ABSTRACT
The hepatic lipase plays a central role in the lipid metabolism, catalyzing the hydrolysis of phospholipids, monoglycerides, diglycerides, and triglycerides, and acyl-CoA. It is also implied in the conversion of very low-density lipoprotein and intermediate density lipoprotein to low density lipoproteins. As a consequence, the gene encoding the hepatic lipase (LIPC) is associated with several diseases derived from the imbalance of lipids that are in general derived from the interaction between life styles and genetic architecture. Therefore, it is interesting to understand more about the characteristics of the microevolutionary processes affecting genes that, like LIPC, have a role in nutrition and lipid metabolism in human populations. We explored the selection signatures on LIPC in 26 populations, detecting three regions under recent positive selection
ABSTRACT
As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits, a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data, sample quality issues, such as gender mismatch, abnormal inbreeding coefficient, cryptic relatedness, and population outliers, can also have fundamental impact on downstream analysis. However, there is a lack of tools specialized in identifying problematic samples from NGS data, often due to the limitation of sample size and variant counts. We developed SeqSQC, a Bioconductor package, to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access, and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor.org/packages/SeqSQC.
Subject(s)
Female , Humans , Breast Neoplasms , Genetics , Cohort Studies , Racial Groups , Genetics , Genome, Human , High-Throughput Nucleotide Sequencing , Methods , Reference Standards , Software , Exome SequencingABSTRACT
Abstract The advent of next-generation sequencing allows simultaneous processing of several genomic regions/individuals, increasing the availability and accuracy of whole-genome data. However, these new approaches may present some errors and bias due to alignment, genotype calling, and imputation methods. Despite these flaws, data obtained by next-generation sequencing can be valuable for population and evolutionary studies of specific genes, such as genes related to how pigmentation evolved among populations, one of the main topics in human evolutionary biology. Melanocortin-1 receptor (MC1R) is one of the most studied genes involved in pigmentation variation. As MC1R has already been suggested to affect melanogenesis and increase risk of developing melanoma, it constitutes one of the best models to understand how natural selection acts on pigmentation. Here we employed a locally developed pipeline to obtain genotype and haplotype data for MC1R from the raw sequencing data provided by the 1000 Genomes FTP site. We also compared such genotype data to Phase 3 VCF to evaluate its quality and discover any polymorphic sites that may have been overlooked. In conclusion, either the VCF file or one of the presently described pipelines could be used to obtain reliable and accurate genotype calling from the 1000 Genomes Phase 3 data.