相关论文: Using GWAS Data to Identify Copy Number Variants C…

Conducting genome-wide association studies (GWAS) in copy number variation (CNV) level is a field where few people involves and little statistical progresses have been achieved, traditional methods suffer from many problems such as batch…

Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the signals identified by association analysis may not have specific pathological relevance to diseases so…

Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies.…

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent structural alterations that influence genomic diversity and disease susceptibility. While…

Genomic copy number variation (CNV) is a large source of variation between organisms, and its consequences include phenotypic differences and genetic disorders. CNVs are commonly detected by hybridizing genomic DNA to microarrays of nucleic…

In addition to variation in terms of single nucleotide polymorphisms (SNPs), whole regions ranging from several kilobases up to a megabase in length differ in copy number among individuals. These differences are referred to as Copy Number…

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) that is caused by genetic, epigenetic, and environmental factors. Recent advances in genomic analysis have uncovered numerous candidate genes with common…

After the completion of human genome sequence was anounced, it is evident that interpretation of DNA sequences is an immediate task to work on. For understanding their signals, improvement of present sequence analysis tools and developing…

DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect…

Motivation: Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with…

Recently, due to the genomic sequence analysis in several types of cancer, the genomic data based on {\em copy number profiles} ({\em CNP} for short) are getting more and more popular. A CNP is a vector where each component is a…

Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations.…

A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of…

The study of genomic variation has provided key insights into the functional role of mutations. Predominantly, studies have focused on single nucleotide variants (SNV), which are relatively easy to detect and can be described with rich…

For the vast majority of genome wide association studies (GWAS) published so far, statistical analysis was performed by testing markers individually. In this article we present some elementary statistical considerations which clearly show…

Genome-Wide Association Studies (GWAS) help identify genetic variations in people with diseases such as Parkinson's disease (PD), which are less common in those without the disease. Thus, GWAS data can be used to identify genetic variations…

Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers. This genomic complexity is known to be associated with poorer…

Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing…

High-dimensional phenotypes hold promise for richer findings in association studies, but testing of several phenotype traits aggravates the grand challenge of association studies, that of multiple testing. Several methods have recently been…

We present an alternative method for genome-wide association studies (GWAS) that is more powerful than the regular GWAS method for locus detection. The regular GWAS method suffers from a substantial multiple-testing burden because of the…