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Package ‘STARSEQ’ February 15, 2013 Type Package Title Secondary Trait Association analysis for Rare variants via SEQuence data Version 1.02 Date 2012-05-15 Author Dajiang Liu Maintainer Dajiang Liu <[email protected]> Description STARSEQ is an R-package for detecting associations with rare variants using selected samples in sequence-based association studies. It corrects for the bias in the secondary trait distribution induced by selective sampling on the primary trait. The corrected secondary trait can be analyzed by standard rare variant tests. In the STARSEQ package, several popular rare variant tests were implemented, which include 1.) combined multivariate and collapsing 2.) weighted sum statistics, 3.) kernel based adaptive cluster, 4.) variable threshold test 5.) sequence kernel association test. License GPL-3 LazyLoad yes Depends CompQuadForm,numDeriv,vcf2geno Repository CRAN Date/Publication 2012-08-24 19:22:18 NeedsCompilation yes 1 2 STAR-package R topics documented: STAR-package . . . . . . . . cmc.uniqtl.pop . . . . . . . . datagen.pleiomap . . . . . . . fit.null.2nd.ex.full.pleiomap . . kbac.uniqtl.pop.C . . . . . . . kbac.weight . . . . . . . . . . lse.wss.misshsq . . . . . . . . mat2ped . . . . . . . . . . . . meta.p . . . . . . . . . . . . . minus.L0.2nd.ex.full.pleiomap mySKAT . . . . . . . . . . . recode.uniqtl . . . . . . . . . set.compare . . . . . . . . . . set.intersect . . . . . . . . . . skat.uniqtl.simple.C . . . . . . STAR.test . . . . . . . . . . . std.score.proscore.stat.mat . . std.score.stat.new . . . . . . . vt.uniqtl.pop.C . . . . . . . . wss.lin.simple.C . . . . . . . . wss.uniqtl.pop.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index STAR-package 2 3 4 4 6 6 7 8 8 9 10 10 11 11 12 12 14 14 15 16 16 18 Secondary Trait Association analysis for Rare variants via SEQuence data Description STARSEQ is an R-package for detecting associations with rare variants using selected samples in sequence-based association studies. It corrects for the bias in the secondary trait distribution induced by selective sampling on the primary trait. The corrected secondary trait can be analyzed by standard rare variant tests. In the STARSEQ package, several popular rare variant tests were implemented, which include 1.) combined multivariate and collapsing 2.) weighted sum statistics, 3.) kernel based adaptive cluster, 4.) variable threshold test 5.) sequence kernel association test. Details Package: Type: Version: Date: License: LazyLoad: STAR Package 1.0 2012-05-15 GPL-3 yes cmc.uniqtl.pop 3 Author(s) Dajiang Liu Maintainer: Dajiang Liu <[email protected]> References 1. Liu, D. J. and S. M. Leal (2011). "A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data." Eur J Hum Genet. 2. Liu, D. J. and S. M. Leal A Unified Method for Detecting Secondary Trait Associations with Rare Variants: Application to Sequence Data PLoS Genetics, under review cmc.uniqtl.pop CMC Test for Population-basd Studies of Quantitative Trait Description This function implements the combined multivariate and collasping method. Usage cmc.uniqtl.pop(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = "greater") Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf.vec User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 0 is recommended for CMC test. alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the CMC test Author(s) Dajiang Liu 4 fit.null.2nd.ex.full.pleiomap datagen.pleiomap Simulation Function for the STARSEQ method Description Simulating Data for Evaluating STARSEQ package Usage datagen.pleiomap(pars) Arguments pars A list of simulation parameters Value dat.ped A list of simulated ped file y2.vec Simulated secondary traits sumstat Summary statistics calculated for simulated dataset Author(s) Dajiang Liu fit.null.2nd.ex.full.pleiomap Model Fitting Function for STARSEQ Description This implements the STARSEQ model for correcting the secondary trait distribution for selected samples. This function is not intended for taking user input. Usage fit.null.2nd.ex.full.pleiomap(marker, rv.count.cutoff, covar.mat, y1.vec, y2.vec, yub, ylb, pct.uppe fit.null.2nd.ex.full.pleiomap Arguments marker A Matrix for marker genotypes. rv.count.cutoff An upper bound for collapsing variants. covar.mat Matrix for covariate y1.vec Primary trait. y2.vec Secondary trait. yub Upper trait threshold for selective sampling. ylb Lower trait threshold for selective sampling. pct.upper Upper percentage for selective sampling. pct.lower Lower percentage for selective sampling. Nub Number of samples from upper extremes. Nlb Number of samples from lower extremes. Value beta.10.est Estimates of intercept for the primary trait beta.11.vec.est beta.11.vec.est is the estimates for primary trait effects beta.20.est beta.2y1.est beta.20.est is the estimated intercept for secondary trait beta.2y1.est is the estimated secondary trait effect sigma.1.est sigma.1.est is the estimated primary trait standard deviation sigma.2.est sigma.2.est is the estimated secondary trait standard deviation beta.1z.est beta.1z.est is the estimated covariate primary trait effect beta.2z.est