Regression Based Robust QTL Analysis for F2 Population

This Quantitative trait locus (QTL) analysis is a widely used statistical approach for the detection of important genes in the chromosomes. Maximum likelihood (ML) based interval mapping (IM) is one of the most popular approaches for QTL analysis. However, it is relatively complex and computationally slower than regression based IM. Haley-Knott (HK) and extended Haley-Knott (eHK) regression based IM save computation time and produce similar results as ML-IM. However, these approaches are not robust against phenotypic outliers. In this research, we have developed a robust regression based IM approach by maximizing beta-likelihood function for intercross (F2) population. The proposed method reduces to the HK-IM method when beta ? 0. The tuning parameter beta controls the performance of the proposed method. The simulation results show that the proposed method improves performance over the existing IM approaches in the case of data contaminations; otherwise, it shows almost the same results as the classical IM approaches.

Download Full-text

Robust linear regression based simple interval mapping for QTL analysis with backcross population

Journal of Bio-Science ◽

10.3329/jbs.v24i0.37489 ◽

2018 ◽

Vol 24 ◽

pp. 75-81

Author(s):

MJ Alam ◽

M Alamin ◽

MR Hossain ◽

SMS Islam ◽

MNH Mollah

Keyword(s):

Linear Regression ◽

Robust Regression ◽

Likelihood Function ◽

Interval Mapping ◽

Lod Score ◽

Backcross Population ◽

New Approach ◽

Simple Interval Mapping ◽

F2 Population ◽

Trait Locus

Simple interval mapping (SIM) is one of the most important techniques for the identification of quantitative trait locus (QTL). Most of the approaches of SIM are very sensitive to phenotypic outliers and produce misleading results. There is a robust approach of SIM only for F2 population. However, there is no robust SIM method for Backcross population. The objective was to develop a new approach of SIM with Backcross population which is robust against phenotypic outliers and performs almost the same as existing classical methods in absence of outliers. Maximum likelihood (ML) and linear regression (LR) based approaches of SIM are not robust against phenotypic outliers. In this research, we have developed a robust regression based SIM approach by maximizing β-likelihood function for Backcross population. The proposed method reduces to the LR-based SIM method when β = 0. To measure the performance of the proposed method in comparison of ML and LR based SIM with backcross population; we have generated phenotypic and genotypic data for Backcross population using simulation technique. LOD score profile plot shows that the highest peaks of LOD scores occur in the true QTL positions of the true chromosomes at true markers by all three methods for the uncontaminated dataset. However, in presence of outliers, only the proposed method gives the highest LOD score peaks at the true QTL positions on the true chromosomes. The simulation results showed that the proposed method improves performance over the existing SIM methods in presence of phenotypic contaminations.J. bio-sci. 24: 75-81, 2016

Download Full-text

Regression based fast multi-trait genome-wide QTL analysis

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020500444 ◽

2021 ◽

pp. 2050044

Author(s):

Md. Jahangir Alam ◽

Md. Ripter Hossain ◽

S. M. Shahinul Islam ◽

Md. Nurul Haque Mollah

Keyword(s):

Qtl Analysis ◽

Expectation Maximization ◽

Computation Time ◽

Interval Mapping ◽

Multivariate Normal ◽

New Approach ◽

Multiple Quantitative Trait Locus ◽

Genome Wide ◽

Complex Process ◽

Trait Locus

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance–covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Download Full-text

QTL Analysis of Domestication Syndrome in Zombi Pea (Vigna vexillata), an Underutilized Legume Crop

10.1101/353029 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sujinna Dachapak ◽

Norihiko Tomooka ◽

Prakit Somta ◽

Ken Naito ◽

Akito Kaga ◽

...

Keyword(s):

Linkage Map ◽

Seed Size ◽

Qtl Analysis ◽

Leaf Size ◽

Interval Mapping ◽

Linkage Groups ◽

Legume Crop ◽

Vigna Vexillata ◽

Length Width ◽

Trait Locus

AbstractZombi pea (Vigna vexillata (L.) A. Rich) is an underutilized crop belonging to the genus Vigna. Two domesticated forms of zombi pea are cultivated as crop plants; seed and tuber forms. The cultivated seed form is present in Africa, while the cultivated tuber form is present in a very limited part of Asia. Genetics of domestication have been investigated in most of cultivated Vigna crops by means of quantitative trait locus (QTL) mapping. In this study, we investigated genetics of domestication in zombi pea by QTL analysis using an F2 population of 139 plants derived from a cross between cultivated tuber form of V. vexillata (JP235863) and wild V. vexillata (AusTRCF66514). A linkage map with 11 linkage groups was constructed from this F2 population using 145 SSR, 117 RAD-seq and 2 morphological markers. Many highly segregation distorted markers were found on LGs 5, 6, 7, 8, 10 and 11. Most of the distorted markers were clustered together and all the markers on LG8 were highly distorted markers. Comparing this V. vexillata linkage map with a previous linkage map of V. vexillata and linkage maps of other four Vigna species demonstrated several macro translocations in V. vexillata. QTL analysis for 22 domestication-related traits was investigated by inclusive composite interval mapping in which 37 QTLs were identified for 18 traits; no QTL was detected for 4 traits. Number of QTLs detected in each trait ranged from 1 to 5 with an average of only 2.3. Tuber traits were controlled by five QTLs with similar effect locating on different linkage groups. Large-effect QTLs (PVE > 20%) were on LG4 (pod length), LG5 (leaf size and seed thickness), and LG7 (for seed-related traits). Comparison of domestication-related QTLs of the zombi pea with those of cowpea (Vigna unguiculata), azuki bean (Vigna angularis), mungbean (Vigna radiata) and rice bean (Vigna umbellata) revealed that there was conservation of some QTLs for seed size, pod size and leaf size between zombi pea and cowpea and that QTLs associated with seed size (weight, length, width and thickness) in each species were clustered on same linkage.

