Genetic Mapping and Diversity in Plants and Animals

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 edQTLs in rice under field conditions and shows they can predict gene expression in different environments and complex genotypes.

This study develops a sorghum pan-genome to explore its functional utility in accelerating genetic gain by identifying genes and SNPs associated with agronomic traits and drought tolerance.

This study identifies genetic factors influencing zinc and cadmium concentrations in sweet corn kernels through genome-wide association analysis.

The paper examines heritable gene expression under salt stress in rice, identifying condition-specific heritable genes and their chromosomal distribution patterns.

The paper reanalyzes old BXD mouse data with new tools to discover novel QTL and candidate genes involved in behavior and drug response.

The paper identifies genetic factors influencing maize rind strength through divergent selection and transcriptome analysis, revealing key genes and pathways related to cell wall composition.

This study identifies marker-trait associations linked to post-drought recovery and persistence traits in smooth bromegrass using association analysis across normal and water stress environments.

MultiGWAS is a tool that performs GWAS for diploid and tetraploid organisms by integrating multiple software, improving reproducibility and model selection.

This study examines how kinship correction affects genetic interaction test statistics in mouse populations, finding no inflation in interaction tests but recommending reduced kinship matrices for better correction.

AFLAP constructs high-density genetic maps and improves genome assemblies using k-mers from whole genome sequencing data without requiring a reference genome.

RIL-StEp identifies epistatic gene interactions in rice seed hull color using SNP data from recombinant inbred lines.

This study characterizes genetic diversity, population structure, and selection signatures in Ethiopian sorghum germplasm using GBS, identifying 40 genomic regions under selection.

The paper applies genome-wide mediation analysis to maize data, identifying 736 mediating genes that explain 12.7% of phenotypic variance, with pleiotropic effects observed in 11% of genes.

This paper integrates Infinium and Axiom SNP array data in apple, identifies compatible SNPs, and highlights challenges in data integration.

The paper develops a genomic resource for West African sorghum by characterizing a diverse panel and identifying QTLs for flowering time and plant height.

The paper uses whole-genome sequencing on 220 alfalfa accessions to identify SNPs associated with fall dormancy height, finding a SNP linked to the DREB1C gene.

K-seq is a cost-effective genotyping method using Klenow amplification and Illumina sequencing, showing comparable genetic results to WGS and GBS with higher SNP detection.

The paper constructs a genetic map and identifies QTL in a tetraploid potato population, finding co-located QTL for multiple traits on linkage group 5.

The paper characterizes traditional Assam rice genotypes using SSR markers and identifies heterotic groups to improve hybrid yield through genetic divergence and heterosis analysis.

The paper develops MNP DNA markers and MNP-Seq method for accurate and efficient genotyping, showing high polymorphism and accuracy in rice and cotton.

This study simulates how increasing recombination rate affects introgression efficiency in backcross programs, showing it reduces linkage drag in cold regions but is harmful in hot regions.

The paper uses a multi-parental recombinant inbred line population in C. elegans to study gene expression variation and identifies eQTLs and trans-bands affecting gene expression.

This study develops a computational pipeline to reconstruct genomes from ultra-low coverage data in Hybrid Swarm populations and evaluates its effectiveness for genetic association studies.

rMVP improves GWAS computation by offering memory efficiency, visualization tools, and parallel acceleration for faster and more scalable analysis.

The paper introduces a statistical framework for detecting QTL hotspots that handles both individual-level and summarized data, addresses correlation structure and computational cost, and improves hotspot identification.

Drone-based remote phenotyping combined with machine learning and Bayesian analysis identified two loci regulating daily floral opening time in lettuce.

This study simulates QTL detection in multi-parent populations of sugar beet, finding that fewer, larger crosses improve detection power, while more parents help detect rare alleles with large effects when population size increases.

The paper integrates genetic mapping with transcriptome analysis to identify QTL and candidate genes for tillering control in sorghum using a recombinant inbred line population.

This study analyzes the genetic diversity of 194 maize landraces from South-West France using a 50K SNP array, identifying two genetic groups and a longitudinal gradient shaped by genetic drift and restricted gene flow.