ArticleAnimals : an open access journal from MDPI2024
Integrating Genomic Selection and a Genome-Wide Association Study to Improve Days Open in Thai Dairy Holstein Cattle: A Comprehensive Genetic Analysis.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Identification of candidate genes for reproductive traits in Chinese Holstein cattle using single-step genome-wide association study.Journal of animal science · 2026Article
- Pleiotropic Genes Affecting Milk Production, Fertility, and Health in Thai-Holstein Crossbred Dairy Cattle: A GWAS Approach.Animals : an open access journal from MDPI · 2025Article
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5 authors.
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Abstract
Days open (DO) is a critical economic and reproductive trait that is commonly employed in genetic selection. Making improvements using conventional genetic techniques is exceedingly challenging. Therefore, new techniques are required to improve the accuracy of genetic selection using genomic data. This study examined the genetic approaches of traditional AIREML and single-step genomic AIREML (ssGAIREML) to assess genetic parameters and the accuracy of estimated breeding values while also investigating SNP regions associated with DO and identifying candidate genes through a genome-wide association study (GWAS). The dataset included 59415 DO records from 36368 Thai-Holstein crossbred cows and 882 genotyped animals. The cows were classified according to their Holstein genetic proportion (breed group, BG) as follows: BG1 (>93.7% Holstein genetics), BG2 (87.5% to 93.6% Holstein genetics), and BG3 (<87.5% Holstein genetics). AIREML was utilized to estimate genetic parameters and variance components. The results of this study reveal that the average DO values for BG1, BG2, and BG3 were 97.64, 97.25, and 96.23 days, respectively. The heritability values were estimated to be 0.02 and 0.03 for the traditional AIREML and ssGAIREML approaches, respectively. Depending on the dataset, the ssGAIREML method produced more accurate estimated breeding values than the traditional AIREML method, ranging from 40.5 to 45.6%. The highest values were found in the top 20% of the dam dataset. For the GWAS, we found 12 potential candidate genes (
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