beta.2z.est is the estimated covariate secondary trait effect r.y1 r.y1 is the primary trait residuals r.y2 r.y2 is the secondary trait residuals Author(s) Dajiang Liu 5 6 kbac.weight KBAC Test for Population-based Studies of Quantitative Trait kbac.uniqtl.pop.C Description This function implements the extended kernel-based adaptive cluster method. Usage kbac.uniqtl.pop.C(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = "greater") Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf.vec User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 0 is recommended for CMC test. alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the CMC test Author(s) Dajiang Liu Weighting Function for KBAC kbac.weight Description Weighting function for KBAC, does not take user input Usage kbac.weight(dat) Arguments dat Matrix format for genotype and phenotypes. lse.wss.misshsq 7 Value x recoded multi-site genotypes Author(s) Dajiang Liu lse.wss.misshsq Least Square Estimates for Genetic Average Effect and Locus Heritability in the Weighted Sum Statistics Description Least Square Estimates for Genetic Average Effect and Locus Heritability in the Weighted Sum Statistics Usage lse.wss.misshsq(marker, y, weight) Arguments marker Genotype Matrix y Quantitative trait weight Weight assigned to each variant site; Value beta1.est Estimates of locus genetic effects; hsq.est Estimates of locus genetic variance Author(s) Dajiang Liu 8 meta.p Transform Mat format to PED format mat2ped Description Transform Mat format data to ped format. Internal function; Not designed for user input; Usage mat2ped(dat.mat) Arguments dat.mat Data in mat format Value Ped format output Author(s) Dajiang Liu meta.p P-value Based Meta-Analysis Description Meta-Analysis Combining P-values Usage meta.p(p.value.vec, N.vec, no.perm.vec, alternative = c("two.sided", "greater", "less")) Arguments p.value.vec A vector of p-values from each study; N.vec Sample size from each study no.perm.vec If p-values are permutation based, the number of permutations has to be specified alternative Alternative hypothesis, which can be two.sided, greater or less. The default choice is two.sided. minus.L0.2nd.ex.full.pleiomap 9 Value p.value P value of meta analysis statistic Meta-analysis test statisitcs. The statistic is obtained by combining Z-score statistics, which are transformed from p-values and weighted by the square root of the sample size. Author(s) Dajiang Liu minus.L0.2nd.ex.full.pleiomap Minus Log-likelihoood Function for STAR Description Minus Log-likelihoood Function for STAR Usage minus.L0.2nd.ex.full.pleiomap(pars, marker, covar.mat, y1.vec, y2.vec, yub, ylb, pct.upper, pct.lowe Arguments pars Vector of model paramters marker Genotype matrix covar.mat Matrix of covariates y1.vec Vector of primary traits y2.vec Vector of secondary traits yub Upper extreme primary trait cutoff for selective sampling ylb Lower extreme primary trait cutoff for selective sampling pct.upper Proportion of samples from the upper extreme pct.lower Proportion of samples from the lower extreme Nub Number of samples from the upper extreme of the primary traits Nlb Number of samples from lower extreme of the primary traits Value Returns the value for minus log likelihood, which will be used for maximum likelihood estimates. This function is not intended to take user inputs. Author(s) Dajiang Liu 10 recode.uniqtl Implementation of SKAT Test mySKAT Description This implements the SKAT test according to Wu et al. Usage mySKAT(marker, y, W) Arguments marker y W Marker genotypes Vector of phenotypes Weights calculated based the kernel used. Value p.value p values from SKAT calculated based upon mixture chi-square distribution. Author(s) Dajiang Liu Recode Multi-site Genotypes for KBAC Test recode.uniqtl Description Record Multi-site Genotypes for KBAC Test Usage recode.uniqtl(dat) Arguments dat Input data in matrix format; Value Return recoded genotypes in mat format; Author(s) Dajiang Liu set.compare set.compare 11 Comparing 2 Ordered Set of Values Description Compare if two ordered sets are equivalent; Usage set.compare(set1, set2) Arguments set1 set2 The first set; The second set; Value 1 if the two sets are equal, 0 if not; Author(s) Dajiang Liu set.intersect Calculate Intersection of Two Sets Description Calculate Intersection of Two Sets Usage set.intersect(set1, set2) Arguments set1 set2 A vector of set 1 A vector of set 2 Value An intersection of Two Sets. Author(s) Dajiang Liu 12 STAR.test skat.uniqtl.simple.C SKAT Test for Population-basd Studies of Quantitative Trait Description This function implements the sequence kernel association test. Usage skat.uniqtl.simple.C(dat.ped, par.dat, maf, maf.cutoff, no.perm = 1000, alternative = "two.sided" , out.type="C") Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 1000 is default for SKAT test. Adaptive permutatoin is implemented alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. out.type C for continuous trait Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the SKAT test Author(s) Dajiang Liu STAR.test Secondary Trait Association Analysis with Rare Variants Description Core function for STAR Usage