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Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks.

ABSTRACT: BACKGROUND: Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data. RESULTS: In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease. CONCLUSIONS: Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.

SUBMITTER: Liu Y

PROVIDER: S-EPMC2982692 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks.

Liu Yang Y Ng Michael M

BMC systems biology 20100913

<h4>Background</h4>Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such ...[more]

PMID: 20840732

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Project description:BACKGROUND:Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. RESULTS:This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed most existing the-state-of-the-art random forests. The top 25 SNPs in Parkinson data set were identified by the proposed model including four interesting genes associated with neurological disorders. CONCLUSION:The presented approach has shown to be effective in selecting informative sub-groups of SNPs potentially associated with diseases that traditional statistical approaches might fail. The new RF works well for the data where the number of case-control objects is much smaller than the number of SNPs, which is a typical problem in gene data and GWAS. Experiment results demonstrated the effectiveness of the proposed RF model that outperformed the state-of-the-art RFs, including Breiman's RF, GRRF and wsRF methods.

Project description:There are dozens of recognized indigenous dog breeds in China. However, these breeds have not had extensive studies to describe their population structure, genomic linkage disequilibrium (LD) patterns, and selection signatures. Here, we systematically surveyed the genomes of 157 unrelated dogs that were from 15 diverse Chinese dog breeds. Canine 170K SNP chips were used to compare the genomic structures of Chinese and Western dogs. The genotyping data of 170K SNP chips in Western dogs were downloaded from the LUPA (a European initiative of canine genome project) database. Chinese indigenous dogs had lower LD and shorter accumulative runs of homozygosity (ROH) in the genome. The genetic distances between individuals within each Chinese breed were larger than those within Western breeds. Chinese indigenous and Western dog breeds were clearly differentiated into two separate clades revealed by the PCA and NJ-tree. We found evidence for historical introgression of Western dogs into Chinese Kazakhstan shepherd and Mongolia Xi dogs. We suggested that Greenland sledge dog, Papillon, and European Eurasier have Chinese dog lineages. Selection sweep analysis identified genome-wide selection signatures of each Chinese breed and three breed groups. We highlighted several genes including EPAS1 and DNAH9 that show signatures of natural selection in Qinghai-Tibetan plateau dogs and are likely important for genetic adaptation to high altitude. Comparison of our findings with previous reports suggested RBP7, NMNAT1, SLC2A5, and H6PD that exhibit signatures of natural selection in Chinese mountain hounds as promising candidate genes for the traits of endurance and night vision, and NOL8, KRT9, RORB, and CAMTA1 that show signals of selection in Xi dogs might be candidate genes influencing dog running speed. The results about genomic and population structures, and selection signatures of Chinese dog breeds reinforce the conclusion that Chinese indigenous dogs with great variations of phenotypes are important resources for identifying genes responsible for complex traits.

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Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks.

Publications

Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks.

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