scholarly journals METU-SNP: An Integrated Software System for SNPComplex Disease Association Analysis

2011 ◽  
Vol 8 (2) ◽  
pp. 204-221 ◽  
Author(s):  
Gürkan Üstünkar ◽  
Yeşim Aydın Son
Keyword(s):  
Association Studies ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Case Control Studies ◽  
Single Nucleotide ◽  
High Dimensional Case ◽  
Genome Wide ◽  
Desktop Application ◽  
Integrated Software

Summary Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual’s genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries

AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
Mathieu Emily
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Biological Interpretation ◽  
Limited Power ◽  
Control Association

AbstractAmong the large of number of statistical methods that have been proposed to identify gene-gene interactions in case-control genome-wide association studies (GWAS), gene-based methods have recently grown in popularity as they confer advantage in both statistical power and biological interpretation. All of the gene-based methods jointly model the distribution of single nucleotide polymorphisms (SNPs) sets prior to the statistical test, leading to a limited power to detect sums of SNP-SNP signals. In this paper, we instead propose a gene-based method that first performs SNP-SNP interaction tests before aggregating the obtained

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals Impact of Pre and Post Variant Filtration Strategies on Imputation

2020 ◽  
Author(s):  
Celine Charon ◽  
Rodrigue Allodji ◽  
Vincent Meyer ◽  
Jean-François Deleuze
Keyword(s):  
Quality Control ◽  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Direct Effects ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Conservative Post

Abstract Quality control methods for genome-wide association studies and fine mapping are commonly used for imputation, however, they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1,031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1,089 NCBI recorded individuals for additional validation.Without variant pre-filtration based on quality control (QC), we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) <0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). As a result, to maintain confidence and enough SNVs, we propose here a 2-step post-filtration approach to increase the number of very rare and rare variants compared to conservative post-filtration methods.

scholarly journals Genome-Wide Association Studies for the Concentration of Albumin in Colostrum and Serum in Chinese Holstein

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2211
Author(s):  
Shan Lin ◽  
Zihui Wan ◽  
Junnan Zhang ◽  
Lingna Xu ◽  
Bo Han ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Albumin Concentration ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Chinese Holstein ◽  
Genome Wide ◽  
Chinese Holstein Cows ◽  
Newborn Calves

Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips (containing 140,668 single nucleotide polymorphisms; SNPs). After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs (false discovery rate (FDR) at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.

scholarly journals Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

2019 ◽  
Vol 48 (D1) ◽  
pp. D659-D667 ◽  
Author(s):  
Wenqian Yang ◽  
Yanbo Yang ◽  
Cecheng Zhao ◽  
Kun Yang ◽  
Dongyang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
High Quality ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Whole Genome Resequencing ◽  
Missing Genotypes

Abstract Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.

scholarly journals Regulatory Variants and Disease: The E-Cadherin −160C/A SNP as an Example

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Gongcheng Li ◽  
Tiejun Pan ◽  
Dan Guo ◽  
Long-Cheng Li
Keyword(s):  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Protein Coding ◽  
Regulate Gene Expression ◽  
Regulatory Variants ◽  
Genome Wide ◽  
Regulatory Snps ◽  
E Cadherin

Single nucleotide polymorphisms (SNPs) occurring in noncoding sequences have largely been ignored in genome-wide association studies (GWAS). Yet, amounting evidence suggests that many noncoding SNPs especially those that are in the vicinity of protein coding genes play important roles in shaping chromatin structure and regulate gene expression and, as such, are implicated in a wide variety of diseases. One of such regulatory SNPs (rSNPs) is the E-cadherin (CDH1) promoter −160C/A SNP (rs16260) which is known to affect E-cadherin promoter transcription by displacing transcription factor binding and has been extensively scrutinized for its association with several diseases especially malignancies. Findings from studying this SNP highlight important clinical relevance of rSNPs and justify their inclusion in future GWAS to identify novel disease causing SNPs.

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