scholarly journals Ghost QTL and hotspots in experimental crosses - novel solution by mixed model with nonzero mean

2019 ◽  
Author(s):  
Piotr A. Szulc ◽  
Jonas Wallin ◽  
Małgorzata Bogdan ◽  
R.W. Doerge ◽  
David O. Siegmund
Keyword(s):  
Mixed Model ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Simultaneous Detection ◽  
Correlation Structure ◽  
Selection Strategy ◽  
Polygenic Effect ◽  
Mixed Effect ◽  
Sample Correlation ◽  
Nonzero Mean

Abstract“Ghost-QTL” are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome which are strongly correlated with the summary polygenic effect depend on a specific sample correlation structure determined by the genotype at all loci. During the analysis of e-QTL data or recombinant inbred lines this correlation structure is preserved for all traits under consideration, and may lead to the so called “hot-spots” via the detection of the summary polygenic effect at exactly the same positions for most of the considered traits. We illustrate that the problem can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach allows to eliminate ghost-QTL/false hot spot effects, while preserving a high power of detection of true QTL effects.

scholarly journals Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects

Genetics ◽  
2021 ◽  
Author(s):  
Jonas Wallin ◽  
Małgorzata Bogdan ◽  
Piotr A Szulc ◽  
R W Doerge ◽  
David O Siegmund
Keyword(s):  
Recombinant Inbred Lines ◽  
Simultaneous Detection ◽  
Selection Strategy ◽  
Strongly Correlated ◽  
Mixed Effect ◽  
Novel Approach ◽  
Effect Model ◽  
Multiple Qtl ◽  
Sample Correlation ◽  
False Discoveries

Abstract Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection.

Diverse sources of resistance to Spodoptera litura (F.) in groundnut

2019 ◽  
Vol 79 (01S) ◽  
Author(s):  
M. A. Saleem ◽  
G. K. Naidu ◽  
H. L. Nadaf ◽  
P. S. Tippannavar
Keyword(s):  
Recombinant Inbred ◽  
Spodoptera Litura ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Insect Pest ◽  
Inbred Lines ◽  
Leaf Damage ◽  
Botanical Variety ◽  
Resistant Genotypes ◽  
Botanical Varieties

Spodoptera litura an important insect pest of groundnut causes yield loss up to 71% in India. Though many effective chemicals are available to control Spodoptera, host plant resistance is the most desirable, economic and eco-friendly strategy. In the present study, groundnut mini core (184), recombinant inbred lines (318) and elite genotypes (44) were studied for their reaction to Spodoptera litura under hot spot location at Dharwad. Heritable component of variation existed for resistance to Spodoptera in groundnut mini core, recombinant inbred lines and elite genotypes indicating scope for selection of Spodoptera resistant genotypes. Only 29 (15%) genotypes belonging to hypogaea, fastigiata and hirsuta botanical varieties under mini core set, 15 transgressive segregants belonging to fastigiata botanical variety among 318 recombinant inbred lines and three genotypes belonging to hypogaea and fastigiata botanical varieties under elite genotypes showed resistance to Spodoptera litura with less than 10% leaf damage. Negative correlation existed between resistance to Spodoptera and days to 50 per cent flowering indicating late maturing nature of resistant genotypes. Eight resistant genotypes (ICG 862, ICG 928, ICG 76, ICG 2777, ICG 5016, ICG 12276, ICG 4412 and ICG 9905) under hypogaea botanical variety also had significantly higher pod yield. These diverse genotypes could serve as potential donors for incorporation of Spodoptera resistance in groundnut.

scholarly journals Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

2021 ◽  
Author(s):  
Anna L Tyler ◽  
Baha El Kassaby ◽  
Georgi Kolishovski ◽  
Jake Emerson ◽  
Ann E Wells ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Interaction ◽  
Recombinant Inbred Lines ◽  
Test Statistics ◽  
Test Statistic ◽  
Kinship Matrix ◽  
Main Effect ◽  
Main Effects ◽  
Interaction Test

Abstract It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Geographical disparities and determinants of childhood diarrheal illness in Ethiopia: Further Analysis of 2016 Ethiopian Demographic and Health Survey

