SUMMARY: We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. AVAILABILITY AND IMPLEMENTATION: SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A perfect phylogeny is a rooted binary tree that recursively partitions sequences. The nested partitions of a perfect phylogeny provide insight into the pattern of ancestry of genetic sequence data. For example, sequences may cluster together in a partition indicating that they arise from a common ancestral haplotype. RESULTS: We present an R package perfectphyloR to reconstruct the local perfect phylogenies underlying a sample of binary sequences. The package enables users to associate the reconstructed partitions with a user-defined partition. We describe and demonstrate the major functionality of the package. CONCLUSION: The perfectphyloR package should be of use to researchers seeking insight into the ancestral structure of their sequence data. The reconstructed partitions have many applications, including the mapping of trait-influencing variants.

MOTIVATION: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients; techniques for conducting statistical inference are not provided. A new Bayesian method is proposed here to overcome this limitation. RESULTS: We develop a Bayesian hierarchical modeling formulation where the posterior mode corresponds to the estimator proposed by Wang et al. and an approach that allows for full posterior inference including the construction of interval estimates for the regression parameters. We show that the proposed hierarchical model can be expressed as a three-level Gaussian scale mixture and this representation facilitates the use of a Gibbs sampling algorithm for posterior simulation. Simulation studies demonstrate that the interval estimates obtained using our approach achieve adequate coverage probabilities that outperform those obtained from the nonparametric bootstrap. Our proposed methodology is applied to the analysis of neuroimaging and genetic data collected as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), and this analysis of the ADNI cohort demonstrates clearly the value added of incorporating interval estimation beyond only point estimation when relating SNPs to brain imaging endophenotypes. AVAILABILITY AND IMPLEMENTATION: Software and sample data is available as an R package 'bgsmtr' that can be downloaded from The Comprehensive R Archive Network (CRAN). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: : The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run. AVAILABILITY AND IMPLEMENTATION: sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: We propose a stepwise approach to identify recombination breakpoints in a sequence alignment. The approach can be applied to any recombination detection method that uses a permutation test and provides estimates of breakpoints. RESULTS: We illustrate the approach by analyses of a simulated dataset and alignments of real data from HIV-1 and human chromosome 7. The presented simulation results compare the statistical properties of one-step and two-step procedures. More breakpoints are found with a two-step procedure than with a single application of a given method, particularly for higher recombination rates. At higher recombination rates, the additional breakpoints were located at the cost of only a slight increase in the number of falsely declared breakpoints. However, a large proportion of breakpoints still go undetected. AVAILABILITY: A makefile and C source code for phylogenetic profiling and the maximum chi2 method, tested with the gcc compiler on Linux and WindowsXP, are available at http://stat-db.stat.sfu.ca/stepwise/ CONTACT: [email protected].