Download Full-text

A working likelihood approach for robust regression

Statistical Methods in Medical Research ◽

10.1177/0962280220936310 ◽

2020 ◽

Vol 29 (12) ◽

pp. 3641-3652

Author(s):

Liya Fu ◽

You-Gan Wang ◽

Fengjing Cai

Keyword(s):

Parameter Estimation ◽

Robust Regression ◽

Likelihood Function ◽

Regularization Parameter ◽

Tuning Parameter ◽

Estimation Efficiency ◽

A Value ◽

Regression Parameters ◽

Heavy Tailed ◽

Heteroscedastic Variance

Robust approach is often desirable in presence of outliers for more efficient parameter estimation. However, the choice of the regularization parameter value impacts the efficiency of the parameter estimators. To maximize the estimation efficiency, we construct a likelihood function for simultaneously estimating the regression parameters and the tuning parameter. The “working” likelihood function is deemed as a vehicle for efficient regression parameter estimation, because we do not assume the data are generated from this likelihood function. The proposed method can effectively find a value of the regularization parameter based on the extent of contamination in the data. We carry out extensive simulation studies in a variety of cases to investigate the performance of the proposed method. The simulation results show that the efficiency can be enhanced as much as 40% when the data follow a heavy-tailed distribution, and reaches as high as 468% for the heteroscedastic variance cases compared to the traditional Huber’s method with a fixed regularization parameter. For illustration, we also analyzed two datasets: one from a diabetics study and the other from a mortality study.

Download Full-text

Abstract 1388: Hdlq14 Gene, A New Gene Regulating HDL Levels

Circulation ◽

10.1161/circ.116.suppl_16.ii_285-a ◽

2007 ◽

Vol 116 (suppl_16) ◽

Author(s):

Zhiguang Su ◽

Allison Cox ◽

Yuan Shen ◽

Ioannis Stylianou ◽

Beverly Paigen

Keyword(s):

Qtl Analysis ◽

Complex Traits ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Density Lipoprotein ◽

Mouse Strains ◽

F2 Population ◽

New Genes ◽

Trait Locus ◽

Significant Expression

Background . The discovery of new genes responsible for regulation of high-density lipoprotein cholesterol (HDL) has great clinical relevance since increases in HDL can reduce cardiovascular disease risk. Quantitative trait locus (QTL) analysis is a means of finding novel genes that regulate complex traits, such as atherosclerosis and HDL. Hdlq14 and Hdlq15 , two closely linked QTLs for HDL on mouse Chr 1, have been detected by using an intercross between strains C57BL/6 (B6) and 129S1/SvImJ (129). Apoa2 is the gene for Hdlq15 locus, but the gene for Hdlq14 is unknown. Methods: To confirm the Hdlq14 and identify the candidate gene, we performed QTL analysis in a F2 population generated from strains NZB and NZW, which are same at Apoa2 to avoid its strong effect on the nearby QTL. Hdlq14 was further narrowed by several strategies including combining crosses, comparative genomics, and haplotype analysis. The reduced lists of candidate genes were evaluated by their expression or sequence differences between the strains that caused the Hdlq14 . Finally, other HDL crosses, including NZOxNON, B6xC3H, and Pera x D2, were examined to point out the QTL gene. The relationship between the polymorphism at the Hdlq14 gene and HDL was analyzed in 43 genetically diverse mouse strains. Results: Hdlq14 was proved in cross NZBxNZW and the critical interval was reduced from 45 Mb harboring 271 genes to 1.65 Mb containing 15 genes by using bioinformatics tools. Six of these 15 genes have polymorphisms that changed an amino acid; and two genes were found have a significant expression difference between strains B6 and 129. The Hdlq14 gene was further pointed out using HDL QTL identified in crosses including NZOxNON, B6xC3H, and PeraxDBA. In 43 genetically diverse mouse strains, we found that strains with one allele of the Hdlq14 had significantly higher plasma HDL levels than those with the other variant. Conclusions: The Hdlq14 was identified as a new HDL-regulating gene.

