RareIBD is a rare variant association method for extended pedigrees. It supports large pedigrees with arbitrary structure, binary and quantitative traits, missing individuals in top generations, and affected only pedigrees. The software will be available soon (Sul et al., Under Review).
eGene-MVN is an efficient and accurate multivariate normal sampling (MVN) approach to perform the multiple testing correction in eQTL studies. Our approach is more than 100 times faster than the permutation test when sample size is 2,000. (Sul et al., 2015)
Meta-Tissue is software for identifying eQTLs from multiple tissues. It uses meta-analysis to combine results from multiple tissues, and is a powerful approach to detect eQTLs shared across multiple tissues. (Sul et al., 2013)
We developed statistical methods to detect associations of groups of rare variants. Those two methods are RWAS (Rare variant Weighted Aggregate Statistic), and LRT (Likelihood Ratio Test). We also developed methods to detect associations of rare variants under low-coverage sequencing or pooling. (Sul et al., 2011), (Sul et al., 2011), (Navon et al., 2013)
Maintained by other people:
- EMMAX: software to correct for population structure. (Kang et al., 2010)
- NICE: software to correct for expression heterogeneity in expression data. (Joo et al., 2014)
- Sul JH, Raj T, de Jong S, de Bakker PIW, Raychaudhuri S, Ophoff RA, et al. Accurate and fast multiple-testing correction in eQTL studies. Am J Hum Genet. 2015 Jun 4;96(6):857–68.
- Sul JH, Han B, Ye C, Choi T, Eskin E. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS Genet. 2013 Jun;9(6):e1003491.
- Sul JH, Han B, He D, Eskin E. An optimal weighted aggregated association test for identification of rare variants involved in common diseases. Genetics. 2011 May;188(1):181–8.
- Sul JH, Han B, Eskin E. Increasing power of groupwise association test with likelihood ratio test. J Comput Biol. 2011 Nov;18(11):1611–24.
- Navon O, Sul JH, Han B, Conde L, Bracci PM, Riby J, et al. Rare variant association testing under low-coverage sequencing. Genetics. 2013 Jul;194(3):769–79.
- Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-Y, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010 Apr;42(4):348–54.
- Joo JWJ, Sul JH, Han B, Ye C, Eskin E. Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies. Genome Biol. 2014;15(4):r61.