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Genetic linkage analysis software tools | Genome-wide association study

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches.

Source text:
(Ott et al., 2015) Genetic linkage analysis in the age of whole-genome sequencing. Nat Rev Genet.

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Constructs accurate linkage maps with ultra-dense genome-wide single nucleotide polymorphism (SNP) data. Lep-MAP utilizes simultaneously data on multiple outbred families and can increase linkage map accuracy by taking into account achiasmatic meiosis, a special feature of Lepidoptera and some other taxa with no recombination in one sex. It outperforms other methods on real and simulated data. The software is very light-weight in computation burden and highly automated, allowing fast and objective linkage map construction.
PRIMUS / Pedigree Reconstruction and Identification of a Maximum Unrelated Set
A pedigree reconstruction algorithm that uses estimates of genome-wide identity by descent to reconstruct pedigrees consistent with observed genetic data. However, when genetic data for individuals within a pedigree are missing, often multiple pedigrees can be reconstructed that fit the data. We report a major expansion of PRIMUS that uses mitochondrial (mtDNA) and non-recombining Y chromosome (NRY) haplotypes to eliminate many pedigree structures that are inconsistent with the genetic data. PRIMUS is a permanent solution to identifying the maximum number of unrelated samples for a genetic analysis.
Superlink-Online SNP
Provides geneticists a suite of genetic analysis utilities and is able to perform analyses that are infeasible elsewhere. The system provides tools for both exact and approximate analysis with a reliable accuracy measure. The system source code is freely available, and an online version is also available, enabling computations using tens of thousands of CPUs. In the online version, each user has a private password-protected account and unauthorized access is prevented to retain data privacy. Users can download their data and delete it from the system at any time. Users with higher privacy concerns can download the system source code and install it on their own clusters.
SOLAR / Sequential Oligogenic Linkage Analysis Routines
An extensive, flexible software package for genetic variance components analysis, including linkage analysis, quantitative genetic analysis, SNP association analysis (QTN, QTLD, and MGA), and covariate screening. Operations are included for calculation of marker-specific or multipoint identity-by-descent (IBD) matrices in pedigrees of arbitrary size and complexity, and for linkage analysis of multiple quantitative traits and/or discrete traits which may involve multiple loci (oligogenic analysis), dominance effects, household effects, and interactions.
Allows the very rapid extraction of complete multipoint inheritance information from pedigrees of moderate size. This information is then used in exact computation of multipoint LOD scores and in a powerful method of non-parametric linkage analysis. Quick calculations involving dozens of markers, even in pedigrees with inbreeding and marriage loops, is possible with GENEHUNTER. In addition, the multipoint inheritance information allows the reconstruction of maximum-likelihood haplotypes for all individuals in the pedigree and information content mapping which measures the fraction of the total inheritance information extracted from the marker data.
Facilitates the evaluation and comparison of subject selection choices in sequencing studies in pedigrees. GIGI-Pick uses a "coverage" as one metric to naturally relate pedigree-based genotype imputation to subject selection. This metric enables the use of inferred inheritance vectors (IVs) to optimize imputation of alleles in candidate regions when such information is available. This approach can incorporate information about IVs to guide subject selection for sequencing. If a candidate region is not available, a variant of this metric may be used to optimize selection genome-wide.
Concerns the genetic studies. MapDisto is a program for mapping genetic markers in experimental segregating populations like backcross, F2, doubled haploids, RIL (single-seed descent), and highly recombinant lines. This tool can be used in many features: handling of very large genotyping datasets like the ones generated by genotyping-by-sequencing (GBS); (ii) direct importation and conversion of Variant Call Format (VCF) files; (iii) detection of linkage, i.e. construction of linkage groups in case of segregation distortion; (iv) data imputation on VCF files using a new approach, called LB-Impute. It can propose also recombination fraction estimates in case of segregation distortion.
Performs linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It also parses the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses, that makes the calculation of genome-wide scans more efficient.
Intends to investigate collective functional impacts of differentially expressed genes (DEGs) taking into account their network synergy. GSLA determines the statistical significance of the functional associations between two gene sets by leaning on a pair of tests. The application can perform from a precomputed set from the gene ontology or a personalized set of genes owned by users and allows them to set user-needed density and p-value. This application is part of the PAIR database.
Implements a collapsed haplotype pattern (CHP) method to generate markers from sequence data for linkage analysis. The core concept is that instead of treating each variant a separate marker, we create regional markers for variants in specified genetic regions (e.g. genes) based on haplotype patterns within families, and perform linkage analysis on markers thus generated. SEQLinkage takes sequence data in VCF format and perform two-point linkage analysis. It reports both LOD and HLOD scores for linkage analysis of multiple families.
Links phenotype to omics data sets using well-established as well new techniques. PhenoLink imputes missing values and preprocesses input data (i) to decrease inherent noise in the data and (ii) to counterbalance pitfalls of the Random Forest algorithm, on which feature (e.g., gene) selection is based. Preprocessed data is used in feature selection to identify relations to phenotypes. Visualization of links to phenotypes offered in PhenoLink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data.
TGS-TB / Total Genotyping Solution for Mycobacterium Tuberculosis
Facilitates multiple genotyping formats. TGS-TB is a web-based application that uses next-generation sequencing (NGS) for the analysis of phylogenies with (i) core genomic single nucleotide variations (SNVs), (ii) linkage network analysis of outbreak strains, (iii) spoligotyping, (iv) the analysis of IS6110 insertion sites and (v) variable number tandem repeat (VNTR). It also includes the prediction of Mycobacterium tuberculosis complex (MTBC) lineages/sub-lineages and potential antimicrobial resistance (AMR) based on the KvarQ script.
Detects Mendelian consistent genotyping errors of dense markers in pedigree data. GIGI-Check detects genotyping errors by using inheritance vectors (IVs), which are inferred by using sparse framework genotypes available on a subset of relatives in the pedigree. Thus, this error detection approach consists of two steps. The first step is to infer IVs at the positions of framework markers using gl_auto, a MCMC-based program from the MORGAN package. These markers are assumed to be free of genotyping errors. The second step is to detect errors in dense genotypes by GIGI-Check using the IVs and pedigree structure file from MORGAN.
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