Henner Simianer

This paper optimizes wheat breeding programs using an evolutionary algorithm, achieving higher genetic gain and diversity while maintaining a fixed budget.

The paper uses stochastic simulations to optimize a maize breeding scheme by balancing genetic gains and diversity through additional phenotyping and external genetic material.

This paper introduces a framework for numerically optimizing breeding programs by maximizing a target function that combines genetic gain and diversity conservation, using stochastic simulations and kernel regression.

Ghat identifies selection on polygenic traits by analyzing allele frequency changes and effect sizes, demonstrating its power on wheat traits and simulated populations.

learnMET is an R package that uses machine learning to predict genomic traits from multi-environment trial data by integrating genomic and environmental factors.

The paper introduces HBimpute, an imputation pipeline that improves calling accuracy, coverage, and read depth from low-read-depth sequencing data, achieving results comparable to high-density arrays.

The paper compares genomic prediction models for maize traits, finding that selecting top SNP interactions improves prediction accuracy compared to additive models.

The paper examines the relationship between genetic diversity in domesticated chicken populations and their genetic distance to wild populations, finding a strong inverse correlation.

The paper investigates how SNP array design affects ascertainment bias by redesigning a chicken SNP array and analyzing allele frequency spectra and heterozygosity estimates.

MoBPSweb simulates and compares breeding programs by integrating a unified language with simulation software, evaluating scenarios based on genomic gains, inbreeding rates, and accuracy of breeding value estimation.

This paper compares bivariate genomic prediction models for plant traits across two growing seasons, finding that models incorporating epistasis improve prediction accuracy.

This paper proposes local convolutional neural networks for genomic prediction, showing improved performance over traditional methods in most cases, especially with medium complexity traits and large datasets.

This paper proposes a general definition of animal breeding programs using a modular graph representation and introduces a web tool and R-package for simulating and analyzing breeding programs.

MoBPS simulates breeding programs and compares their economic and genetic impacts using a modular, efficient framework with customizable modules.

The paper introduces two methods for analysis of variance in high-dimensional data and evaluates their performance on genomic datasets.

The paper evaluates BEAGLE versions for imputation quality in crop and livestock data, finding that parameter tuning significantly improves imputation accuracy.

HaploBlocker identifies subgroup-specific haplotype blocks and constructs libraries to represent genetic variability with fewer parameters than traditional methods.