The use of a genetic relationship matrix biases the best linear unbiased prediction.

The best linear unbiased prediction (BLUP), derived from the linear mixed model (LMM), has been popularly used to estimate animal and plant breeding values (BVs) for a few decades. Conventional BLUP has a constraint that BVs are estimated from the assumed covariance among unknown BVs, namely conventional BLUP assumes that its covariance matrix is a , in which is a coefficient that leads to the minimum mean square error of the LMM, and is a genetic relationship matrix. The uncertainty regarding the use of in conventional BLUP was recognized by past studies, but it has not been sufficiently investigated.

Classifying accessions into and using logistic regression model with phenotypic data.

In , and are pivotal subpopulations, and other subpopulations such as and are considered to be derived from or . In this regard, accessions are frequently viewed from the perspective. This study introduces a computational method for classification by applying phenotypic variables to the logistic regression model (LRM).

GWASpro: a high-performance genome-wide association analysis server.

Summary: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model.

How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function.

This article introduces a new method for genome-wide association study (GWAS), (HH-CCDF). Existing GWAS methods, e.g.

The protective role of macrophage migration inhibitory factor in acute kidney injury after cardiac surgery.

Acute kidney injury (AKI) represents the most frequent complication after cardiac surgery. Macrophage migration inhibitory factor (MIF) is a stress-regulating cytokine that was shown to protect the heart from myocardial ischemia-reperfusion injury, but its role in the pathogenesis of AKI remains unknown. In an observational study, serum and urinary MIF was quantified in 60 patients scheduled for elective conventional cardiac surgery with the use of cardiopulmonary bypass.

Hierarchical Association Coefficient Algorithm: New Method for Genome-Wide Association Study.

Hierarchical association coefficient algorithm calculates the degree of association between observations and categories into a value named (HA-coefficient) between 0 for the lower limit and 1 for the upper limit. The HA-coefficient algorithm can be operated with stratified ascending categories based on the average of observations in each category. The upper limit refers to a condition where observations are increasingly ordered into the stratified ascending categories, whereas the lower limit refers to a condition where observations are decreasingly ordered into the stratified ascending categories.

Numericware i: Identical by State Matrix Calculator.

We introduce software, Numericware i, to compute identical by state (IBS) matrix based on genotypic data. Calculating an IBS matrix with a large dataset requires large computer memory and takes lengthy processing time. Numericware i addresses these challenges with 2 algorithmic methods: multithreading and forward chopping.

Numericware N: Numerator Relationship Matrix Calculator.

We present the generalized numerator relationship matrix (GNRM) algorithm and Numericware N as a software tool for calculating the numerator relationship matrix (NRM). The GNRM algorithm aims to build the NRM based on plant pedigrees. Customary plant pedigrees have a sparse format representing multiple ancestors and offspring.