Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins

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Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins. / Wu, Xiaoping; Lund, Mogens S; Sahana, Goutam; Guldbrandtsen, Bernt; Sun, Dongxiao; Zhang, Qin; Su, Guosheng.

In: Genetics Selection Evolution, Vol. 47, No. 50, 08.2015, p. 1-14.

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Wu, Xiaoping ; Lund, Mogens S ; Sahana, Goutam ; Guldbrandtsen, Bernt ; Sun, Dongxiao ; Zhang, Qin ; Su, Guosheng. / Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins. In: Genetics Selection Evolution. 2015 ; Vol. 47, No. 50. pp. 1-14.

Bibtex

@article{a2a03f19e7ed4b0390674d8dc7c491c1,
title = "Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins",
abstract = "BACKGROUND: The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).RESULTS: After quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.CONCLUSIONS: The power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.",
author = "Xiaoping Wu and Lund, {Mogens S} and Goutam Sahana and Bernt Guldbrandtsen and Dongxiao Sun and Qin Zhang and Guosheng Su",
year = "2015",
month = aug,
doi = "10.1186/s12711-015-0129-1",
language = "English",
volume = "47",
pages = "1--14",
journal = "Genetics Selection Evolution",
issn = "0999-193X",
publisher = "BioMed Central Ltd.",
number = "50",

}

RIS

TY - JOUR

T1 - Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins

AU - Wu, Xiaoping

AU - Lund, Mogens S

AU - Sahana, Goutam

AU - Guldbrandtsen, Bernt

AU - Sun, Dongxiao

AU - Zhang, Qin

AU - Su, Guosheng

PY - 2015/8

Y1 - 2015/8

N2 - BACKGROUND: The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).RESULTS: After quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.CONCLUSIONS: The power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.

AB - BACKGROUND: The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).RESULTS: After quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.CONCLUSIONS: The power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.

U2 - 10.1186/s12711-015-0129-1

DO - 10.1186/s12711-015-0129-1

M3 - Journal article

C2 - 26087655

VL - 47

SP - 1

EP - 14

JO - Genetics Selection Evolution

JF - Genetics Selection Evolution

SN - 0999-193X

IS - 50

ER -