Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Monohybrid Crosses01:20

Monohybrid Crosses

Overview
Monohybrid Crosses01:20

Monohybrid Crosses

Overview
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Designed diversity: from marker-assisted backcrossing to computationally optimised polygenic introgression.

Trends in plant science·2026
Same author

Ascertainment Bias in Cattle SNP Arrays and Implications for Multibreed Genomic Predictions.

Animal genetics·2026
Same author

Negative Energy Balance in Transition Cows Induces Complex Changes in Lipid Profile of Milk.

Metabolites·2026
Same author

Factors affecting recording methane emission phenotypes of composite and crossbreed beef cattle grazing tropical and subtropical rangelands of Northern Australia.

Journal of animal science·2026
Same author

Advancing rust resistance in elite wheat with haplotype mapping and a novel introgression strategy.

Journal of experimental botany·2026
Same author

A geospatial model of entry pathways of lumpy skin disease virus introduction into Australia.

Scientific reports·2026
Same journal

Comprehensive Analysis of Macrophage Dynamics, CCBE1, and Their Implications in Colorectal Cancer Microenvironment: Insights Into Tumor Progression and Therapeutic Opportunities.

Genetics research·2026
Same journal

Compound Heterozygous ATM Variants Cause Adolescent-Onset Cerebellar and Extrapyramidal Disease Without Telangiectasia in a Consanguineous Pakistani Family.

Genetics research·2026
Same journal

Biological Context-Informed and Population-Stratified Strategies Improve Genetic Diagnosis of CCDC22-Related Disorder.

Genetics research·2026
Same journal

Predicting the Impact of Deleterious Single-Nucleotide Polymorphisms in the p47ING1a Isoform of Human ING1 Gene.

Genetics research·2026
Same journal

Two Novel FBN2 Variants Causing Congenital Contractural Arachnodactyly.

Genetics research·2026
Same journal

Identification of Genetic Diagnostic Markers for Systemic Lupus Erythematosus.

Genetics research·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

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

LASSO with cross-validation for genomic selection.

M Graziano Usai1, Mike E Goddard, Ben J Hayes

  • 1Settore Genetica e Biotecnologie, AGRIS-Sardegna, Olmedo 07040, Italy. graziano.usai@gmail.com

Genetics Research
|February 4, 2010
PubMed
Summary
This summary is machine-generated.

The LASSO-LARS method accurately estimates marker effects for genomic selection, outperforming BLUP and BayesA. This approach is particularly useful for reducing genotyping costs by utilizing a smaller set of markers.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 16, 2026

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

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

Area of Science:

  • Animal Breeding and Genetics
  • Statistical Genomics
  • Quantitative Genetics

Background:

  • Genomic selection (GS) is crucial for improving livestock and crop traits.
  • Accurate estimation of marker effects is essential for effective GS.
  • Traditional methods like BLUP and Bayesian approaches have limitations in marker selection and computational efficiency.

Purpose of the Study:

  • To evaluate the Least Absolute Shrinkage and Selection Operator (LASSO) with Least Angle Regression (LARS) for estimating marker effects in genomic selection.
  • To compare the performance of the LASSO-LARS approach against established methods like BLUP and BayesA.
  • To assess the impact of marker subset selection on the accuracy of genomic estimated breeding values (GEBVs).

Main Methods:

  • Implemented a LASSO-LARS approach for marker effect estimation in genomic selection.
  • Utilized cross-validation with different population splitting strategies (random splitting, last generation sampling) on simulated and mouse data.
  • Compared LASSO-LARS with BLUP and BayesA using simulated data (5865 individuals, 6000 SNPs) and a mouse dataset (1885 individuals, 10656 SNPs).

Main Results:

  • LASSO-LARS achieved higher accuracy (0.89) in simulated data using 156 SNPs compared to BLUP (0.75) and BayesA (0.84).
  • Optimal accuracy in simulated data was obtained via random splitting across the entire population for cross-validation.
  • In mouse data, LASSO-LARS showed improved GEBV accuracy for weight at six weeks over BLUP and BayesA, with comparable or slightly lower accuracy for other traits.

Conclusions:

  • The LASSO-LARS approach is a viable and effective alternative for estimating marker effects in genomic selection.
  • This method offers potential cost savings in genotyping by enabling the use of a reduced marker subset.
  • LASSO-LARS demonstrates competitive or superior performance compared to BLUP and BayesA, especially when marker selection is optimized.