PRESTO: Rapid calculation of order statistic distributions and multiple-testing adjusted P-values via permutation for one and two-stage genetic association studies

Background  

Large-scale genetic association studies can test hundreds of thousands of genetic markers for association with a trait. Since the genetic markers may be correlated, a Bonferroni correction is typically too stringent a correction for multiple testing. Permutation testing is a standard statistical technique for determining statistical significance when performing multiple correlated tests for genetic association. However, permutation testing for large-scale genetic association studies is computationally demanding and calls for optimized algorithms and software. PRESTO is a new software package for genetic association studies that performs fast computation of multiple-testing adjusted P-values via permutation of the trait.     

Optimal Step Length EM Algorithm (OSLEM) for the estimation of haplotype frequency and its application in lipoprotein lipase genotyping

Background  

Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data.     

Prediction model for knee osteoarthritis based on genetic and clinical information

Introduction  

Osteoarthritis (OA) is the most common bone and joint disease influenced by genetic and environmental factors. Recent association studies have uncovered the genetic factors behind OA, its susceptibility genes, which would enable us to predict disease occurrence based on genotype information. However, most previous studies have evaluated the effects of only a single susceptibility gene, and hence prediction based on such information is not as reliable. Here, we constructed OA-prediction models based on genotype information from a case-control association study and tested their predictability.     

Association of common polymorphisms in known susceptibility genes with rheumatoid arthritis in a Slovak population using osteoarthritis patients as controls

Introduction  

Both genetic and environmental factors contribute to rheumatoid arthritis (RA), a common and complex autoimmune disease. As well as the major susceptibility gene HLA-DRB1, recent genome-wide and candidate-gene studies reported additional evidence for association of single nucleotide polymorphism (SNP) markers in the PTPN22, STAT4, OLIG3/TNFAIP3 and TRAF1/C5 loci with RA. This study was initiated to investigate the association between defined genetic markers and RA in a Slovak population. In contrast to recent studies, we included intensively-characterized osteoarthritis (OA) patients as controls.     

Tools for efficient epistasis detection in genome-wide association study

Background  

Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested.     

GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

Background  

Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.     

Genetic variations and haplotypes in TIM-3 gene and the risk of gastric cancer

Purpose  

T cell immunoglobulin and mucin domain-containing molecule 3 (TIM-3) could weaken the Th1-mediated anti-tumor responses and accelerate the tumor cell proliferation by inhabiting the production of IL-2 or IFN-γ. This study was to assess the association between TIM-3 genetic variations and the development of gastric cancer.     

Evidence for a heritable predisposition to Chronic Fatigue Syndrome

Background  

Chronic Fatigue Syndrome (CFS) came to attention in the 1980s, but initial investigations did not find organic causes. Now decades later, the etiology of CFS has yet to be understood, and the role of genetic predisposition in CFS remains controversial. Recent reports of CFS association with the retrovirus xenotropic murine leukemic virus-related virus (XMRV) or other murine leukemia related retroviruses (MLV) might also suggest underlying genetic implications within the host immune system.     

Plant traits correlated with generation time directly affect inbreeding depression and mating system and indirectly genetic structure

Background  

Understanding the mechanisms that control species genetic structure has always been a major objective in evolutionary studies. The association between genetic structure and species attributes has received special attention. As species attributes are highly taxonomically constrained, phylogenetically controlled methods are necessary to infer causal relationships. In plants, a previous study controlling for phylogenetic signal has demonstrated that Wright's F _ST, a measure of genetic differentiation among populations, is best predicted by the mating system (outcrossing, mixed-mating or selfing) and that plant traits such as perenniality and growth form have only an indirect influence on F _ST via their association with the mating system. The objective of this study is to further outline the determinants of plant genetic structure by distinguishing the effects of mating system on gene flow and on genetic drift. The association of biparental inbreeding and inbreeding depression with population genetic structure, mating system and plant traits are also investigated.     

Evaluating diabetes and hypertension disease causality using mouse phenotypes

Background  

Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used.     