STAR.test(dat.ped, y2.vec, covar.mat, rv.count.cutoff, maf.vec, maf.cutoff, pct.upper, pct.lower, yu STAR.test 13 Arguments dat.ped The list of pedigree files. Components include individual.id, family.id, genotype, phenotype, father.id, mother.id, and sex. y2.vec Vector of secondary trait. covar.mat Covariate matrix. rv.count.cutoff The upper bound for the very rare variants that will be collapsed in the analysis. maf.vec Mandatory minor allele frequencies. maf.cutoff The minor allele frequency threshold which is used to determine the set of variants that are analyzed. pct.upper Upper percentage of selection. pct.lower Lower percentage of selection. yub Upper primary trait threshold. ylb Lower primary trait threshold. no.perm Number of permutations to be performed. Default choice is 1000. Adaptive permutation is implemented. For many tests, the asymptotic p-values can be obtained, however, they may not be accurate. method Supported methods include CMC, WSS (extended by DY Lin and ZZ Tang and by Liu and Leal), KBAC, SKAT, and VT. alternative Three possible alternative hypothesis two.sided, greater and less. The default choice is two.sided. For SKAT, only two sided alternative hypothesis can be tested Value p.value P values for the specified test statistic Statistic values beta.10.est Estimates of intercept for the primary trait beta.11.vec.est beta.11.vec.est is the estimates for primary trait effects beta.20.est beta.2y1.est beta.20.est is the estimated intercept for secondary trait beta.2y1.est is the estimated secondary trait effect sigma.1.est sigma.1.est is the estimated primary trait standard deviation sigma.2.est sigma.2.est is the estimated secondary trait standard deviation beta.1z.est beta.1z.est is the estimated covariate primary trait effect beta.2z.est beta.2z.est is the estimated covariate secondary trait effect r.y1 r.y1 is the primary trait residuals r.y2 r.y2 is the secondary trait residuals Author(s) Dajiang J. Liu & Suzanne M. Leal 14 std.score.stat.new std.score.proscore.stat.mat Single Variant Score Statistics for a Genotype Matrix Description Single Variant Score Statistics for a Genotype Matrix Usage std.score.proscore.stat.mat(x.mat, y, yub, ylb, par.nuis) Arguments x.mat y yub ylb par.nuis Genotype matrix Quantitative or binary trait Upper trait threshold Lower trait threshold Nuissance parameters Value The function returns an array of statistics Author(s) Dajiang Liu std.score.stat.new Standard Score Statistics from Prospective Likelihood Description Prospective Likelihood Score Statistics Usage std.score.stat.new(x, y, yub, ylb, par.nuis) Arguments x y yub ylb par.nuis Predictor variable; Response variable. Upper trait threshold. Lower trait threshold; Other nuissance parameters; vt.uniqtl.pop.C 15 Value Value of statistics from the prospective likelihhod Author(s) Dajiang Liu vt.uniqtl.pop.C VT Test for Population-basd Studies of Quantitative Trait Description This function implements the variable threshold test Usage vt.uniqtl.pop.C(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = c("two.sided"," Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf.vec User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 1000 is default for VT test. Adaptive permutatoin is implemented alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the VT test Author(s) Dajiang Liu 16 wss.uniqtl.pop.C wss.lin.simple.C WSS Test for Population-basd Studies of Quantitative Trait as extended by Lin and Tang Description This function implements the extended weigthed sum statisitc Usage wss.lin.simple.C(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = c("two.sided", Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf.vec User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 1000 is default for WSS test. Adaptive permutatoin is implemented alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the WSS test Author(s) Dajiang Liu wss.uniqtl.pop.C WSS Test for Population-basd Studies of Quantitative Trait Description This function implements the extended weigthed sum statisitc Usage wss.uniqtl.pop.C(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = "greater") wss.uniqtl.pop.C 17 Arguments dat.ped A list of ped files. par.dat A list of parameters for ascertainment. The default in an empty list. maf.vec User specified minor allele frequency vector maf.cutoff Upper minor allele frequency cutoff for rare variant analysis no.perm The number of permutations. Set to 1000 is default for WSS test. Adaptive permutatoin is implemented alternative Alternative hypothesis, default choice is two.sided. Other options include greater or less. Value p.value P-value as determined by the alternative hypothesis tested statistic Statistic value for the WSS test Author(s) Dajiang Liu Index cmc.uniqtl.pop, 3 datagen.pleiomap, 4 fit.null.2nd.ex.full.pleiomap, 4 kbac.uniqtl.pop.C, 6 kbac.weight, 6 lse.wss.misshsq, 7 mat2ped, 8 meta.p, 8 minus.L0.2nd.ex.full.pleiomap, 9 mySKAT, 10 recode.uniqtl, 10 set.compare, 11 set.intersect, 11 skat.uniqtl.simple.C, 12 STAR (STAR-package), 2 STAR-package, 2 STAR.test, 12 std.score.proscore.stat.mat, 14 std.score.stat.new, 14 vt.uniqtl.pop.C, 15 wss.lin.simple.C, 16 wss.uniqtl.pop.C, 16 18