R-Gada: a fast and flexible pipeline for copy number analysis in association studies

Background  

Genome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.     

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.     

High resolution discovery and confirmation of copy number variants in 90 Yoruba Nigerians

Background  

Copy number variants (CNVs) account for a large proportion of genetic variation in the genome. The initial discoveries of long (> 100 kb) CNVs in normal healthy individuals were made on BAC arrays and low resolution oligonucleotide arrays. Subsequent studies that used higher resolution microarrays and SNP genotyping arrays detected the presence of large numbers of CNVs that are < 100 kb, with median lengths of approximately 10 kb. More recently, whole genome sequencing of individuals has revealed an abundance of shorter CNVs with lengths < 1 kb.     

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

Background  

Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task.     

Partitioning of copy-number genotypes in pedigrees

Background  

Copy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic community's attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in Birdsuite and PLINK for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrix's Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.     

Function2Gene: A gene selection tool to increase the power of genetic association studies by utilizing public databases and expert knowledge

Background  

Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power.     

solQTL: a tool for QTL analysis,visualization and linking to genomes at SGN database

Background  

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.     

GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures

Background  

We introduce GASH, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity that can be applied to any structural alignment method. Multiple alignments are optimized by conjugate gradient maximization of the NER score within the genetic algorithm framework. Initial alignments are generated by the program Local ASH, and can be supplemented by alignments from any other program.     

Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model

Background  

Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variation in human populations. Array-based comparative genomic hybridization (array-CGH) and newly developed read-depth approach through ultrahigh throughput genomic sequencing both provide rapid, robust, and comprehensive methods to identify CNVs on a whole-genome scale.     

Accounting for uncertainty when assessing association between copy number and disease: a latent class model

Background  

Copy number variations (CNVs) may play an important role in disease risk by altering dosage of genes and other regulatory elements, which may have functional and, ultimately, phenotypic consequences. Therefore, determining whether a CNV is associated or not with a given disease might be relevant in understanding the genesis and progression of human diseases. Current stage technology give CNV probe signal from which copy number status is inferred. Incorporating uncertainty of CNV calling in the statistical analysis is therefore a highly important aspect. In this paper, we present a framework for assessing association between CNVs and disease in case-control studies where uncertainty is taken into account. We also indicate how to use the model to analyze continuous traits and adjust for confounding covariates.     

(Sub)clinical cardiovascular disease is associated with increased bone loss and fracture risk; a systematic review of the association between cardiovascular disease and osteoporosis

Introduction  

Both cardiovascular disease and osteoporosis are important causes of morbidity and mortality in the elderly. The co-occurrence of cardiovascular disease and osteoporosis prompted us to review the evidence of an association between cardiovascular (CV) disease and osteoporosis and potential shared common pathophysiological mechanisms.     

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.     

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background  

The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.     

Bridging the phenotypic gap: Real-time assessment of mitochondrial function and metabolism of the nematode <Emphasis Type="Italic">Caenorhabditis elegans</Emphasis>

Background  

The ATP levels of an organism are an important physiological parameter that is affected by genetic make up, ageing, stress and disease.     

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.     

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.     

A linkage study of candidate loci in familial Parkinson's Disease

Background  

Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Most cases are sporadic, however familial cases do exist. We examined 12 families with familial Parkinson's disease ascertained at the Movement Disorder clinic at the Oregon Health Sciences University for genetic linkage to a number of candidate loci. These loci have been implicated in familial Parkinson's disease or in syndromes with a clinical presentation that overlaps with parkinsonism, as well as potentially in the pathogenesis of the disease.     

Respiratory symptoms and disease characteristics as predictors of pulmonary function abnormalities in patients with rheumatoid arthritis: an observational cohort study

Introduction  

Lung involvement is a common extra-articular manifestation of rheumatoid arthritis (RA) that confers significant morbidity and mortality. The objective of the present study is to assess which respiratory symptoms and patient and disease characteristics are most highly associated with pulmonary function test (PFT) abnormalities in an RA patient cohort without clinical cardiovascular disease.     

Familial frontotemporal dementia with neuronal intranuclear inclusions is not a polyglutamine expansion disease

Background  

Many cases of frontotemporal dementia (FTD) are familial, often with an autosomal dominant pattern of inheritance. Some are due to a mutation in the tau- encoding gene, on chromosome 17, and show an accumulation of abnormal tau in brain tissue (FTDP-17T). Most of the remaining familial cases do not exhibit tau pathology, but display neuropathology similar to patients with dementia and motor neuron disease, characterized by the presence of ubiquitin-immunoreactive (ub-ir), dystrophic neurites and neuronal cytoplasmic inclusions in the neocortex and hippocampus (FTLD-U). Recently, we described a subset of patients with familial FTD with autopsy-proven FTLD-U pathology and with the additional finding of ub-ir neuronal intranuclear inclusions (NII). NII are a characteristic feature of several other neurodegenerative conditions for which the genetic basis is abnormal expansion of a polyglutamine-encoding trinucleotide repeat region. The genetic basis of familial FTLD-U is currently not known, however the presence of NII suggests that a subset of cases may represent a polyglutamine expansion disease.