ArticleGenetics, selection, evolution : GSE2018
Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model.Biology · 2026Article
- Longitudinal Genome-Wide Association Study for Female Fertility Traits in German Holstein Cattle.Animal genetics · 2026Article
- Genome-wide identifications of regulatory variants influencing milk fat percentage in Chinese Holstein cow.BMC genomics · 2025Article
- Identifying New Loci and Genes Associated with Feed Efficiency in Broilers.International journal of molecular sciences · 2025Article
- Identification of regulatory loci and candidate genes related to body weight traits in broilers based on different models.BMC genomics · 2025Article
- Genome-Wide Association Study of Insertions and Deletions Identified Novel Loci Associated with Milk Production Traits in Dairy Cattle.Animals : an open access journal from MDPI · 2024Article
- Elucidation of the genetic determination of body weight and size in Chinese local chicken breeds by large-scale genomic analyses.BMC genomics · 2024Article
- Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle.JDS communications · 2023Article
- Genome-Wide Association Study for Lactation Performance in the Early and Peak Stages of Lactation in Holstein Dairy Cows.Animals : an open access journal from MDPI · 2022Article
- Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort.Genomics & informatics · 2022Article
- The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis.Animals : an open access journal from MDPI · 2022Article
- Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle.Frontiers in genetics · 2022Article
- Genome-wide association analysis of milk production, somatic cell score, and body conformation traits in Holstein cows.Frontiers in veterinary science · 2022Article
- Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle.Animals : an open access journal from MDPI · 2021Article
- Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle.Animals : an open access journal from MDPI · 2021Article
- Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats.Animals : an open access journal from MDPI · 2020Article
- Genome-wide association for milk production and lactation curve parameters in Holstein dairy cows.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2020Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
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7 authors.
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Abstract
backgroundPseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.
resultsWe propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.
conclusionsOur rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.
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