Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows

et al. 2020

Preprint

Background: Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help the livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle. Results: Based on the significant-association threshold (p < 5 × 10−8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n=2), PME per kg fat (n=14), and valeric acid (n=17). Besides, 69 genes were identified for valeric acid (n=18), PME per kg milk (n=4) and PME per kg fat (n=47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10-2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10-10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs. Conclusions: Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to food preference and feed intake. Identified SNPs in this study were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of the reference population required in genome-wide association studies and genomic selection.

Section: Discussionmentioning

confidence: 99%

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

et al. 2020

Preprint

“…Since there was a positive genetic correlation (ranging from 0.18 to 0.84) between RFI and PME in Holstein-Friesian cows [7], olfactory receptors genes may affect methane mission per animal. In addition, ve candidate genes including CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25 suggested by Pszczola et al [30], involved in a number of metabolic processes that might be related to methane emission. PKD1 gene was associated with development of the digestive tract [30].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, ve candidate genes including CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25 suggested by Pszczola et al [30], involved in a number of metabolic processes that might be related to methane emission. PKD1 gene was associated with development of the digestive tract [30]. Based on the results of gene networks analyses, there were some contributions among candidate genes by co-expression, pathway, physical interactions and shared protein domains for valeric acid and methane emission traits.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

et al. 2020

Preprint

Background: Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help the livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle.Results: Based on the significant-association threshold (p < 5 × 10−8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n=2), PME per kg fat (n=14), and valeric acid (n=17). Besides, 69 genes were identified for valeric acid (n=18), PME per kg milk (n=4) and PME per kg fat (n=47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10-2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10-10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs.Conclusions:Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to food preference and feed intake. Identified SNPs in this study were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of the reference population required in genome-wide association studies and genomic selection.

“…Holstein-Friesian cows [7], olfactory receptors genes may affect methane mission per animal. In addition, five candidate genes including CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25 suggested by Pszczola et al [30], involved in a number of metabolic processes that might be related to methane emission. PKD1 gene was associated with development of the digestive tract [30].…”

Section: Post-gwas Bioinformatics Analysismentioning

confidence: 99%

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

et al. 2020

Preprint

Background: Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help the livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle. Results: Based on the significant-association threshold ( p < 5 × 10 −8 ), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n=2), PME per kg fat (n=14), and valeric acid (n=17). Besides, 69 genes were identified for valeric acid (n=18), PME per kg milk (n=4) and PME per kg fat (n=47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10 -2 ) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10 -10 ) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs. Conclusions: Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to food preference and feed intake. Identified SNPs in this study were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of the reference population required in genome-wide association studies and genomic selection.