Fine-mapping causal variants from genome-wide association studies (GWAS) loci is challenging in populations with substantial relatedness, such as livestock, as standard methods often assume unrelatedness, leading to poor fine-mapping accuracy. Here, we introduce a comprehensive Bayesian framework to address this. Our approach features BFMAP-Shotgun Stochastic Search for individual-level data, which uses a linear mixed model (LMM) and shotgun stochastic search with simulated annealing. For summary statistics, we develop FINEMAP-adj and SuSiE-adj, novel strategies that directly use standard FINEMAP and SuSiE for samples of related individuals by employing LMM-derived inputs (particularly a relatedness-adjusted linkage disequilibrium matrix). Furthermore, genomic-feature posterior inclusion probability (PIP), implemented here as gene-level PIP (PIPgene), is proposed to enhance detection power by aggregating variant signals. Extensive simulations based on pig genotypes across diverse heritability levels and population structures (pure-breed and multi-breed) show our methods substantially outperform existing tools (FINEMAP, SuSiE, FINEMAP-inf, SuSiE-inf, and GCTA-COJO) in samples of related individuals, achieving notable improvements in fine-mapping accuracy (e.g. up to several-fold increases in the area under the precision-recall curve). Multi-breed populations greatly enhance fine-mapping accuracy compared to single-breed populations. Additionally, PIPgene markedly improves candidate gene identification. Application to Duroc pig traits demonstrates practical utility, with functional enrichment analysis confirming our methods' superior identification of biologically relevant variants. This work provides robust, validated methods and associated software for accurate fine-mapping in populations with complex relatedness.

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

MOTIVATION: Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. RESULTS: We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. AVAILABILITY AND IMPLEMENTATION: A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. Results: We have developed a user-friendly R/Bioconductor package, named GDCRNATools, for downloading, organizing and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis. Availability and implementation: GDCRNATools is an R/Bioconductor package that is freely available at Bioconductor (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html). Detailed instructions, manual and example code are also available in Github (https://github.com/Jialab-UCR/GDCRNATools).