PapersFlow Research Brief
Genetic and phenotypic traits in livestock
Research Guide
What is Genetic and phenotypic traits in livestock?
Genetic and phenotypic traits in livestock refer to the heritable genetic variations and observable physical characteristics in domesticated animals that are analyzed using methods like genomic selection, population genetics, and quantitative genetics to improve breeding outcomes.
The field encompasses 169,842 works on topics including genomic selection, marker-assisted selection, genome-wide association studies, population genetics, genetic diversity, and livestock domestication. Pritchard et al. (2000) introduced a model-based clustering method in "Inference of Population Structure Using Multilocus Genotype Data" to infer population structure from multilocus genotype data, cited 33,557 times. Weir and Cockerham (1984) developed methods for estimating F-statistics in "ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE" to analyze population structure, cited 16,820 times.
Topic Hierarchy
Research Sub-Topics
Genomic Selection in Livestock
This sub-topic develops genomic estimated breeding values using high-density SNP markers for traits like milk yield and growth rate in cattle and sheep. Researchers optimize reference populations and prediction accuracy across breeds.
Genome-Wide Association Studies in Livestock
This sub-topic identifies QTLs and causal variants for complex traits such as disease resistance and feed efficiency through GWAS in diverse livestock populations. Researchers address population stratification and polygenic architecture.
Quantitative Genetics of Livestock Traits
This sub-topic estimates heritability, genetic correlations, and GxE interactions for production and reproduction traits using pedigree and genomic relationship matrices. Researchers apply BLUP and Bayesian methods for selection indices.
Population Genetics of Domestic Animals
This sub-topic analyzes genetic diversity, inbreeding, and admixture in livestock breeds using F-statistics and STRUCTURE software. Researchers track signatures of selection during domestication and breed formation.
Marker-Assisted Selection in Animal Breeding
This sub-topic integrates major gene markers into breeding programs for traits like hornlessness and muscle hypertrophy in cattle. Researchers evaluate MAS accuracy compared to genomic selection in multi-trait scenarios.
Why It Matters
Genetic and phenotypic traits analysis enables precise breeding programs in livestock, such as dairy and beef cattle, by predicting genetic values and selecting for traits like growth, meat quality, and reproduction. The 1000 Bull Genomes Project advances genomic selection from whole genome sequence data in dairy and beef cattle, supporting genomically-enhanced expected progeny differences (GE-EPDs) integrated into commercial herd management since the late 2000s. NIFA invested $2.8M in animal breeding, genetics, and genome research to develop novel quantitative genetic methods and new phenotypes for selection criteria. Recent preprints like "Omics Evidence Chains for Complex Traits in Beef Cattle: From Cross-Layer Colocalization to Genetic Evaluation and Application" combine genome, transcriptome, proteome, and metabolome data to link genes to complex traits.
Reading Guide
Where to Start
"GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update" by Peakall and Smouse (2012) is the beginner start because it provides an accessible Excel-based tool for population genetic analyses including F-statistics and heterozygosity, ideal for initial hands-on exploration of livestock genotype data.
