.ttbdis.2016.02.005 [DOI] [PubMed] [Google Scholar]
  9. Mömke S., Sickinger M., Lichtner P., Doll K., Rehage J., Distl O. (2013). Genome-wide association analysis identifies loci for left-sided displacement of the abomasum in German Holstein cattle. J. Dairy Sci. 96 3959–3964. 10.3168/jds.2012-5679 [DOI] [PubMed] [Google Scholar]
  10. Pant S. D., Schenkel F. S., Verschoor C. P., You Q., Kelton D. F., Moore S. S., et al. (2010). A principal component regression based genome wide analysis approach reveals the presence of a novel QTL on BTA7 for MAP resistance in Holstein cattle. Genomics 95 176–182. 10.1016/j.ygeno.2010.01.001 [DOI] [PubMed] [Google Scholar]
  11. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A., Bender D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 559–575. 10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Sahana G., Guldbrandtsen B., Lund M. S. (2011). Genome-wide association study for calving traits in Danish and Swedish Holstein cattle. J. Dairy Sci. 94 479–486. 10.3168/jds.2010-3381 [DOI] [PubMed] [Google Scholar]
  13. Wang D., Sun Y., Stang P., Berlin J. A., Wilcox M. A., Li Q., et al. (2009). Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling. BMC Proc. 3 (Suppl. 7):S109. 10.1186/1753-6561-3-s7-s109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Wu Y., Fan H., Wang Y., Zhang L., Gao X., Chen Y., et al. (2014). Genome-wide association studies using haplotypes and individual SNPs in Simmental cattle. PLoS One 9:e109330. 10.1371/journal.pone.0109330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Yan Z. G., Wang Z. H., Zhang Q., Yue S., Yin B., Jiang Y., et al. (2019). Identification of whole-genome significant single nucleotide polymorphisms in candidate genes associated with body conformation traits in Chinese Holstein cattle. Anim. Genet. 51 141–146. 10.1111/age.12865 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Yang J., Lee S. H., Goddard M. E., Visscher P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88 76–82. 10.1016/j.ajhg.2010.11.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Yang W., Wang Y., Fu C., Zan L. S. (2015). Association study and expression analysis of MTNR1A as a candidate gene for body measurement and meat quality traits in Qinchuan cattle. Gene 570 199–204. 10.1016/j.gene.2015.06.012 [DOI] [PubMed] [Google Scholar]
  18. Yu J., Pressoir G., Briggs W. H., Vroh Bi I., Yamasaki M., Doebley J. F., et al. (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38 203–208. 10.1038/ng1702 [DOI] [PubMed] [Google Scholar]
  19. Yue S. J., Zhao Y. Q., Gu X. R., Yin B., Jiang Y. L., Wang Z. H., et al. (2017). A genome-wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle. Anim. Genet. 48 677–681. 10.1111/age.12593 [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

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Click here for additional data file. (1.3MB, PPTX)

Data Availability Statement

The dataset generated in this study has been deposited into the Animal QTLdb (https://www.animalgenome.org/cgi-bin/QTLdb/BT/pubtails?PUBMED_ID=ISU0115).

Articles from Frontiers in Genetics are provided here courtesy of Frontiers Media SA

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