13,730 research outputs found
Discrete Algorithms for Analysis of Genotype Data
Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. The optimization formulations for searching disease-associated risk/resistant factors and predicting disease susceptibility for given case-control study have been introduced. Several discrete methods for disease association search exploiting greedy strategy and topological properties of case-control studies have been developed. New disease susceptibility prediction methods based on the developed search methods have been validated on datasets from case-control studies for several common diseases. Our experiments compare favorably the proposed algorithms with the existing association search and susceptibility prediction methods
Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA
In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this pa- per, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection
Population Structure and Cryptic Relatedness in Genetic Association Studies
We review the problem of confounding in genetic association studies, which
arises principally because of population structure and cryptic relatedness.
Many treatments of the problem consider only a simple ``island'' model of
population structure. We take a broader approach, which views population
structure and cryptic relatedness as different aspects of a single confounder:
the unobserved pedigree defining the (often distant) relationships among the
study subjects. Kinship is therefore a central concept, and we review methods
of defining and estimating kinship coefficients, both pedigree-based and
marker-based. In this unified framework we review solutions to the problem of
population structure, including family-based study designs, genomic control,
structured association, regression control, principal components adjustment and
linear mixed models. The last solution makes the most explicit use of the
kinships among the study subjects, and has an established role in the analysis
of animal and plant breeding studies. Recent computational developments mean
that analyses of human genetic association data are beginning to benefit from
its powerful tests for association, which protect against population structure
and cryptic kinship, as well as intermediate levels of confounding by the
pedigree.Comment: Published in at http://dx.doi.org/10.1214/09-STS307 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Neural networks for genetic epidemiology: past, present, and future
During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes
Bayesian semiparametric analysis for two-phase studies of gene-environment interaction
The two-phase sampling design is a cost-efficient way of collecting expensive
covariate information on a judiciously selected subsample. It is natural to
apply such a strategy for collecting genetic data in a subsample enriched for
exposure to environmental factors for gene-environment interaction (G x E)
analysis. In this paper, we consider two-phase studies of G x E interaction
where phase I data are available on exposure, covariates and disease status.
Stratified sampling is done to prioritize individuals for genotyping at phase
II conditional on disease and exposure. We consider a Bayesian analysis based
on the joint retrospective likelihood of phases I and II data. We address
several important statistical issues: (i) we consider a model with multiple
genes, environmental factors and their pairwise interactions. We employ a
Bayesian variable selection algorithm to reduce the dimensionality of this
potentially high-dimensional model; (ii) we use the assumption of gene-gene and
gene-environment independence to trade off between bias and efficiency for
estimating the interaction parameters through use of hierarchical priors
reflecting this assumption; (iii) we posit a flexible model for the joint
distribution of the phase I categorical variables using the nonparametric Bayes
construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009)
1042-1051].Comment: Published in at http://dx.doi.org/10.1214/12-AOAS599 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
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