2020 ◽  
Author(s):  
Asmamaw Atnafu ◽  
Malede Mequanent Sisay ◽  
Getu Debalkie Demissie ◽  
Zemenu Tadesse Tessema
Keyword(s):  
Health Survey ◽  
Mixed Model ◽  
Addis Ababa ◽  
Demographic And Health Survey ◽  
Sub Saharan Africa ◽  
Childhood Diarrhea ◽  
Mixed Effect ◽  
Under Five ◽  
Diarrheal Illness ◽  
Under Five Children

Abstract Background: Childhood diarrheal illness is the second leading cause of child mortality in Sub Saharan Africa, including Ethiopia. Studies hypothesized that there are regional variations. Thus, the study aimed to examine the spatial variations and to identify the determinants of childhood diarrhea in Ethiopia. Methods: Data from the 2016 Ethiopia Demographic and Health Survey (EDHS) was analyzed. This nationwide survey involved 10,337 children below 5 years old. The survey was carried out using a two-stage stratified sampling design. Moran’s I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Descriptive statistics followed by a mixed-effect logistic regression was used to identify the factors associated with the prevalence of diarrhea. Results: Overall, 11.87% of children were experienced childhood diarrheal illness. The study reveals high-risk areas were Southern and central Ethiopia, while eastern and west were indicated as low-risk regions. Younger children were more likely to suffer from childhood diarrhea than their older counterparts: age 6 to 12, 12 to 23, and 24 to 35 months were (AOR = 2.66, (95% CI 2.01, 3.52)), (AOR = 2.45, (95% CI 1.89, 3.17)), and (AOR = 1.53, (95% CI 1.17, 2.01)), respectively. Children living in Tigray (AOR= 1.69 (95% CI, 1.01, 2.83)), Amhara (AOR = 1.80, (95% CI, 1.06, 3.06), SNNPR (AOR = 2.04, 95% CI 1.22, 3.42), and Gambela (AOR = 2.05, (95% CI 1.22, 3.42)), faced greater risk than Addis Ababa city. The odds of getting diarrhea is decreased by 24% among households having ≥3 under-five children as compared to households having only one under-five child (AOR = 0.76 (95% CI: 0.61, 0.94)). The odds of children getting diarrheal illness among working mothers increase by 19% as compared to not working (AOR = 1.19 (95% CI 1.03, 1.38)). Conclusions: childhood diarrheal illness is highly prevalent among under-five children, particularly in SNNP, Gambella, Oromia, and Benishangul Gumuz regions. Capacity building programs with best experience sharing and better household environment may prove effective in reducing the incidence of childhood diarrhea in Ethiopia. Keywords: Spatial statistics, Ethiopia, under-five children, Diarrhea, Generalized Mixed Model

Meta-analysis of understory white spruce response to release from overstory aspen

2004 ◽  
Vol 80 (6) ◽  
pp. 694-704 ◽  
Author(s):  
Rongzhou Man ◽  
Ken J Greenway
Keyword(s):  
Growth Response ◽  
Mixed Model ◽  
Polynomial Regression ◽  
Meta Analysis ◽  
Basal Area ◽  
Tree Height ◽  
White Spruce ◽  
Response Ratio ◽  
Mixed Effect ◽  
Individual Tree

Meta-analysis was used to summarize the research results on the growth response of understory white spruce to release from overstory aspen from different studies available from published and unpublished sources. The data were screened for the suitability for meta-analysis. Treatment effect sizes were calculated using response ratio from mean cumulative increments of released and control trees since release in height, diameter, and volume and modeled using a polynomial mixed effect regression procedure. Predictor variables include linear, quadratic, and cubic components of three independent variables — initial tree height, number of years after release, and residual basal area at release — and their linear interactions. Models with a reasonable predictive power were developed for height, diameter, and volume response, but no significant model was identified for survival. The models developed in this study can be applied to predict the growth response of understory white spruce to release, based on the growth of unreleased control trees, initial tree height, residual basal area at release, and time since release. The individual tree prediction can be easily scaled up to stand level if residual tree density and distribution is known. Key words: meta-analysis, boreal mixedwood, mixed model, polynomial regression, response ratio, growth, survival

scholarly journals Validation of Genomic Selection Approach for Predicting Sheath Blight Resistance in Rice (Oryza sativa L.,)

2021 ◽  
pp. 158-167
Author(s):  
Mahantesh . ◽  
K. Ganesamurthy ◽  
Sayan Das ◽  
R. Saraswathi ◽  
C. Gopalakrishnan ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Oryza Sativa L ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Sheath Blight ◽  
Blight Resistance ◽  
Illumina Platform ◽  
Selection Approach ◽  
Sheath Blight Resistance

