scholarly journals Required marker properties for unbiased estimates of the genetic correlation between populations

2018 ◽  
Author(s):  
Yvonne C.J. Wientjes ◽  
Mario P.L. Calus ◽  
Pascal Duenk ◽  
Piter Bijma
Keyword(s):  
Genetic Correlation ◽  
Genetic Correlations ◽  
Allele Frequencies ◽  
Substitution Effects ◽  
Marker Allele ◽  
Allele Substitution ◽  
Single Genotype ◽  
Different Populations ◽  
The Difference ◽  
Two Populations

ABSTRACTPopulations generally differ in environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in both populations is known as the additive genetic correlation between populations and can differ from one. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect bias of the estimated genetic correlation. We simulated two populations that were separated for 50 generations. Markers and causal loci were selected to either have similar or different allele frequencies in the two populations. Differences in consistency of LD between populations were obtained by using different marker density panels. Results showed that when the difference in allele frequencies of causal loci between populations was reflected by the markers, genetic correlations were only slightly underestimated using markers. This was even the case when LD patterns, measured by LD statistic r, were different between populations. When the difference in allele frequencies of causal loci between populations was not reflected by the markers, genetic correlations were severely underestimated. We conclude that for an unbiased estimate of the genetic correlation between populations, marker allele frequencies should reflect allele frequencies of causal loci so that marker-based relationships can accurately predict the relationships at causal loci, i.e. E(Gcausal loci|Gmarkers) ≠ Gmarkers. Differences in LD between populations have little effect on the estimated genetic correlation.

scholarly journals THE CORRELATION OF SUBSTITUTION EFFECTS ACROSS POPULATIONS AND GENERATIONS IN THE PRESENCE OF NON-ADDITIVE FUNCTIONAL GENE ACTION

Genetics ◽  
2021 ◽  
Author(s):  
Andres Legarra ◽  
Carolina A Garcia-Baccino ◽  
Yvonne C J Wientjes ◽  
Zulma G Vitezica
Keyword(s):  
Gene Action ◽  
Taylor Series Expansion ◽  
Additive Functional ◽  
Genetic Distances ◽  
Allele Frequencies ◽  
Functional Gene ◽  
Substitution Effects ◽  
Reference Allele ◽  
Allele Substitution ◽  
The Difference

Abstract Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of non-additive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the non-additive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects.

scholarly journals The Correlation Of Substitution Effects Across Populations And Generations In The Presence Of Non-Additive Functional Gene Action

2020 ◽  
Author(s):  
A. Legarra ◽  
C.A. Garcia-Baccino ◽  
Y.C.J. Wientjes ◽  
Z.G. Vitezica
Keyword(s):  
Gene Action ◽  
Taylor Series Expansion ◽  
Genotype By Environment Interaction ◽  
Additive Functional ◽  
Allele Frequencies ◽  
Functional Gene ◽  
Substitution Effects ◽  
Environment Interaction ◽  
Additive Variance ◽  
The Difference

ABSTRACTAllele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In presence of non-additive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance and additive by additive variance; relationships across populations (similar to Fst indexes); and functions of heterozygosities at the markers. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Using published estimates of the needed parameters, we estimate the correlation between substitution effects to be around 0.60 for distinct breeds and higher than 0.9 for generations closer than 5. Simulation results confirmed our derivations although our estimators tended to underestimate the correlation. Our derivations are useful to understand the difficulty in predicting across populations, to forecast the possibility of prediction across populations, and we suggest that they can be useful to disentangle genotype by environment interaction from genotype by genotype interaction.

Micron blowout: heritability and genetic correlations with fibre diameter and secondary follicle diameter

1999 ◽  
Vol 50 (8) ◽  
pp. 1375 ◽  
Author(s):  
J. A. Hill ◽  
R. W. Ponzoni ◽  
J. W. James
Keyword(s):  
Genetic Correlation ◽  
Scale Effect ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Fibre Diameter ◽  
The Other ◽  
Follicle Diameter ◽  
The Difference ◽  
Biased Estimates ◽  
Practical Implications

Calculation of micron blowout as the difference between fibre diameter records taken at different ages can produce ‘biased’ estimates of the heritability and genetic correlations due to a scale effect. In some instances, standardisation of the fibre diameter records to a common genetic variance (i.e. removal of the scale effect) changed the heritability and the genetic correlation estimates. The effect of standardisation on the heritability of micron blowout was determined to a large extent by the difference in the genetic variance between the 2 fibre diameter measurements, whereas in the case of the genetic correlation between micron blowout and another trait, it was also dependent on the genetic correlation between the other trait and the two fibre diameters. It is recommended that heritabilities and genetic correlations involving micron blowout be calculated after standardising the fibre diameter measurements to a common genetic variance. The practical implications of the results are briefly discussed.

