The Parkinson's Disease DNA Variant Browser.

Background: Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations.

The endocytic membrane trafficking pathway plays a major role in the risk of Parkinson's disease.

Background: PD is a complex polygenic disorder. In recent years, several genes from the endocytic membrane-trafficking pathway have been suggested to contribute to disease etiology. However, a systematic analysis of pathway-specific genetic risk factors is yet to be performed.

Using exome sequencing to reveal mutations in TREM2 presenting as a frontotemporal dementia-like syndrome without bone involvement.

Objective: To identify new genes and risk factors associated with frontotemporal dementia (FTD). Several genes and loci have been associated with different forms of FTD, but a large number of families with dementia do not harbor mutations in these genes.

Design: Whole-exome sequencing and whole-genome genotyping were performed in all patients.

Exome sequencing reveals an unexpected genetic cause of disease: NOTCH3 mutation in a Turkish family with Alzheimer's disease.

Alzheimer's disease (AD) is a genetically complex disorder for which the definite diagnosis is only accomplished postmortem. Mutations in 3 genes (APP, PSEN1, and PSEN2) are known to cause AD, but a large number of familial cases do not harbor mutations in these genes and several unidentified genes that contain disease-causing mutations are thought to exist. We performed whole exome sequencing in a Turkish patient clinically diagnosed with Alzheimer's disease from a consanguineous family with a complex history of neurological and immunological disorders and identified a mutation in NOTCH3 (p.

A simple and efficient algorithm for genome-wide homozygosity analysis in disease.

Here we propose a simple statistical algorithm for rapidly scoring loci associated with disease or traits due to recessive mutations or deletions using genome-wide single nucleotide polymorphism genotyping case-control data in unrelated individuals. This algorithm identifies loci by defining homozygous segments of the genome present at significantly different frequencies between cases and controls. We found that false positive loci could be effectively removed from the output of this procedure by applying different physical size thresholds for the homozygous segments.