Variation I: Genetics

Mate Choice ◽  
2017 ◽  
Author(s):  
Gil G. Rosenthal
Keyword(s):  
Genetic Variation ◽  
Gene Structure ◽  
Infinite Number ◽  
Reverse Genetics ◽  
Forward Genetics ◽  
Genetic Models ◽  
Genetic Studies ◽  
Quantitative Genetic ◽  
Negative Effect ◽  
Genomic Regions

This chapter reviews the main approaches for characterizing preference genetics. Approaches to understanding the genetics underlying preferences (or any other phenotype) take two broad forms. The first approach consists of attempts to identify particular genes or genomic regions associated with preference variation; for preferences, this is typically done using so-called forward genetics, whereby variation in phenotype is correlated with variation in genotype. Alternatively, the effects of candidate genes on preference can be characterized using reverse genetics, whereby gene structure or function is altered to test its effect on phenotype. The second approach encompasses quantitative genetic studies that assume that the underlying genetic variation is continuous and additive. Quantitative genetic models often assume an infinite number of loci each contributing infinitely small positive or negative effect, summing to determine trait value.

scholarly journals Recombination rate variation and speciation: theoretical predictions and empirical results from rabbits and mice

2012 ◽  
Vol 367 (1587) ◽  
pp. 409-421 ◽  
Author(s):  
Michael W. Nachman ◽  
Bret A. Payseur
Keyword(s):  
Genetic Variation ◽  
Gene Flow ◽  
Dna Sequence ◽  
Population Genetic ◽  
Sequence Variation ◽  
Genetic Models ◽  
Rate Variation ◽  
Ancestral Polymorphism ◽  
Dna Sequence Variation ◽  
Genomic Regions

Recently diverged taxa may continue to exchange genes. A number of models of speciation with gene flow propose that the frequency of gene exchange will be lower in genomic regions of low recombination and that these regions will therefore be more differentiated. However, several population-genetic models that focus on selection at linked sites also predict greater differentiation in regions of low recombination simply as a result of faster sorting of ancestral alleles even in the absence of gene flow. Moreover, identifying the actual amount of gene flow from patterns of genetic variation is tricky, because both ancestral polymorphism and migration lead to shared variation between recently diverged taxa. New analytic methods have been developed to help distinguish ancestral polymorphism from migration. Along with a growing number of datasets of multi-locus DNA sequence variation, these methods have spawned a renewed interest in speciation models with gene flow. Here, we review both speciation and population-genetic models that make explicit predictions about how the rate of recombination influences patterns of genetic variation within and between species. We then compare those predictions with empirical data of DNA sequence variation in rabbits and mice. We find strong support for the prediction that genomic regions experiencing low levels of recombination are more differentiated. In most cases, reduced gene flow appears to contribute to the pattern, although disentangling the relative contribution of reduced gene flow and selection at linked sites remains a challenge. We suggest fruitful areas of research that might help distinguish between different models.

scholarly journals Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Genetics ◽  
2022 ◽  
Vol 220 (1) ◽  
Author(s):  
Erik C Andersen ◽  
Matthew V Rockman
Keyword(s):  
Phenotypic Variation ◽  
Genetic Studies ◽  
Phenotypic Differences ◽  
Natural Genetic Variation ◽  
C Elegans ◽  
Advantages And Disadvantages ◽  
Quantitative Genetic ◽  
Model Animal ◽  
Exceptional Value ◽  
Genomic Regions

Abstract Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.

Maintenance of Quantitative Genetic Variation

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Genetic Variation ◽  
Quantitative Genetics ◽  
Stabilizing Selection ◽  
Quantitative Genetic ◽  
Wide Range

One of the major unresolved issues in quantitative genetics is what accounts for the amount of standing genetic variation in traits. A wide range of models, all reviewed in this chapter, have been proposed, but none fit the data, either giving too much variation or too little apparent stabilizing selection.

scholarly journals UNEQUAL CROSSING OVER AT THE rRNA TANDON AS A SOURCE OF QUANTITATIVE GENETIC VARIATION IN DROSOPHILA

Genetics ◽  
1980 ◽  
Vol 95 (3) ◽  
pp. 727-742 ◽  
Author(s):  
R Frankham ◽  
D A Briscoe ◽  
R K Nurthen
Keyword(s):  
Genetic Variation ◽  
Selection Response ◽  
Crossing Over ◽  
Wild Type ◽  
X Chromosomes ◽  
Unequal Crossing Over ◽  
Quantitative Genetic ◽  
Y Chromosomes ◽  
Homozygous Line ◽  
Minimum Estimate

ABSTRACT Abdominal bristle selection lines (three high and three low) and controls were founded from a marked homozygous line to measure the contribution of sex-linked "mutations" to selection response. Two of the low lines exhibited a period of rapid response to selection in females, but not in males. There were corresponding changes in female variance, in heritabilities in females, in the sex ratio (a deficiency of females) and in fitness, as well as the appearance of a mutant phenotype in females of one line. All of these changes were due to bb alleles (partial deficiencies for the rRNA tandon) in the X chromosomes of these lines, while the Y chromosomes remained wild-type bb+. We argue that the bb alleles arose by unequal crossing over in the rRNA tandon.—A prediction of this hypothesis is that further changes can occur in the rRNA tandon as selection is continued. This has now been shown to occur.—Our minimum estimate of the rate of occurrence of changes at the rRNA tandon is 3 × 10-4. As this is substantially higher than conventional mutation rates, the questions of the mechanisms and rates of origin of new quantitative genetic variation require careful re-examination.

