PLINK: Whole genome data analysis toolset [an error occurred while processing this directive] PLINK is a whole genome association analysis toolset, designed to perform large-scale analyses in a computationally efficient manner. By using a binary representation of SNP data, very large datasets can be loaded into memory in their entirety and processed very efficiently. For example, to calculate the chi-square statistics and odds ratios for all SNPs in a 100K dataset containing 350 individuals took ~2 seconds on a standard Linux workstation, making a permutation-based approach to whole genome analysis feasible.

PLINK will generate a number of standard summary statistics that are useful for quality control and can be used as thresholds for subsequent analyses (e.g. missing genotype rate, minor allele frequency, Hardy-Weinberg equilibrium failures and non-Mendelian transmission rates) as well as some more novel ones (estimated inbreeding coefficients for each individual and genome-wide identity-by-state and identity-by-descent estimates for all pairs of individuals). The later can be used to detect sample contaminations, swaps and duplications as well as pedigree errors and unknown familial relationships (e.g. sibling pairs in a case/control population-based sample). PLINK also provides a simple interface for recoding, reordering, flipping DNA-strand and extracting subsets of SNP data.

A simple but powerful approach to population stratification is included in PLINK, that can use whole genome SNP data in a computationally efficient manner. We use complete linkage agglomerative clustering, based on pairwise IBS distance, but with some modifications to the clustering process: restrictions based on a significance test for whether two individuals belong to the same population (i.e. do not merge clusters that contain significantly different individuals) , a phenotype criterion (i.e. all pairs must contain at least one case and one control) and a cluster size restriction (i.e. such that, with a cluster size of 2, for example, the subsequent association test would implicitly match every case with its nearest control, as long as the case and control do not show evidence of belonging to different populations). Any evidence of population substructure (from this or any other analysis) can be incorporated in subsequent association tests via the specification of clusters, as at each permutation step of the tests described below, individuals are only permuted within cluster.

The basic association test is for a disease trait and is based on comparing allele frequencies between cases and controls (asymptotic and empirical p-values are available). Also implemented are the Cochran-Armitage trend test, dominant and recessive models and a two degree of freedom general model. The ability to compare different models by likelihood ratio test and to evaluate the significant of the most significant model by permutation are also incorporated. We also test for difference in missing genotype rates between cases and controls.

Family-based data can be analyzed with the basic TDT, using either asymptotic or empirical significance values. In addition, the basic test is modified to include information on parental phenotypes to give a more powerful combined test. The permutation procedure will flip transmitted/untransmitted status constantly for all SNPs for a given family, thereby preserving the LD and linkage information between markers and siblings.

Quantitative traits can be tested for association also, using either asymptotic (likelihood ratio test and Wald test) or empirical significance values. As above, any clustering scheme can be specified for the permutations: in this way, it is possible to control for subpopulation membership as well as family structure, if related individuals are being analyzed.

For all tests (disease, quantitative and family-based) it is possible to specify 'sets' of SNPs to calculate 'set-based' or 'gene-based' test statistics: these are based on cumulative sums of rank-ordered single SNP statistics, and are evaluated by permutation.

For the disease-trait population-based samples, it is possible to test for epistasis. The epistasis test can either be case-only or case-control. Either all pairwise combinations of SNPs can be tested (although this is most likely not desirable, it is computationally feasible using PLINK -- the 4.5 billion two-locus tests generated from a 100K data set took just over 24 hours to run) or sets can be specified (e.g. to test only the most significant 100 SNPs against all other SNPs, or against themselves, etc). The output consists only pairwise epistatic results above a certain significance value; also, for each SNP, a summary of all the pairwise epistatic tests is given (e.g. maximum test, proportion of tests significant at a certain threshold, etc). A similar methodology allows for testing of gene-environment interaction (for dichotomous environmental variables).

All tests described above are based on single SNP tests. It is also possible to impute haplotypes based on multimarker predictors using the standard E-M algorithm and either perform the test on the posterior distribution of haplotypes given genotypes for each individual (for main disease and quantitative trait population-based association tests only), alternatively to create a new file with the most likely haplotype pair imputed (or set to missing if the most likely phase is below a certain value).

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