Rice sheath blight (ShB) is one of the most serious fungal diseases caused by Rhizoctonia solani, instigating significant yield losses in many rice-growing regions of the world. Intensive studies indicated that resistance for sheath blight is controlled possibly by polygenes. Because of complex inheritance, it’s very difficult to exploit and tap all the genomic regions conferring resistance using classical approaches of QTL mapping, it’s very important to have a different strategy to harness such resistance mechanism. One promising approach that can potentially provide accurate predictions of the resistance phenotypes is genomic selection (GS). The research was undertaken with an objective to validate genomic selection approach for predicting sheath blight resistance involving 1545 Recombinant inbred lines (RILs) derived from eleven crosses between resistant and susceptible parents (Jasmine 85XTN1, Jasmine 85XSwarnaSub1, Jasmine 85XII32B, Jasmine 85XIR54, TetepXTN1, TetepXSwarna Sub1, TetepXII32B, TetepXIR54, MTU 9992XTN1, MTU 9992XII32B and MTU 9992XIRBB4). Where, Jasmine 85, Tetep & MTU 9992 were resistant parents and TN1, Swarna Sub1, II32B, IR54 & IRBB4 were susceptible parents. During rainy season (2020) the F7 RILs were screened for their reaction to sheath blight in two hot spot locations. The genotyping was done with Illumina platform having 6564 SNP markers. Bayesian B approach was used to train the statistical model for calculation of marker effects and GEBVs. The prediction accuracy of training set (data fit analysis) obtained was 0.70 and random cross validation with different approaches, the prediction accuracy ranged from 0.67 to 0.74. The results are lucrative, all in all, high prediction accuracies observed in this study suggest genomic selection as a very promising breeding strategy for predicting sheath blight resistance in Rice.

scholarly journals Permutation Testing in the Presence of Polygenic Variation

2015 ◽  
Author(s):  
Mark Abney
Keyword(s):  
Quantitative Trait ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Permutation Test ◽  
Statistical Significance ◽  
Null Distribution ◽  
Polygenic Effect ◽  
Permutation Testing ◽  
Test Statistic ◽  
Omnibus Test

This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

scholarly journals Average IGF-1 Prediction for Once-Weekly Lonapegsomatropin in Children with Growth Hormone Deficiency

2021 ◽  
Author(s):  
Zhengning Lin ◽  
Aimee D Shu ◽  
Mark Bach ◽  
Bradley S Miller ◽  
Alan D Rogol
Keyword(s):  
Growth Hormone ◽  
Steady State ◽  
Growth Hormone Deficiency ◽  
Taylor Series ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Pharmacodynamic Model ◽  
Hormone Deficiency ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect

Abstract Context Serum IGF-1 levels are relatively constant in somatropin-treated children with growth hormone deficiency (GHD), and guide dose adjustments for clinical efficacy and long-term safety. IGF-1 levels following treatment with long-acting growth hormones such as lonapegsomatropin (TransCon hGH), a once-weekly somatropin prodrug, exhibit a characteristic profile over the dosing interval. Objective Develop a method to predict average IGF-1 in lonapegsomatropin-treated GHD children to interpret IGF-1 data based on a single sample obtained any time at steady state. Design A population nonlinear mixed-effect pharmacodynamic model for IGF-1 was developed based on two randomized, open-label trials of lonapegsomatropin in GHD children and used to develop a linear mixed model with Taylor series to fit simulated IGF-1 profiles of lonapegsomatropin-treated children. Setting and Patients 49,896 IGF-1 sample data simulated from 105 lonapegsomatropin-treated GHD children were utilized for the final prediction model. Intervention Dose range of lonapegsomatropin was 0.14–0.30 hGH mg/kg/week. Main Outcome Measure Weekly average IGF-1 was calculated using IGF-1 profiles simulated from the nonlinear pharmacodynamic model. Predicted average IGF-1 was obtained by linear mixed model with Taylor series. Results The nonlinear mixed-effect model provided satisfactory model fit. The linear mixed model with Taylor series fit simulated IGF-1 data well, with a relatively straightforward prediction formula. IGF-1 values sampled at ~4.5 days post-dose coincided with weekly average IGF-1 at steady state. Conclusion A formula to predict average IGF-1 from a single sample of IGF-1 at steady state was established to aid clinicians in interpreting IGF-1 levels in GHD children administered lonapegsomatropin.

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