Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits: Genetic Model for Diploid Plants

2007 ◽  
Vol 34 (6) ◽  
pp. 562-568 ◽  
Author(s):  
Lide Han ◽  
Jian Yang ◽  
Jun Zhu
Keyword(s):  
Quantitative Traits ◽  
Genetic Model ◽  
Genetic Effects ◽  
Cytoplasmic Interaction

scholarly journals Analysis of conditional genetic effects and variance components in developmental genetics.

Genetics ◽  
1995 ◽  
Vol 141 (4) ◽  
pp. 1633-1639 ◽  
Author(s):  
J Zhu
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Model ◽  
Genetic Effects ◽  
Unbiased Estimation ◽  
Developmental Genetics ◽  
Additive Effects ◽  
Genotype X Environment ◽  
Worked Example

Abstract A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

Bayesian Model Choice and Search Strategies for Mapping Interacting Quantitative Trait Loci

Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 867-883 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
David B Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Model ◽  
Genetic Effects ◽  
Monte Carlo Algorithm ◽  
Data Set ◽  
Epistatic Effects ◽  
Trait Loci

AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.

scholarly journals PSVIII-26 Gene editing of complex traits

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 259-260
Author(s):  
Ashley Ling ◽  
Romdhane Rekaya
Keyword(s):  
Genetic Engineering ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Gene Editing ◽  
Genetic Model ◽  
Variable Number ◽  
Production Parameters ◽  
Potential Gain ◽  
Better Than

Abstract Gene editing (GE) is a form of genetic engineering in which DNA is removed, inserted or replaced. For simple monogenic traits, the technology has been successfully implemented to create heritable modifications in animals and plants. The benefits of these niche applications are undeniable. For quantitative traits the benefits of GE are hard to quantify mainly because these traits are not genetic enough (low to moderate heritability) and their genetic architecture is often complex. Because its impact on the gene pool through the introduction of heritable modifications, the potential gain from GE must be evaluated within reasonable production parameters and in comparison, with available tools used in animal selection. A simulation was performed to compare GE with genomic selection (GS) and QTN-assisted selection (QAS) under four experimental factors: 1) heritability (0.1 or 0.4), 2) number of QTN affecting the trait (1000 or 10000) and their effect distribution (Gamma or uniform); 3) Percentage of selected females (100% or 33%); and 4) fixed or variable number of edited QTNs. Three models GS (M1), GS and GE (M2), and GS and QAS (M3) were implemented and compared. When the QTN effects were sampled from a Gamma distribution, all females were selected, and non-segregating QTNs were replaced, M2 clearly outperformed M1 and M3, with superiority ranging from 19 to 61%. Under the same scenario, M3 was 7 to 23% superior to M1. As the complexity of the genetic model increased (10000 QTN; uniform distribution), only one third of the females were selected, and the number of edited QTNs was fixed, the superiority of M2 was significantly reduced. In fact, M2 was only slightly better than M3 (2 to 6%). In all cases, M2 and M3 were better than M1. These results indicate that under realistic scenarios, GE for complex traits might have only limited advantages.

An autotetraploid model for genetic analysis of ozone tolerance in potato, Solanum tuberosum L.

Genome ◽  
1987 ◽  
Vol 29 (1) ◽  
pp. 85-90 ◽  
Author(s):  
N. Dragoescu ◽  
R. R. Hill Jr. ◽  
E. J. Pell
Keyword(s):  
Solanum Tuberosum ◽  
Genetic Analysis ◽  
Alternative Model ◽  
Genetic Model ◽  
Solanum Tuberosum L ◽  
Genetic Effects ◽  
Sums Of Squares ◽  
Ozone Tolerance ◽  
Additive Genetic Effects ◽  
Nonadditive Effects

A genetic model for the analysis of descendants of two autotetraploid parents was developed and applied to genetic analysis of ozone tolerance in the potato (Solanum tuberosum L.). The model was developed for two alleles with chromosome segregation at a single locus and contained 13 parameters. Assumptions about the genotype of the parents were not required. A multiple regression approach was used to derive sums of squares associated with the different parameters. Additive genetic effects for ozone resistance were more important than nonadditive genetic effects in the descendants of two sets of crosses. Deviations from the genetic model were not significant in either cross. Digenic effects and parameters associated with a disequilibrium constant were the only other significant effects, but sums of squares owing to these effects were much smaller than those for additive effects. Generation means derived from the crosses indicated that part of the nonadditive effects may have been caused by inbreeding depression. An alternative model with only additive genetic effects and a parameter with the coefficient of inbreeding as the coefficient was evaluated. The alternative model did not fit the observed data as well as the original model. Key words: ozone tolerance, potato, Solanum.

scholarly journals Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253167
Author(s):  
Mita Khatun ◽  
Md. Mamun Monir ◽  
Ting Xu ◽  
Haiming Xu ◽  
Jun Zhu
Keyword(s):  
Surface Area ◽  
Body Surface Area ◽  
Body Surface ◽  
Genetic Model ◽  
Genetic Regulation ◽  
Genetic Effects ◽  
Additive Effect ◽  
Conditional Models ◽  
Artery Disease ◽  
Calcium Compounds

Body surface area (BSA) is an important trait used for many clinical purposes. People’s BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10−5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ –0.013 for T/T and 0.013 for G/G, −Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and –0.012 for G/G, −Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ –0.018 for G/G and 0.018 for C/C, −Log10 PEW = 19.737) and dominance (d≙ –0.038 for G/C, −Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

PSVIII-4 Genetic evaluation of functional heifer longevity in north American angus cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 240-241
Author(s):  
Hinayah R Oliveira ◽  
Stephen P Miller ◽  
Luiz F Brito ◽  
Flavio S Schenkel
Keyword(s):  
Random Effects ◽  
Maternal Effects ◽  
Statistical Models ◽  
Evaluation System ◽  
Genetic Model ◽  
Genetic Evaluation ◽  
Genetic Effects ◽  
Reduced Model ◽  
Complete Model ◽  
Angus Cattle

Abstract The goals of this study were to develop a genetic evaluation system for a novel trait called functional heifer longevity (FHL), and determine if this novel trait is heritable. The FHL trait was defined as binary, in which the heifers received the code 1 if they had calved by the end of their third year (n = 377,938), or 0 if they were culled/sold during this period (n = 368,308). Analysis were performed using linear animal models and Bayesian inference. The significant systematic effects included in the statistical models are born by embryo transfer, year-season of birth, and age at calving (in months). Three models, differing according to their random effects (i.e., reduced model, which included only herd-year-season and additive genetic random effects; maternal genetic model, which added maternal genetic effects; and complete model, which further added maternal permanent environmental effects), were compared based on the deviance information criterion (DIC) and the estimates of genetic parameters. The reduced model was preferred according to the DIC values. However, high maternal heritabilities were estimated using the maternal genetic (0.51) and complete (0.36) models, indicating that maternal effects can impact the selection of heifers for breeding. Similar additive genetic heritabilities were estimated among the three models (0.24, 0.27, and 0.25 using the reduced, maternal genetic, and complete models, respectively), and no significant re-ranking of selection candidates were observed based on their additive genetic breeding values. Total heritabilities and correlations estimated between additive genetic and maternal genetic effects were 0.37 and -0.28 for the maternal genetic, and 0.31 and -0.27 for the complete model, respectively. This study shows that FHL is heritable, and that including maternal effects in the statistical models might be important. These results contribute to a larger project studying the genetics of female longevity in Angus cattle.

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