Robust joint analysis allowing for model uncertainty in two-stage genetic association studies

Background  

The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate.     

PADB : Published Association Database

Background  

Although molecular pathway information and the International HapMap Project data can help biomedical researchers to investigate the aetiology of complex diseases more effectively, such information is missing or insufficient in current genetic association databases. In addition, only a few of the environmental risk factors are included as gene-environment interactions, and the risk measures of associations are not indexed in any association databases.     

A SNP-centric database for the investigation of the human genome

Background  

Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for genetic and biomedical research. Although current genomic databases contain information on several million SNPs and are growing at a very fast rate, the true value of a SNP in this context is a function of the quality of the annotations that characterize it. Retrieving and analyzing such data for a large number of SNPs often represents a major bottleneck in the design of large-scale association studies.     

Two common nonsynonymous paraoxonase 1 (<Emphasis Type="Italic">PON1</Emphasis>) gene polymorphisms and brain astrocytoma and meningioma

Background  

Human serum paraoxonase 1 (PON1) plays a major role in the metabolism of several organophosphorus compounds. The enzyme is encoded by the polymorphic gene PON1, located on chromosome 7q21.3. Aiming to identify genetic variations related to the risk of developing brain tumors, we investigated the putative association between common nonsynonymous PON1 polymorphisms and the risk of developing astrocytoma and meningioma.     

Genome-wide association reveals genetic effects on human Aβ <Subscript>42</Subscript> and τ protein levels in cerebrospinal fluids: a case control study

Background  

Alzheimer's disease (AD) is common and highly heritable with many genes and gene variants associated with AD in one or more studies, including APOE ε2/ε3/ε4. However, the genetic backgrounds for normal cognition, mild cognitive impairment (MCI) and AD in terms of changes in cerebrospinal fluid (CSF) levels of Aβ_1-42, T-tau, and P-tau_181P, have not been clearly delineated. We carried out a genome-wide association study (GWAS) in order to better define the genetic backgrounds to these three states in relation to CSF levels.     

CONAN: copy number variation analysis software for genome-wide association studies

Background  

Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs.     

catmap: Case-control And TDT Meta-Analysis Package

Background  

Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. Although many software options are available for meta-analysis of genetic case-control data, no currently available software implements the method described by Kazeem and Farrall (2005), which combines data from independent family-based and case-control studies.     

Genotype determination for polymorphisms in linkage disequilibrium

Background  

Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information.     

Construction of nested genetic core collections to optimize the exploitation of natural diversity in <Emphasis Type="Italic">Vitis vinifera</Emphasis> L. subsp. <Emphasis Type="Italic">sativa</Emphasis>

Background  

The first high quality draft of the grape genome sequence has just been published. This is a critical step in accessing all the genes of this species and increases the chances of exploiting the natural genetic diversity through association genetics. However, our basic knowledge of the extent of allelic variation within the species is still not sufficient. Towards this goal, we constructed nested genetic core collections (G-cores) to capture the simple sequence repeat (SSR) diversity of the grape cultivated compartment (Vitis vinifera L. subsp. sativa) from the world's largest germplasm collection (Domaine de Vassal, INRA Hérault, France), containing 2262 unique genotypes.     

TriMEDB: A database to integrate transcribed markers and facilitate genetic studies of the tribe Triticeae

Background  

The recent rapid accumulation of sequence resources of various crop species ensures an improvement in the genetics approach, including quantitative trait loci (QTL) analysis as well as the holistic population analysis and association mapping of natural variations. Because the tribe Triticeae includes important cereals such as wheat and barley, integration of information on the genetic markers in these crops should effectively accelerate map-based genetic studies on Triticeae species and lead to the discovery of key loci involved in plant productivity, which can contribute to sustainable food production. Therefore, informatics applications and a semantic knowledgebase of genome-wide markers are required for the integration of information on and further development of genetic markers in wheat and barley in order to advance conventional marker-assisted genetic analyses and population genomics of Triticeae species.