scholarly journals The limits of normal approximation for adult height

2021 ◽  
Author(s):  
Sergei A. Slavskii ◽  
Ivan A. Kuznetsov ◽  
Tatiana I. Shashkova ◽  
Georgii A. Bazykin ◽  
Tatiana I. Axenovich ◽  
...  
Keyword(s):  
Complex Traits ◽  
Adult Height ◽  
Normal Approximation ◽  
Classical Model ◽  
Model Complexity ◽  
Test Case ◽  
Genetic Studies ◽  
Quantitative Genetic ◽  
Profound Influence ◽  
Log Normal

AbstractAdult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

scholarly journals Genetic variation in recombination rate in the pig

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Martin Johnsson ◽  
Andrew Whalen ◽  
Roger Ros-Freixedes ◽  
Gregor Gorjanc ◽  
Ching-Yi Chen ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Recombination Rate ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Genetic Material ◽  
A Genome ◽  
Pig Genome ◽  
Genetic Length ◽  
Trait Locus ◽  
Genomic Regions

Abstract Background Meiotic recombination results in the exchange of genetic material between homologous chromosomes. Recombination rate varies between different parts of the genome, between individuals, and is influenced by genetics. In this paper, we assessed the genetic variation in recombination rate along the genome and between individuals in the pig using multilocus iterative peeling on 150,000 individuals across nine genotyped pedigrees. We used these data to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig. Results Our results confirmed known features of the recombination landscape of the pig genome, including differences in genetic length of chromosomes and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, there were differences in average autosome-wide recombination rate between lines. The heritability of autosome-wide recombination rate was low but not zero (on average 0.07 for females and 0.05 for males). We found six genomic regions that are associated with recombination rate, among which five harbour known candidate genes involved in recombination: RNF212, SHOC1, SYCP2, MSH4 and HFM1. Conclusions Our results on the variation in recombination rate in the pig genome agree with those reported for other vertebrates, with a low but nonzero heritability, and the identification of a major quantitative trait locus for recombination rate that is homologous to that detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.

scholarly journals K-seq, an affordable, reliable, and open Klenow NGS-based genotyping technology

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Peio Ziarsolo ◽  
Tomas Hasing ◽  
Rebeca Hilario ◽  
Victor Garcia-Carpintero ◽  
Jose Blanca ◽  
...  
Keyword(s):  
Cost Effective ◽  
Genetic Distances ◽  
Set Design ◽  
Special Equipment ◽  
Genetic Studies ◽  
Set Up ◽  
Genomic Regions ◽  
Biology Laboratory ◽  
Genotyping Technology ◽  
Genetic Results

Abstract Background K-seq, a new genotyping methodology based on the amplification of genomic regions using two steps of Klenow amplification with short oligonucleotides, followed by standard PCR and Illumina sequencing, is presented. The protocol was accompanied by software developed to aid with primer set design. Results As the first examples, K-seq in species as diverse as tomato, dog and wheat was developed. K-seq provided genetic distances similar to those based on WGS in dogs. Experiments comparing K-seq and GBS in tomato showed similar genetic results, although K-seq had the advantage of finding more SNPs for the same number of Illumina reads. The technology reproducibility was tested with two independent runs of the tomato samples, and the correlation coefficient of the SNP coverages between samples was 0.8 and the genotype match was above 94%. K-seq also proved to be useful in polyploid species. The wheat samples generated specific markers for all subgenomes, and the SNPs generated from the diploid ancestors were located in the expected subgenome with accuracies greater than 80%. Conclusion K-seq is an open, patent-unencumbered, easy-to-set-up, cost-effective and reliable technology ready to be used by any molecular biology laboratory without special equipment in many genetic studies.

scholarly journals The Molecular Basis of Quantitative Genetic Variation in Central and Secondary Metabolism in Arabidopsis

Genetics ◽  
1998 ◽  
Vol 149 (2) ◽  
pp. 739-747 ◽  
Author(s):  
Thomas Mitchell-Olds ◽  
Deana Pedersen
Keyword(s):  
Enzyme Activity ◽  
Genetic Variation ◽  
Secondary Metabolism ◽  
Genetic Correlations ◽  
Theoretical Models ◽  
Model Systems ◽  
Activity Levels ◽  
Significant Qtls ◽  
Cis Acting ◽  
Quantitative Genetic

Abstract To find the genes controlling quantitative variation, we need model systems where functional information on physiology, development, and gene regulation can guide evolutionary inferences. We mapped quantitative trait loci (QTLs) influencing quantitative levels of enzyme activity in primary and secondary metabolism in Arabidopsis. All 10 enzymes showed highly significant quantitative genetic variation. Strong positive genetic correlations were found among activity levels of 5 glycolytic enzymes, PGI, PGM, GPD, FBP, and G6P, suggesting that enzymes with closely related metabolic functions are coregulated. Significant QTLs were found influencing activity of most enzymes. Some enzyme activity QTLs mapped very close to known enzyme-encoding loci (e.g., hexokinase, PGI, and PGM). A hexokinase QTL is attributable to cis-acting regulatory variation at the AtHXK1 locus or a closely linked regulatory locus, rather than polypeptide sequence differences. We also found a QTL on chromosome IV that may be a joint regulator of GPD, PGI, and G6P activity. In addition, a QTL affecting PGM activity maps within 700 kb of the PGM-encoding locus. This QTL is predicted to alter starch biosynthesis by 3.4%, corresponding with theoretical models, suggesting that QTLs reflect pleiotropic effects of mutant alleles.

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