Copy number variants in lipid metabolism genes are associated with gallstones disease in men

Identification of Intestinal Microbial Community in Gallstone Patients with Metagenomic Next-Generation Sequencing

Gallstone disease (GD) is one of the most common gastrointestinal diseases worldwide. Nowadays, intestinal microbiota are thought to play important roles in the formation of gallstones. In our study, human fecal samples were extracted for metagenomic next-generation sequencing (mNGS) on the Illumina HiSeq platform, followed by bioinformatics analyses. Our results showed that there was a particular intestinal micro-ecosystem in GD patients. In contrast to healthy people, the sequences of Bacteroidetes, Bacteroides and Thetaiotaomicron were obviously more abundant in GD patients at phylum, genus and species levels, respectively. On the other hand, the glycan metabolism and drug resistance, especially for the β-lactams, were the most profound functions of gut microbes in GD patients compared to those in normal subjects. Furthermore, a correlation analysis drew out that there existed a significant relationship between the serum levels of biochemical indicators and abundances of intestinal microbes in GD patients. Our results illuminate both the composition and functions of intestinal microbiota in GD patients. All in all, our study can broaden the insight into the potential mechanism of how gut microbes affect the progression of gallstones to some extent, which may provide potential targets for the prevention, diagnosis or treatment of GD.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.