Key Papers Explained
Pritchard et al. (2000) in "Inference of Population Structure Using Multilocus Genotype Data" establishes model-based clustering for population structure inference, which Weir and Cockerham (1984) in "ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE" complements with F-statistic estimation for differentiation analysis. Excoffier et al. (2007) in "Arlequin (version 3.0): an integrated software package for population genetics data analysis" and Peakall and Smouse (2012) in "GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update" build practical software implementations of these methods for genetic diversity and HWE tests. Nei (1978) in "ESTIMATION OF AVERAGE HETEROZYGOSITY AND GENETIC DISTANCE FROM A SMALL NUMBER OF INDIVIDUALS" provides foundational unbiased estimators that underpin software applications.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints focus on omics integration for beef cattle traits, whole-genome genomic selection via the 1000 Bull Genomes Project, and genetic parameters for indigenous chickens. NIFA funds novel quantitative methods and high-throughput phenotypes, while tools like GSPathways simulate breeding cycles with various genomic prediction models.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Inference of Population Structure Using Multilocus Genotype Data | 2000 | Genetics | 33.6K | ✓ |
| 2 | ESTIMATING<i>F</i>-STATISTICS FOR THE ANALYSIS OF POPULATION S... | 1984 | Evolution | 16.8K | ✕ |
| 3 | Arlequin (version 3.0): an integrated software package for pop... | 2007 | PubMed | 12.9K | ✓ |
| 4 | GenAlEx 6.5: genetic analysis in Excel. Population genetic sof... | 2012 | Bioinformatics | 12.8K | ✓ |
| 5 | ESTIMATION OF AVERAGE HETEROZYGOSITY AND GENETIC DISTANCE FROM... | 1978 | Genetics | 11.5K | ✓ |
| 6 | glmmTMB Balances Speed and Flexibility Among Packages for Zero... | 2017 | The R Journal | 11.1K | ✓ |
| 7 | Mendelian randomization with invalid instruments: effect estim... | 2015 | International Journal ... | 10.0K | ✓ |
| 8 | Fast model-based estimation of ancestry in unrelated individuals | 2009 | Genome Research | 9.9K | ✓ |
| 9 | Consistent Estimation in Mendelian Randomization with Some Inv... | 2016 | Genetic Epidemiology | 9.1K | ✓ |
| 10 | GCTA: A Tool for Genome-wide Complex Trait Analysis | 2010 | The American Journal o... | 8.8K | ✓ |
In the News
NIFA Invests $2.8M in Animal Breeding, Genetics and ...
priority (A1201)] . The program supports research on the development of novel quantitative genetic methods; national and regional breeding strategies; new phenotypes for improving selection criteri...
Heritable Agriculture and KWS target feed trait breakthroughs
Under the agreement, Heritable Agriculture will apply its proprietary AI algorithms and multi-omic analysis to KWS’s genomic and phenotypic datasets. The goal: to identify gene targets that deliver...
New hub to aid precision breeding of future farm animals
One focus for the research team will be investigating genetic traits linked to milk production in cattle.
Cattle Genomics: Genomic Tools for the Commercial Herd
In the late 2000s, the first genomic tests were delivered to the beef industry, allowing information on actual DNA content to be integrated into EPD calculations. We refer to these as genomically-e...
Advances and Challenges in Genome-Edited Livestock for ...
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Code & Tools
The goal of ‘IndexWizard’ is to provide a framework for the exploration of effects of complex selection indices on the genetic and phenotypic progr...
## Repository files navigation These scripts simulate any number of breeding cycles under a genomic selection framework using the pedigree breedin...
Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and h...
G2P is an integrated genomic selection (GS) package for predicting phenotypes from genotypes. It includes 15 GS algorithms and 13 evaluation measur...
This repository contains our R-package MoBPS and the associated packages (miraculix/RandomFieldsUtils/MoBPSmaps).
Recent Preprints
Genetic and Phenotypic Parameter Estimates of Body Weight ...
Genetic and phenotypic parameter estimates guide the setting of breeding objectives and the stage of selection in a breeding program [ 16 ]. These estimates are a prerequisite for chicken genetic i...
Improvement in genetic evaluation of quantitative traits in ...
cause continuously distributed phenotypes in the population 1, 2. The phenotypic variation of quantitative traits observed within a population may be due to genetic variation, environmental varia...
Breeding practices and trait preferences among smallholder ...
programmes. This study provides the first systematic baseline on Somali cattle breeding and shows that farmer-centred programmes can pair simple trait indices with community bull management to incr...
Omics Evidence Chains for Complex Traits in Beef Cattle: From Cross-Layer Colocalization to Genetic Evaluation and Application
Beef cattle breeding has entered the multi-omics era, where data from the genome, transcriptome, proteome, and metabolome can be combined to reveal how genes influence complex traits such as growth...
The 1000 bull genomes project - Toward genomic selection from whole genome sequence data in dairy and beef cattle
W150 - The 1000 Bull Genomes Project - Toward Genomic Selection From Whole Genome Sequence Data In Dairy and Beef Cattle Genomic prediction of breeding values is now used as the basis for selectio...
