title: Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model.
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.
  
  
  RESULTS
  We 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 α-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.
authors.affiliation: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. sheena@iis.sinica.edu.tw.|Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan. sheena@iis.sinica.edu.tw.|Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. sheena@iis.sinica.edu.tw.
authors.forename: Ying-Chao
authors.initials: YC
authors.lastname: Lin
authors.affiliation: Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan. ivysunnyelmo@mail.cmu.edu.tw.
authors.forename: Ai-Ru
authors.initials: AR
authors.lastname: Hsieh
authors.affiliation: Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. ching.lin.hsiao@gmail.com.
authors.forename: Ching-Lin
authors.initials: CL
authors.lastname: Hsiao
authors.affiliation: Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. sawaltz@ibms.sinica.edu.tw.
authors.forename: Shang-Jung
authors.initials: SJ
authors.lastname: Wu
authors.affiliation: Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. mekumi@ibms.sinica.edu.tw.
authors.forename: Hui-Min
authors.initials: HM
authors.lastname: Wang
authors.affiliation: Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua, Taiwan. maiblian@cc.ncue.edu.tw.
authors.forename: Ie-Bin
authors.initials: IeB
authors.lastname: Lian
authors.affiliation: Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan. csjfann@ibms.sinica.edu.tw.|Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. csjfann@ibms.sinica.edu.tw.|Institute of Public Health, National Yang-Ming University, Taipei, Taiwan. csjfann@ibms.sinica.edu.tw.
authors.forename: Cathy S J
authors.initials: CS
authors.lastname: Fann
mesh_terms: D016022:Case-Control Studies; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D010300:Parkinson Disease / Q000235:genetics*