Genetic Variation In Human Genome Research Articles

An Integrated Framework for Analysis and Prediction of Impact of Single Nucleotide Polymorphism Associated with Human Diseases.

Single nucleotide polymorphisms are most common type of genetic variation in human genome. Analyzing genetic variants can help us better understand the genetic basis of diseases and develop predictive models which are useful to identify individuals who are at increased risk for certain diseases. Several SNP analysis tools have already been developed. For running these tools, the user needs to collect data from various databases. Secondly, often researchers have to use multiple variant analysis tools for cross validating their results and increase confidence in their findings. Extracting data from multiple databases and running multiple tools at a time, increases complexity and time required for analysis. There are some web-based tools that integrate multiple genetic variant databases and provide variant annotations for a few tools. These approaches have some limitations such as retrieving annotation information, filtering common pathogenic variants. The proposed web-based tool, namely IPSNP: An Integrated Platform for Predicting Impact of SNPs is written in Django which is a python-based framework. It uses RESTful API of MyVariant.info to extract annotation information of variants associated with a given gene, rsID, HGVS format variants specified in a VCF file for 29 tools. The results are in the form of a CSV file of predictions (1) derived from the consensus decision, (2) a file having annotations for the variants associated with the given gene, (3) a file showing variants declared as pathogenic commonly by the selected tools, and (4) a CSV file containing chromosome coordinates based on GRCh37 and GRCh38 genome assemblies, rsIDs and proteomic data, so that users may use tools of their choice and avoiding manual parameter collection for each tool. IPSNP is a valuable resource for researchers and clinicians and it can help to save time and effort in discovering the novel disease-associated variants and the development of personalized treatments.

Recovery of non-reference sequences missing from the human reference genome

BackgroundThe non-reference sequences (NRS) represent structure variations in human genome with potential functional significance. However, besides the known insertions, it is currently unknown whether other types of structure variations with NRS exist.ResultsHere, we compared 31 human de novo assemblies with the current reference genome to identify the NRS and their location. We resolved the precise location of 6113 NRS adding up to 12.8 Mb. Besides 1571 insertions, we detected 3041 alternate alleles, which were defined as having less than 90% (or none) identity with the reference alleles. These alternate alleles overlapped with 1143 protein-coding genes including a putative novel MHC haplotype. Further, we demonstrated that the alternate alleles and their flanking regions had high content of tandem repeats, indicating that their origin was associated with tandem repeats.ConclusionsOur study detected a large number of NRS including many alternate alleles which are previously uncharacterized. We suggested that the origin of alternate alleles was associated with tandem repeats. Our results enriched the spectrum of genetic variations in human genome.

The role of DENND1A and CYP19A1 gene variants in individual susceptibility to obesity in Turkish population-a preliminary study.

Single nucleotide polymorphisms (SNPs), the most common genetic variations in human genome, can manage the predisposition of certain complex diseases or situations such as obesity. Genetic polymorphisms also play an important role as they can impact a population's susceptibility to being overweight or obese and developing related chronic complications such as hypertension, coronary heart disease, diabetes and cancer. The present study comprised of 193 unrelated healthy volunteers (120 females and 73 males) with Turkish origin. Only female adolescents (n = 110) were divided into 2 categories according to their BMI values as overweight (BMI ≥ 25) and normal (18.5 < BMI < 25) according to WHO classification. Genomic DNA was isolated from venous blood samples and genotyping of DENND1A rs10818854 and CYP19A1 rs2414096 variants was performed on Roche Light Cycler 2.0 Real-Time PCR platform. Serum hormone levels were analyzed by Electrochemiluminescent Immunoassay (ECLIA; Roche diagnostics). The genotype distributions were consistent with the Hardy-Weinberg equilibrium for both SNPs in the studied population (p > 0.05). The genotype distribution of DENND1A rs10818854 was determined for the first time in Turkish population and the variant allele frequency was found as 0.095. According to reduced sex hormone-binding globulin levels and increased free androgen index in the present study, obesity was linked with hyperandrogenism in female subjects. Both polymorphisms were investigated as potential genetic susceptibility markers for obesity and neither DENND1A nor CYP19A1 showed any associations.

Genetics of human autoimmunity: From genetic information to functional insights

Genome-wide association studies have identified hundreds of risk variants associated with human autoimmune diseases. Recent evidence suggests that a substantial portion of them affect gene expression in specific cell types. To obtain the functional insights of GWAS findings, comprehensive characterization of genetic variants in human genome is a key task. In parallel with GWAS, many researches in functional genomics have been conducted in the past decade, and our understandings of cell type-specific gene regulatory system have been substantially improved. In this review, we will introduce the main research topics in functional genomics, and explain their utility to understand biological mechanisms of autoimmune diseases.

SRinversion: a tool for detecting short inversions by splitting and re-aligning poorly mapped and unmapped sequencing reads.

Rapid development in sequencing technologies has dramatically improved our ability to detect genetic variants in human genome. However, current methods have variable sensitivities in detecting different types of genetic variants. One type of such genetic variants that is especially hard to detect is inversions. Analysis of public databases showed that few short inversions have been reported so far. Unlike reads that contain small insertions or deletions, which will be considered through gap alignment, reads carrying short inversions often have poor mapping quality or are unmapped, thus are often not further considered. As a result, the majority of short inversions might have been overlooked and require special algorithms for their detection. Here, we introduce SRinversion, a framework to analyze poorly mapped or unmapped reads by splitting and re-aligning them for the purpose of inversion detection. SRinversion is very sensitive to small inversions and can detect those less than 10 bp in size. We applied SRinversion to both simulated data and high-coverage sequencing data from the 1000 Genomes Project and compared the results with those from Pindel, BreakDancer, DELLY, Gustaf and MID. A better performance of SRinversion was achieved for both datasets for the detection of small inversions. SRinversion is implemented in Perl and is publicly available at http://paed.hku.hk/genome/software/SRinversion/index.html CONTACT: yangwl@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

Associations of Polymorphisms in mir-196a2, mir-146a and mir-149 with Colorectal Cancer Risk: A Meta-Analysis.

MicroRNAs (miRNAs) are non-coding RNAs which act as tumor suppressors or oncogenes. And single nucleotide polymorphism (SNP) in miRNA regions is one type of genetic variations in human genome. Various studies have investigated the associations of miRNAs SNP and kinds of cancers. In this article, we searched eligible studies to explore the relationships between mir-196a2 /mir-146a /mir-149 polymorphisms and colorectal cancer (CRC). A literature search of PubMed, Web of Science and ScienceDirect was conducted to identify all relevant studies. Three genetic models with pooled ratio and 95% confidence interval were used to evaluate the associations. We found that mir-196a2 polymorphism was significantly associated with CRC in Asian group (additive model: OR = 1.197, 95%CI 1.084 ~ 1.32, P < 0.001; dominant model: OR = 1.247, 95%CI 1.065 ~ 1.46, P = 0.006; recessive model: OR = 1.298, 95%CI 1.101 ~ 1.531, P = 0.002). And no associations were observed between SNPs of mir-146a, mir-149 and CRC in three genetic models. We also found CRC risk was not associated with mir-146a and mir-149 polymorphisms in population subgroup analysis. The current meta-analysis suggests that mir-196a2 polymorphism is associated with CRC, especially in Asian group. While, no associations have been found between mir-146a /mir-149 polymorphisms and CRC.

Parametric modeling of whole-genome sequencing data for CNV identification

Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.