Latest Developments
Recent developments in livestock genetic and phenotypic traits research include advancements in genetic evaluation tools, such as new statistical frameworks to identify causal DNA changes influencing traits (NC State, ScienceDirect), and comprehensive multi-tissue single-cell atlases in cattle that reveal cellular heterogeneity and genetic mechanisms underlying important traits (Nature). Additionally, large-scale genomic data mining in U.S. Holstein cattle has identified genetic variants affecting quantitative traits such as milk yield and reproductive performance (PMC). Moreover, research continues to explore phenotypic traits related to hoof health, mobility, and milk production, leveraging state-of-the-art genetic evaluations and precision management tools (uscdcb.com, Farmonaut). As of 2026, genetic editing is also expected to significantly boost crop yields, indicating ongoing integration of genetic technologies in livestock and crop improvement (Farmonaut).
Sources
Frequently Asked Questions
What methods are used to infer population structure in livestock genetics?
Pritchard et al. (2000) in "Inference of Population Structure Using Multilocus Genotype Data" describe a model-based clustering method using multilocus genotype data to infer population structure and assign individuals to populations assuming K populations with allele frequencies. This approach corrects for population stratification in genetic studies. It has 33,557 citations and applies to livestock genetic diversity analysis.
How are F-statistics estimated for livestock population structure?
Weir and Cockerham (1984) in "ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE" provide methods to estimate F-statistics for analyzing population structure from genetic data. These statistics quantify differentiation among populations. The paper has 16,820 citations and supports quantitative genetics in animal breeding.
What software tools analyze population genetics data in livestock research?
Excoffier et al. (2007) developed Arlequin (version 3.0) in "Arlequin (version 3.0): an integrated software package for population genetics data analysis," which computes genetic diversity indices, allele frequencies, and tests for linkage equilibrium. Peakall and Smouse (2012) updated GenAlEx 6.5 in "GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update" for analyzing diploid codominant loci and F-statistics in Excel. Both tools aid livestock trait studies with 12,854 and 12,843 citations respectively.
How does genomic selection apply to livestock traits?
Genomic selection uses SNPs to predict quantitative phenotypes for traits like body weight in chicken and beef cattle, as in recent preprints on genetic parameter estimates and omics chains. Tools like G2P package include 15 GS algorithms for phenotype prediction from genotypes. The 1000 Bull Genomes Project enables genomic prediction of breeding values in dairy and beef cattle.
What is the current state of genetic evaluation in livestock breeding?
Recent preprints address genetic and phenotypic parameter estimates for body weight in indigenous chickens and quantitative traits under genomic selection frameworks. NIFA's $2.8M investment supports novel quantitative genetic methods and high-throughput phenotypes. Preprints like "The 1000 bull genomes project - Toward genomic selection from whole genome sequence data in dairy and beef cattle" advance whole-genome applications.
Open Research Questions
- ? How can multi-omics data from genome, transcriptome, proteome, and metabolome be integrated to causally link genes to complex livestock traits like growth and meat quality, as raised in beef cattle studies?
- ? What breeding strategies optimize genetic progress using complex selection indices in livestock populations with limited indigenous data?
- ? How do invalid instruments affect Mendelian randomization effect estimates for livestock quantitative traits, and what bias detection methods like Egger regression improve reliability?
- ? What genomic prediction models and generational training sets maximize response to selection in simulated livestock breeding cycles?
- ? How can whole genome sequence data from projects like 1000 Bull Genomes enhance accuracy of breeding value predictions across diverse cattle breeds?
Recent Trends
Recent preprints emphasize genetic parameter estimates for body weight in African indigenous chickens and omics evidence chains for beef cattle complex traits.
The 1000 Bull Genomes Project progresses toward genomic selection from whole-genome sequences in dairy and beef cattle.
NIFA allocated $2.8M for animal breeding genetics research, including new phenotypes and quantitative methods, while Heritable Agriculture applies AI to multi-omic datasets for feed trait improvements in livestock.
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