Alexander Edward Lipka

This study reconstructs ancestral bacterial DEDDy dinucleases to trace the evolution of substrate specificity and oligomerization from a dimeric ancestor with intermediate preferences.

This study evaluates genomic prediction models for maize hybrids using GBLUP-based multi-kernel models to estimate genetic variance components and predict hybrid performance.

This study evaluates MTME genomic prediction models for Miscanthus sacchariflorus, finding they improve prediction for some traits but not all, depending on the cross-validation scenario.

This study evaluates sparse testing designs for genomic prediction in Miscanthus breeding to reduce phenotyping costs while maintaining predictive accuracy.

This study optimizes genomic prediction in Miscanthus sacchariflorus by incorporating weather data to improve model performance across different locations.

The paper evaluates cross-validation schemes for genomic selection in perennial Miscanthus species, showing improved predictive ability by incorporating genotype-by-environment interactions.

This study identifies the polygenic architecture of photoprotection in sorghum through a high-throughput screen of natural genetic variation in non-photochemical quenching traits.

This paper improves simulation accuracy for digital breeding by integrating GWAS-derived genetic architecture into trait simulations, achieving high concordance with empirical data.

This paper improves genomic prediction accuracy by using synthetic traits derived from hyperspectral data, showing higher accuracy than single-trait models for leaf traits in sorghum.

The paper uses gene regulatory networks from maize to inform genomic prediction models for plant architecture traits, finding that markers near highly connected transcription factors improve prediction accuracy in maize and related crops.

The paper uses a crop model to show that breeding sorghum for limited transpiration could increase yield in drought-prone US regions without tradeoffs.

The paper tests genetic variation for limited transpiration in sorghum under field conditions and evaluates canopy temperature as a surrogate trait.

The paper introduces a statistic to filter paralogs in genotyping-by-sequencing data from duplicated genomes, enabling efficient locus filtering before genotype calling.

This study simulates traits controlled by variance quantitative trait nucleotides in Arabidopsis and maize to evaluate the performance of two statistical methods for identifying variance quantitative trait loci.

The paper identifies eleven biosynthetic genes explaining most of the natural variation in carotenoid levels in maize grain through QTL analysis and expression correlation.

simplePHENOTYPES simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in genetic architectures to aid in testing multivariate statistical analyses for GWAS and GS approaches.

The paper uses genomic prediction to identify biological processes influencing free amino acid traits in Arabidopsis seeds, revealing new trait-pathway associations and improving predictive ability for specific amino acids.

HAPPI GWAS is a tool that automates pre-GWAS, GWAS, and post-GWAS analysis, addressing gaps in existing pipelines by including outlier removal, BLUP/BLUE calculation, and haploblock analysis.

This study identifies 120 QTL associated with domestication traits in cotton using an F2 population, revealing subgenome-specific distribution and environmental variability.

polyRAD improves genotype calling in polyploids and diploids by using Bayesian methods with flexible priors, reducing errors from low read depth and improving genotype accuracy.

The paper measures elemental accumulation in maize kernels across environments and identifies genetic loci associated with elemental traits using QTL and GWAS.