Distinguishing between Different Populations of Harp Seal (Phoca groenlandica) by Chemometry of the Fatty Acid Profiles in the Jaw Bone

1993 ◽  
Vol 50 (7) ◽  
pp. 1400-1404 ◽  
Author(s):  
Otto Grahl-Nielsen ◽  
Olav Mjaavatten ◽  
Einar Tvedt
Keyword(s):  
Fatty Acid ◽  
Barents Sea ◽  
Principal Component ◽  
Eye Lens ◽  
Fatty Acid Profiles ◽  
Phoca Groenlandica ◽  
The Barents Sea ◽  
Different Populations ◽  
The Difference ◽  
Two Populations

The relative amounts of various fatty acids of jawbone and eye lens were determined in harp seals (Phoca groenlandica) caught in the Greenland Sea and in the Barents Sea. The two tissues had distinctly different profiles. The fatty acid profile in the lens tissue changed with age. Principal component analysis of the data showed that the profiles in the jawbone were different in seals from the two populations, while the difference was much less prominent in the case of the eye lens. Using the jawbone profiles from the western and eastern seals as reference, it was shown that seven seals, caught in west coast seine nets during the large invasion of harp seals along the Norwegian coast in the winter of 1986–87, had come from the eastern population.

scholarly journals The Impact of Non-additive Effects on the Genetic Correlation Between Populations

2019 ◽  
Vol 10 (2) ◽  
pp. 783-795 ◽  
Author(s):  
Pascal Duenk ◽  
Piter Bijma ◽  
Mario P. L. Calus ◽  
Yvonne C. J. Wientjes ◽  
Julius H. J. van der Werf
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Genetic Model ◽  
Allele Frequencies ◽  
Additive Effects ◽  
Simulated Genotype ◽  
The Difference ◽  
Allelic Interaction ◽  
Additive Genetic Effects ◽  
The Impact

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations (rg). This rg can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of rg remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of rg. In addition, we found that with epistasis, rg decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, rg had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true rg values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, rg dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.

Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model

2019 ◽  
Vol 59 (2) ◽  
pp. 207 ◽  
Author(s):  
A. Haiduck Padilha ◽  
E. P. M. Alfonzo ◽  
D. S. Daltro ◽  
H. A. L. Torres ◽  
J. Braccini Neto ◽  
...  
Keyword(s):  
Regression Model ◽  
Somatic Cell ◽  
Genetic Correlation ◽  
Milk Yield ◽  
Genetic Correlations ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Random Regression Model ◽  
The Difference ◽  
Estimated Breeding Values

The objective was to estimate genetic correlations for persistency, milk yield and somatic cell score (SCS) in Holstein cattle in Brazil. A dataset with 190389 records of test-day milk and of test-day SCS from 21824 cows was used. Two-trait random regression model with a fourth order Legendre polynomial was used. Persistency (PS) was defined as the difference between estimated breeding values (EBV) along different days in milk using two formulae: and PS2=(EBV290–EBV90). Larger values for PS2 or lower ones for PS1 indicate higher persistency. Heritability was 0.24 for 305-day milk yield, 0.14 for SCS up to 305 days, 0.15 for PS1 and 0.14 for PS2. Genetic correlation between 305-day milk yield and SCS up to 305 days was –0.47. Genetic correlation of 305-day milk yield with PS1 and PS2 was –0.32 and 0.30, respectively. Genetic correlation of SCS up to 305 days was 0.25 with PS1 and –0.20 with PS2. The additive genetic correlations between milk yield, SCS and persistency showed that selection for higher persistency or for low somatic cell score will increase 305-day milk yield.

scholarly journals 68 Genomic prediction accuracies of vulva size traits in Landrace and Yorkshire gilts

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 39-39
Author(s):  
Flor Anita Corredor ◽  
Richard J Leach ◽  
Jason W Ross ◽  
Aileen F Keating ◽  
Nick V L Serão
Keyword(s):  
Genomic Prediction ◽  
Fixed Effects ◽  
Cross Validation ◽  
Random Effect ◽  
Substitution Effects ◽  
Marker Allele ◽  
Pork Industry ◽  
Allele Substitution ◽  
Industry Center ◽  
Fold Cross Validation

