Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r _b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ( $$\hat r_b = 0.70$$r ^ b = 0.70 for cis-eQTLs and $$\hat r_ b = 0.78$$r ^ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.

De-domestication is a unique evolutionary process by which domesticated crops are converted into ‘wild predecessor like’ forms. Weedy rice (Oryza sativa f. spontanea) is an excellent model to dissect the molecular processes underlying de-domestication. Here, we analyse the genomes of 155 weedy and 76 locally cultivated rice accessions from four representative regions in China that were sequenced to an average 18.2 × coverage. Phylogenetic and demographic analyses indicate that Chinese weedy rice was de-domesticated independently from cultivated rice and experienced a strong genetic bottleneck. Although evolving from multiple origins, critical genes underlying convergent evolution of different weedy types can be found. Allele frequency analyses suggest that standing variations and new mutations contribute differently to japonica and indica weedy rice. We identify a Mb-scale genomic region present in weedy rice but not cultivated rice genomes that shows evidence of balancing selection, thereby suggesting that there might be more complexity inherent to the process of de-domestication.

Precise prediction for genetic architecture of complex traits is impeded by the limited understanding on genetic effects of complex traits, especially on gene-by-gene (GxG) and gene-by-environment (GxE) interaction. In the past decades, an explosion of high throughput technologies enables omics studies at multiple levels (such as genomics, transcriptomics, proteomics and metabolomics). The analyses of large omics data, especially two-loci interaction analysis, are very time intensive. Integrating the diverse omics data and environmental effects in the analyses also remain challenges. We proposed mixed linear model approaches using GPU (Graphic Processing Unit) computation to simultaneously dissect various genetic effects. Analyses can be performed for estimating genetic main effects, GxG epistasis effects and GxE environment interaction effects on large-scale omics data for complex traits and for estimating heritability of specific genetic effects. Both mouse data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be unbiasedly estimated with high statistical power by using the proposed approaches.

Human peripheral blood exhibits molecular and cellular heterogeneity across populations, yet the underlying mechanisms remain unclear. We present the Chinese Immune Multi-Omics Atlas (CIMA), characterizing molecular variations linked to sex, age, and genetic variants through multi-omics analysis of more than 10 million circulating immune cells from 428 Chinese adults. CIMA established an enhancer-driven gene regulatory network comprising 237 robust regulons; identified 9600 eGenes and 52,361 caPeaks at cell type resolution; and revealed pleiotropic associations among immune-related disease risk loci, cis-expression quantitative trait loci (QTLs), and chromatin accessibility QTLs. Furthermore, the cell language model CIMA-CLM predicted chromatin accessibility and evaluated the effects of noncoding variants from chromatin sequences and gene expression. CIMA provides a comprehensive reference for immune-related disease research.