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

Wu X, Lund MS, Sahana G, Guldbrandtsen B, Sun D, Zhang Q, Su G - Genet. Sel. Evol. (2015)

Bottom Line: A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. xpwu594419341@gmail.com.

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.

No MeSH data available.


Related in: MedlinePlus

Weighted QTL intensity peaks detected by multiple t-tests for different marker densities. Blue solid circles are the weighted QTL intensities that are significant with the t test. From top to bottom are shown the plots based on MD, HD, and SEQ data, respectively. The six chromosome regions are between 84 and 95 Mb on BTA5, 88 and 96 Mb on BTA6, 57 and 63 Mb on BTA13, 48 and 55 Mb on BTA16, 55 and 58 Mb on BTA19, and 32 and 40 Mb on BTA20; the regions are separated by vertical dotted lines
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Fig5: Weighted QTL intensity peaks detected by multiple t-tests for different marker densities. Blue solid circles are the weighted QTL intensities that are significant with the t test. From top to bottom are shown the plots based on MD, HD, and SEQ data, respectively. The six chromosome regions are between 84 and 95 Mb on BTA5, 88 and 96 Mb on BTA6, 57 and 63 Mb on BTA13, 48 and 55 Mb on BTA16, 55 and 58 Mb on BTA19, and 32 and 40 Mb on BTA20; the regions are separated by vertical dotted lines

Mentions: QTL intensity profiles from the analysis based on the three marker datasets are in Fig. 3. The corresponding weighted QTL intensities are in Fig. 4. The association patterns of the weighted QTL intensities were consistent across the three marker densities. BVSINTMD, BVSINTHD, and BVSINTSEQ detected one, 16, and 33 QTL intensity peaks, respectively (Fig. 5). Among the SNPs that were significant with the BVS models (BF > 10), 36.4, 87.5, and 86.7 % were within the QTL regions identified by BVSINTMD, BVSINTHD, and BVSINTSEQ, respectively. The positions and intervals of the detected QTL intensity peaks by BVSINTMD and BVSINTHD are in Table 2. BVSINTMD detected only one QTL intensity peak on BTA6. Table 3 shows the 51 genes that were located within or adjacent to the QTL intensity peaks detected by BVSINTSEQ. Among these QTL intensity peaks, 27 were located within or overlapped with known genes, while the others were 5 to 165 kb away from the nearest known gene. The average LD (r2) of the 33 QTL intensity regions was equal to 0.68. The average interval length of the QTL intensity peaks became smaller as marker densities increased and was approximately 1.39, 0.22, and 0.10 Mb for BVSINTMD, BVSINTHD, and BVSINTSEQ, respectively.Fig. 3


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

Wu X, Lund MS, Sahana G, Guldbrandtsen B, Sun D, Zhang Q, Su G - Genet. Sel. Evol. (2015)

Weighted QTL intensity peaks detected by multiple t-tests for different marker densities. Blue solid circles are the weighted QTL intensities that are significant with the t test. From top to bottom are shown the plots based on MD, HD, and SEQ data, respectively. The six chromosome regions are between 84 and 95 Mb on BTA5, 88 and 96 Mb on BTA6, 57 and 63 Mb on BTA13, 48 and 55 Mb on BTA16, 55 and 58 Mb on BTA19, and 32 and 40 Mb on BTA20; the regions are separated by vertical dotted lines
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4472403&req=5

Fig5: Weighted QTL intensity peaks detected by multiple t-tests for different marker densities. Blue solid circles are the weighted QTL intensities that are significant with the t test. From top to bottom are shown the plots based on MD, HD, and SEQ data, respectively. The six chromosome regions are between 84 and 95 Mb on BTA5, 88 and 96 Mb on BTA6, 57 and 63 Mb on BTA13, 48 and 55 Mb on BTA16, 55 and 58 Mb on BTA19, and 32 and 40 Mb on BTA20; the regions are separated by vertical dotted lines
Mentions: QTL intensity profiles from the analysis based on the three marker datasets are in Fig. 3. The corresponding weighted QTL intensities are in Fig. 4. The association patterns of the weighted QTL intensities were consistent across the three marker densities. BVSINTMD, BVSINTHD, and BVSINTSEQ detected one, 16, and 33 QTL intensity peaks, respectively (Fig. 5). Among the SNPs that were significant with the BVS models (BF > 10), 36.4, 87.5, and 86.7 % were within the QTL regions identified by BVSINTMD, BVSINTHD, and BVSINTSEQ, respectively. The positions and intervals of the detected QTL intensity peaks by BVSINTMD and BVSINTHD are in Table 2. BVSINTMD detected only one QTL intensity peak on BTA6. Table 3 shows the 51 genes that were located within or adjacent to the QTL intensity peaks detected by BVSINTSEQ. Among these QTL intensity peaks, 27 were located within or overlapped with known genes, while the others were 5 to 165 kb away from the nearest known gene. The average LD (r2) of the 33 QTL intensity regions was equal to 0.68. The average interval length of the QTL intensity peaks became smaller as marker densities increased and was approximately 1.39, 0.22, and 0.10 Mb for BVSINTMD, BVSINTHD, and BVSINTSEQ, respectively.Fig. 3

Bottom Line: A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. xpwu594419341@gmail.com.

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.

No MeSH data available.


Related in: MedlinePlus