FAPI: Fast and accurate P-value Imputation for genome-wide association study.

Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi.

SNPTracker: A Swift Tool for Comprehensive Tracking and Unifying dbSNP rs IDs and Genomic Coordinates of Massive Sequence Variants.

The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.

Comparative genomic analysis reveals evolutionary characteristics and patterns of microRNA clusters in vertebrates.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that can play important regulatory roles in many important biological processes. Although clustering patterns of miRNA clusters have been uncovered in animals, the origin and evolution of miRNA clusters in vertebrates are still poorly understood. Here, we performed comparative genomic analyses to construct 51 sets of orthologous miRNA clusters (SOMCs) across seven test vertebrate species, a collection of miRNA clusters from two or more species that are likely to have evolved from a common ancestral miRNA cluster, and used these to systematically examine the evolutionary characteristics and patterns of miRNA clusters in vertebrates. We found that miRNA clusters are continuously generated, and most of them tend to be conserved and maintained in vertebrate genomes, although some adaptive gains and losses of miRNA cluster have occurred during evolution. Furthermore, miRNA clusters appeared relatively early in the evolutionary history might suffer from more complicated adaptive gain-and-loss than those young miRNA clusters. Detailed analysis showed that genomic duplication events of ancestral miRNAs or miRNA clusters are likely to be major driving force and apparently contribute to origin and evolution of miRNA clusters. Comparison of conserved with lineage-specific miRNA clusters revealed that the contribution of duplication events for the formation of miRNA cluster appears to be more important for conserved miRNA clusters than lineage-specific. Our study provides novel insights for further exploring the origins and evolution of miRNA clusters in vertebrates at a genome scale.

Systematic analysis of genomic organization and heterogeneities of miRNA cluster in vertebrates.

The clustering propensity of microRNA genes is a common biological phenomenon in various animal and plant species. To gain novel insight into genomic organization and potential functional heterogeneities of miRNA clusters in vertebrates from a genome scale, we used large scale data and presented a comprehensive analysis to examine various features of genomic organization of miRNA clusters across seven vertebrates by a combination of comparative genomics and bioinformatics approaches. The results of pair-wise distance analysis of same-strand consecutive miRNAs suggested that the fractions of the miRNA gene pairs are higher at relatively short pair-wise distances than those of protein-coding genes and other non-coding RNA genes. Especially relatively small number of miRNAs is more clustered at very short pair-wise distances than expected at random. We further observed significant difference between real miRNA clusters and randomly organized clusters for different aspects, including higher overlap of target genes, fewer seed types and significant enrichment in diseases. However, the extent of these features of clustered miRNAs has a different tendency and largely depends on inter-miRNA distances because of diverse clustering propensity of miRNAs in vertebrates, suggesting that this cooperated function or cooperative effects between miRNAs in clusters perhaps be affected by inter-miRNA distances.