Abstract Recent results indicated that vulva size measured prior to puberty may be predictive of reproductive performance in sows. Therefore, the objective of this study was to estimate genomic prediction accuracies for vulva size traits in purebred gilts. A total of 1,185 Landrace (n = 477) and Yorkshire (n = 708) gilts originated from two different lines were used in this study. All animals had vulva size measurements taken at an average 21.5 weeks of age (SD = 5.8). Measurements included vulva width (VW), vulva height (VH), and vulva area (VA). Genotype data (Geneseek GGP-HD) was available for all animals, for ~40K SNPs. Marker allele substitution effects were estimated using Bayes-B (pi = 0.99) in a model including the fixed effects of contemporary group, line, breed (for multi-breed analysis only) and body weight (covariate), and the random effect of SNPs. Genomic prediction accuracies were estimated using three training and validation strategies: between-breed, within-breed (4 and 6 cross-validation folds for Landrace and Yorkshire, respectively), and multi-breed (10-fold cross-validation, using one-fold per breed for validation at a time). Between-breed accuracies were low and consistently negative, with -0.02, -0.10, and -0.05 in Landrace and -0.05, -0.04 and -0.03 in Yorkshire, for VW, VH, and VA, respectively. Within Landrace, these were moderate, with 0.35 (VW), 0.42 (VH), and 0.56 (VA), whereas lower accuracies were obtained for Yorkshire, with 0.07 (VW), 0.20 (VH), and 0.14 (VA). Multi-breed accuracies were low with 0.14 (VW), 0.14 (VH), and 0.24 (VA) for Landrace, and 0.03 (VW), 0.16 (VH), and 0.09 (VA) for Yorkshire. These results indicate that genomic selection for vulva size traits is possible in Landrace, but limited in Yorkshire gilts. The low between- and multi-breed results suggest that QTL for these traits are in opposite phases between breeds and/or do not segregate in both breeds. Financial support from the Iowa Pork Industry Center is appreciated.

Genetic and Environmental Influences on Executive Functions and Intelligence in the ABCD Study

2020 ◽  
Author(s):  
Samantha M Freis ◽  
Claire Morrison ◽  
Jeffrey M. Lessem ◽  
John K. Hewitt ◽  
Naomi P. Friedman
Keyword(s):  
Cognitive Development ◽  
Executive Functions ◽  
Genetic Correlation ◽  
Middle Childhood ◽  
Middle Age ◽  
Environmental Influences ◽  
Genetic Correlations ◽  
Twin Studies

Executive functions (EFs) and intelligence (IQ) are phenotypically correlated and heritable; however, they show variable genetic correlations in twin studies spanning childhood to middle age. We analyzed data from over 11,000 children (9-10-year-olds, including 749 twin pairs) in the Adolescent Brain Cognitive Development (ABCD) Study to examine the phenotypic and genetic relations between EFs and IQ in childhood. We identified two EF factors – Common EF and Updating-Specific, which were both related to IQ (rs = .64-.81). Common EF and IQ were heritable (53-67%), and their genetic correlation (rG = .86) was not significantly different than 1. These results suggest that EFs and IQ are phenotypically but not genetically separable in middle childhood.

scholarly journals Inbreeding of Bottlenecked Butterfly Populations: Estimation Using the Likelihood of Changes in Marker Allele Frequencies

Genetics ◽  
1999 ◽  
Vol 151 (3) ◽  
pp. 1053-1063 ◽  
Author(s):  
Ilik J Saccheri ◽  
Ian J Wilson ◽  
Richard A Nichols ◽  
Michael W Bruford ◽  
Paul M Brakefield
Keyword(s):  
Linkage Disequilibrium ◽  
Probability Distribution ◽  
Reproductive Success ◽  
Genetic Markers ◽  
Allele Frequencies ◽  
Demographic Parameters ◽  
Bicyclus Anynana ◽  
Marker Allele ◽  
Wide Applicability ◽  
Per Gene

Abstract Polymorphic enzyme and minisatellite loci were used to estimate the degree of inbreeding in experimentally bottlenecked populations of the butterfly, Bicyclus anynana (Satyridae), three generations after founding events of 2, 6, 20, or 300 individuals, each bottleneck size being replicated at least four times. Heterozygosity fell more than expected, though not significantly so, but this traditional measure of the degree of inbreeding did not make full use of the information from genetic markers. It proved more informative to estimate directly the probability distribution of a measure of inbreeding, σ2, the variance in the number of descendants left per gene. In all bottlenecked lines, σ2 was significantly larger than in control lines (300 founders). We demonstrate that this excess inbreeding was brought about both by an increase in the variance of reproductive success of individuals, but also by another process. We argue that in bottlenecked lines linkage disequilibrium generated by the small number of haplotypes passing through the bottleneck resulted in hitchhiking of particular marker alleles with those haplotypes favored by selection. In control lines, linkage disequilibrium was minimal. Our result, indicating more inbreeding than expected from demographic parameters, contrasts with the findings of previous (Drosophila) experiments in which the decline in observed heterozygosity was slower than expected and attributed to associative overdominance. The different outcomes may both be explained as a consequence of linkage disequilibrium under different regimes of inbreeding. The likelihood-based method to estimate inbreeding should be of wide applicability. It was, for example, able to resolve small differences in σ2 among replicate lines within bottleneck-size treatments, which could be related to the observed variation in reproductive viability.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

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