@article {250, title = {Power analysis and sample size estimation for sequence-based association studies.}, journal = {Bioinformatics}, volume = {30}, year = {2014}, month = {2014 Aug 15}, pages = {2377-8}, abstract = {

MOTIVATION: Statistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner.

SUMMARY: We developed SEQPower, a software package to perform statistical power analysis for sequence-based association data under a variety of genetic variant and disease phenotype models. It aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It also provides biostatisticians with a platform to fairly compare RV association methods and to validate and assess novel association tests.

AVAILABILITY AND IMPLEMENTATION: The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.

}, keywords = {Alleles, Data Interpretation, Statistical, Disease, Genetic Association Studies, Genetic Variation, Humans, Phenotype, Sample Size, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu296}, author = {Wang, Gao T and Li, Biao and Santos-Cortez, Regie P Lyn and Peng, Bo and Leal, Suzanne M} } @article {249, title = {Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 May 01}, pages = {770-83}, abstract = {

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT{\textquoteright}s data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

}, keywords = {Genetic Association Studies, Genetic Variation, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Quality Control, Software}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.04.004}, author = {Wang, Gao T and Peng, Bo and Leal, Suzanne M} }