Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data.
ABSTRACT Several genes underlying rare monogenic forms of Parkinson's disease have been identified over the past decade. Despite evidence for a role for genetics in sporadic Parkinson's disease, few common genetic variants have been unequivocally linked to this disorder. We sought to identify any common genetic variability exerting a large effect in risk for Parkinson's disease in a population cohort and to produce publicly available genome-wide genotype data that can be openly mined by interested researchers and readily augmented by genotyping of additional repository subjects.
We did genome-wide, single-nucleotide-polymorphism (SNP) genotyping of publicly available samples from a cohort of Parkinson's disease patients (n=267) and neurologically normal controls (n=270). More than 408,000 unique SNPs were used from the Illumina Infinium I and HumanHap300 assays.
We have produced around 220 million genotypes in 537 participants. This raw genotype data has been and as such is the first publicly accessible high-density SNP data outside of the International HapMap Project. We also provide here the results of genotype and allele association tests.
We generated publicly available genotype data for Parkinson's disease patients and controls so that these data can be mined and augmented by other researchers to identify common genetic variability that results in minor and moderate risk for disease.
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ABSTRACT: Background Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson¿s disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.ResultsWe propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson¿s disease case¿control dataset as a model to demonstrate the application of our method.Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of ¿-synuclein gene (SNCA) with Parkinson¿s disease (p¿=¿0.005). We further identify association with hyaluronan synthase 2 (HAS2, p¿=¿0.028) and kringle containing transmembrane protein 1 (KREMEN1, p¿=¿0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson¿s disease.Conclusions Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson¿s disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.Journal of Biomedical Science 08/2014; 21(1):88. · 2.74 Impact Factor
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ABSTRACT: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.BMC Medical Informatics and Decision Making 08/2014; 14(1):80. · 1.50 Impact Factor
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ABSTRACT: We demonstrate and analyze an aggregation method for sparse logistic regression in high-dimensional settings. This approach linearly combines the estimators from various logistic models with different sparsity patterns and can balance the predictive ability and model interpretability. We also study the Kullback-Leibler risk of the aggregation estimator and show that it is comparable to the risk of the best estimator based on a single logistic regression, chosen by an oracle. Numerical performance of the estimator is also investigated using both simulated and real data.10/2014;