NAM - Nested Association Mapping

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

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openblascpp

5.80 score 2 stars 1 dependents 52 scripts 333 downloads

bWGR - Bayesian Whole-Genome Regression

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

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cpp

4.27 score 8 stars 33 scripts 382 downloads

SoyNAM - Soybean Nested Association Mapping Dataset

Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis.

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cpp

3.48 score 2 stars 5 scripts 313 downloads

mas - Multi-Population Association Studies

Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses.

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cpp

3.00 score 2 scripts 233 downloads

pegs - Pseudo-Expectation Gauss-Seidel

A lightweight, dependency-free, and simplified implementation of the Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate ridge regression model for genomic prediction Xavier and Habier (2022) <doi:10.1186/s12711-022-00730-w> and Xavier et al. (2025) <doi:10.1093/genetics/iyae179>, providing heritability estimates, genetic correlations, breeding values, and regression coefficient estimates for prediction. This package provides an alternative to the 'bWGR' package by Xavier et al. (2019) <doi:10.1093/bioinformatics/btz794> by using 'LAPACK' for its algebraic operations.

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openblascpp

1.00 score 191 downloads