In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).

Genetic associations between mitochondrial DNA (mtDNA) and economic traits have been widely reported for pigs, which indicate the importance of mtDNA. However, studies on mtDNA heteroplasmy in pigs are rare. Next generation sequencing (NGS) methodologies have emerged as a promising genomic approach for detection of mitochondrial heteroplasmy. Due to the short reads, flexible bioinformatic analyses and the contamination of nuclear mitochondrial sequences (NUMTs), NGS was expected to increase false-positive detection of heteroplasmy. In this study, Sanger sequencing was performed as a gold standard to detect heteroplasmy with a detection sensitivity of 5% in pigs and then one whole-genome sequencing method (WGS) and two mtDNA enrichment sequencing methods (Capture and LongPCR) were carried out. The aim of this study was to determine whether mitochondrial heteroplasmy identification from NGS data was affected by NUMTs. We find that WGS generated more false intra-individual polymorphisms and less mapping specificity than the two enrichment sequencing methods, suggesting NUMTs indeed led to false-positive mitochondrial heteroplasmies from NGS data. In addition, to accurately detect mitochondrial diversity, three commonly used tools-SAMtools, VarScan and GATK-with different parameter values were compared. VarScan achieved the best specificity and sensitivity when considering the base alignment quality re-computation and the minimum variant frequency of 0.25. It also suggested bioinformatic workflow interfere in the identification of mtDNA SNPs. In conclusion, intra-individual polymorphism in pig mitochondria from NGS data was confused with NUMTs, and mtDNA-specific enrichment is essential before high-throughput sequencing in the detection of mitochondrial genome sequences.

SUMMARY: We have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows the examination of all pairwise interactions in a remarkably fast manner of linear with population size. Simulation studies are performed to investigate the properties of REMMAX. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model on computational efficiency. It took less than 40 h for the pairwise analysis of 5000 individuals genotyped with roughly 350 000 SNPs with five threads on Intel Xeon E5 2.6 GHz CPU. AVAILABILITY AND IMPLEMENTATION: Source codes are freely available at https://github.com/chaoning/GMAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: Epistasis provides a feasible way for probing potential genetic mechanism of complex traits. However, time-consuming computation challenges successful detection of interaction in practice, especially when linear mixed model (LMM) is used to control type I error in the presence of population structure and cryptic relatedness. Results: A rapid epistatic mixed-model association analysis (REMMA) method was developed to overcome computational limitation. This method first estimates individuals' epistatic effects by an extended genomic best linear unbiased prediction (EG-BLUP) model with additive and epistatic kinship matrix, then pairwise interaction effects are obtained by linear retransformations of individuals' epistatic effects. Simulation studies showed that REMMA could control type I error and increase statistical power in detecting epistatic QTNs in comparison with existing LMM-based FaST-LMM. We applied REMMA to two real datasets, a mouse dataset and the Wellcome Trust Case Control Consortium (WTCCC) data. Application to the mouse data further confirmed the performance of REMMA in controlling type I error. For the WTCCC data, we found most epistatic QTNs for type 1 diabetes (T1D) located in a major histocompatibility complex (MHC) region, from which a large interacting network with 12 hub genes (interacting with ten or more genes) was established. Availability and implementation: Our REMMA method can be freely accessed at https://github.com/chaoning/REMMA. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.