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DBlocks

Detects haplotype blocks that simultaneously use information about linkage-disequilibrium (LD) decay between blocks and the diversity of haplotypes within blocks. By use of phased single-nucleotide polymorphism (SNP) data, DBlocks partitions a chromosome into a series of adjacent, nonoverlapping blocks. The partition is made by choosing among a family of Markov models for block structure in a chromosomal region. Using computer simulations, MDBlocks can reliably locate the boundaries between blocks at regions of rapid LD decay.

FREGAT / Family REGional Association Tests

An R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.

FFBSKAT / Fast Family-Based Sequence Kernel Association Test

An efficient tool for region/gene association analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals.

famSKAT / family-based SKAT

An extension of sequence kernel association test (SKAT) that can be applied to data with familial correlation. famSKAT has a different test statistic and null distribution compared to SKAT, but is equivalent to SKAT when there is no familial correlation. We demonstrate that famSKAT is a general and flexible variance component score test approach, which is equivalent to SKAT when the familial variance component is set to 0. It can be applied to quantitative traits with unknown or known heritability. Compared with famBT, famSKAT is advantageous in power when the proportion of causal SNPs in a genomic region is small, and when not all causal SNPs have the same direction of effects. As expected, famBT outperforms famSKAT when the proportion of causal SNPs is greater than or equal to 50% and all these SNPs have positive effects, but the performance of famSKAT in these scenarios is still satisfactory. In real data analysis, when we do not have sufficient a priori information about the proportion of causal SNPs or the directions of effects, famSKAT would be a better choice over famBT.