title: A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts.
abstract: BACKGROUND
  The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case-control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc.
  
  
  RESULTS
  We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson's disease (PD) case-control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk.
  
  
  CONCLUSIONS
  We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS.
authors: Department of Biostatistics and Computational Biology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, 200433, Shanghai, China.
authors: Minghui
authors: M
authors: Wang
authors: Lin
authors: L
authors: Wang
authors: Ning
authors: N
authors: Jiang
authors: Tianye
authors: T
authors: Jia
authors: Zewei
authors: Z
authors: Luo
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D016208:Databases, Factual; D005346:Fibroblast Growth Factors / Q000235:genetics; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D000071158:Leucine-Rich Repeat Serine-Threonine Protein Kinase-2; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D010300:Parkinson Disease / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D017346:Protein Serine-Threonine Kinases / Q000235:genetics
chemical_list: C416003:FGF20 protein, human; D005346:Fibroblast Growth Factors; C495280:LRRK2 protein, human; D000071158:Leucine-Rich Repeat Serine-Threonine Protein Kinase-2; D017346:Protein Serine-Threonine Kinases
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