@article {326, title = {SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Jul 01}, pages = {1745-51}, abstract = {

MOTIVATION: Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS: It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY: SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.html.

}, keywords = {Adenoma, Case-Control Studies, Colorectal Neoplasms, Genetic Association Studies, Genetic Variation, Genotype, Humans, Phenotype, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts263}, author = {Liu, Dajiang J and Leal, Suzanne M} } @article {316, title = {A unified method for detecting secondary trait associations with rare variants: application to sequence data.}, journal = {PLoS Genet}, volume = {8}, year = {2012}, month = {2012}, pages = {e1003075}, abstract = {

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

}, keywords = {Blood Pressure, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Italy, Lipoproteins, LDL, Models, Genetic, Phenotype, Quantitative Trait Loci, Software}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003075}, author = {Liu, Dajiang J and Leal, Suzanne M} }