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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic

Penghao Wang1,2,3, Xiao-Qi Zhang1, Viet Dang1,2

  • 1Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, 6150, Australia.

Plant Phenomics (Washington, D.C.)
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new hybrid method for genomic selection (GS) that accurately predicts crop performance by modeling gene interactions and environmental factors. The approach identifies optimal genetic variations for specific environments, accelerating crop breeding.

Keywords:
De novo breedingGenomic selectionHaplotype predictionMachine learning

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Plant breeding and genetics
  • Agricultural science
  • Bioinformatics

Background:

  • Genomic selection (GS) accelerates crop improvement but faces challenges in predicting complex traits and adapting to diverse environments.
  • Accurate prediction of genomic estimated breeding values (GEBVs) for complex traits and their application across environments are critical for efficient crop breeding.

Purpose of the Study:

  • To develop and evaluate a novel hybrid method for genomic selection that models gene-gene and gene-environment interactions.
  • To enhance the prediction accuracy of phenotypic performance for complex traits and identify environment-specific optimal haplotypes.
  • To provide a user-friendly tool for breeders to leverage haplotype-based, environment-informed breeding strategies.

Main Methods:

  • Developed a hybrid method integrating genomic data (SNPs), soil parameters, and daily environmental variables to model complex interactions.
  • Utilized a dataset of 855 barley lines with phenotypic data for grain yield and flowering time across multiple environments.
  • Implemented a web-based interface for breeders to identify optimal haplotypes and associated varieties.

Main Results:

  • Achieved high prediction accuracies: 0.93 for flowering time and 0.82 for grain yield.
  • Identified significant haplotype blocks associated with flowering time (10 blocks) and grain yield (13 blocks), explaining over 90% of genetic variance.
  • Successfully predicted phenotypic effects of haplotypes and identified elite varieties for targeted crossing and selection.

Conclusions:

  • The novel hybrid method significantly improves prediction accuracy for complex traits in diverse environments.
  • The approach facilitates environment-informed breeding by identifying optimal haplotypes and predicting genotype x environment interactions.
  • The developed web tool streamlines the application of haplotype-based breeding for crop improvement, though multi-trait trade-offs require future investigation.