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Shogo Tsuruta

Researcher at University of Georgia

Publications -  159
Citations -  6001

Shogo Tsuruta is an academic researcher from University of Georgia. The author has contributed to research in topics: Population & Heritability. The author has an hindex of 37, co-authored 140 publications receiving 4937 citations. Previous affiliations of Shogo Tsuruta include University of Nebraska–Lincoln.

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Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

TL;DR: A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure.

BLUPF90 and related programs (BGF90)

TL;DR: BGF90 is a collection of software in Fortran 90 useful for breeding & genetics applications that consists of library modules and application programs that support a wide range of models, including those with multiple-correlated effects, multiple animal models and dominance.
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Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation.

TL;DR: This study investigated efficient computing options to create relationship matrices based on genomic markers and pedigree information as well as their inverses to implement a unified approach to genomic evaluations.
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Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

TL;DR: Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP, and using the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals that uses recursions on a small subset of reference animals.
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Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications.

TL;DR: The preconditionsed conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is essential.