ArticleAnimals : an open access journal from MDPI2018
PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy.
Article in Animals : an open access journal from MDPI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Meta-assembly of genomic associations to identify cattle fat depot candidate genes and pleiotropic effects.BMC genomics · 2024Pooled it
- Potential Applications of Genome-Wide Association Studies in Establishing Climate Resilience in Livestock: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Large deletions in the DNA primase large subunit PRIM2 are associated with NADP-malate dehydrogenase activity in a porcine FAnimal genetics · 2026Article
- Genome-wide association analysis identifies SNP loci for multi-stage yield-related traits in wheat.Frontiers in plant science · 2026Article
- Genome-wide association study identifies novel variants in olfactory, vitamin A, vitamin B, and cadherin pathways associated with learning and memory.Scientific reports · 2025Article
- Multi-omics analysis reveals genetic architecture and local adaptation of coumarins metabolites in Populus.BMC plant biology · 2024Article
- SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer.Genome medicine · 2024Article
- Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS.Scientific reports · 2023Article
- Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population.Frontiers in nutrition · 2023Article
- Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.PLoS genetics · 2022Article
- A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits.The plant genome · 2022Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle.Frontiers in genetics · 2021Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
- Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene,
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.