Download Full-text

Integration and Modularity of Quantitative Trait Locus Effects on Geometric Shape in the Mouse Mandible

Genetics ◽

10.1093/genetics/166.4.1909 ◽

2004 ◽

Vol 166 (4) ◽

pp. 1909-1921

Author(s):

Christian Peter Klingenberg ◽

Larry J Leamy ◽

James M Cheverud

Keyword(s):

Quantitative Trait ◽

Parametric Bootstrap ◽

Interval Mapping ◽

Procrustes Analysis ◽

Bootstrap Test ◽

Mandible Shape ◽

Trait Locus ◽

F2 Generation ◽

Analysis Interval ◽

Qtl Effects

Abstract The mouse mandible has long served as a model system for complex morphological structures. Here we use new methodology based on geometric morphometrics to test the hypothesis that the mandible consists of two main modules, the alveolar region and the ascending ramus, and that this modularity is reflected in the effects of quantitative trait loci (QTL). The shape of each mandible was analyzed by the positions of 16 morphological landmarks and these data were analyzed using Procrustes analysis. Interval mapping in the F2 generation from intercrosses of the LG/J and SM/J strains revealed 33 QTL affecting mandible shape. The QTL effects corresponded to a variety of shape changes, but ordination or a parametric bootstrap test of clustering did not reveal any distinct groups of QTL that would affect primarily one module or the other. The correlations of landmark positions between the two modules tended to be lower than the correlations between arbitrary subsets of landmarks, indicating that the modules were relatively independent of each other and confirming the hypothesized location of the boundary between them. While these results are in agreement with the hypothesis of modularity, they also underscore that modularity is a question of the relative degrees to which QTL contribute to different traits, rather than a question of discrete sets of QTL contributing to discrete sets of traits.

Download Full-text

Genetic Architecture of a Morphological Shape Difference Between Two Drosophila Species

Genetics ◽

10.1093/genetics/154.1.299 ◽

2000 ◽

Vol 154 (1) ◽

pp. 299-310 ◽

Cited By ~ 11

Author(s):

Zhao-Bang Zeng ◽

Jianjun Liu ◽

Lynn F Stam ◽

Chen-Hung Kao ◽

John M Mercer ◽

...

Keyword(s):

Interval Mapping ◽

Additive Effect ◽

Parameter Estimates ◽

Effect Estimate ◽

Interspecific Difference ◽

Posterior Lobe ◽

Complete Dominance ◽

Backcross Populations ◽

History Of ◽

Trait Locus

AbstractThe size and shape of the posterior lobe of the male genital arch differs dramatically between Drosophila simulans and D. mauritiana. This difference can be quantified with a morphometric descriptor (PC1) based on elliptical Fourier and principal components analyses. The genetic basis of the interspecific difference in PC1 was investigated by the application of quantitative trait locus (QTL) mapping procedures to segregating backcross populations. The parental difference (35 environmental standard deviations) and the heritability of PC1 in backcross populations (>90%) are both very large. The use of multiple interval mapping gives evidence for 19 different QTL. The greatest additive effect estimate accounts for 11.4% of the parental difference but could represent multiple closely linked QTL. Dominance parameter estimates vary among loci from essentially no dominance to complete dominance, and mauritiana alleles tend to be dominant over simulans alleles. Epistasis appears to be relatively unimportant as a source of variation. All but one of the additive effect estimates have the same sign, which means that one species has nearly all plus alleles and the other nearly all minus alleles. This result is unexpected under many evolutionary scenarios and suggests a history of strong directional selection acting on the posterior lobe.

Download Full-text

Genetic Mechanisms Underlying Apimaysin and Maysin Synthesis and Corn Earworm Antibiosis in Maize (Zea mays L.)

Genetics ◽

10.1093/genetics/149.4.1997 ◽

1998 ◽

Vol 149 (4) ◽

pp. 1997-2006

Author(s):

E A Lee ◽

P F Byrne ◽

M D McMullen ◽

M E Snook ◽

B R Wiseman ◽

...

Keyword(s):

Helicoverpa Zea ◽

Larval Growth ◽

Corn Earworm ◽

Phenotypic Variance ◽

Significant Qtls ◽

F2 Population ◽

Genetic Mechanisms ◽

Trait Locus ◽

Maize Silks ◽

Related Compounds

Abstract C-glycosyl flavones in maize silks confer resistance (i.e., antibiosis) to corn earworm (Helicoverpa zea [Boddie]) larvae and are distinguished by their B-ring substitutions, with maysin and apimaysin being the di- and monohydroxy B-ring forms, respectively. Herein, we examine the genetic mechanisms underlying the synthesis of maysin and apimaysin and the corresponding effects on corn earworm larval growth. Using an F2 population, we found a quantitative trait locus (QTL), rem1, which accounted for 55.3% of the phenotypic variance for maysin, and a QTL, pr1, which explained 64.7% of the phenotypic variance for apimaysin. The maysin QTL did not affect apimaysin synthesis, and the apimaysin QTL did not affect maysin synthesis, suggesting that the synthesis of these closely related compounds occurs independently. The two QTLs, rem1 and pr1, were involved in a significant epistatic interaction for total flavones, suggesting that a ceiling exists governing the total possible amount of C-glycosyl flavone. The maysin and apimaysin QTLs were significant QTLs for corn earworm antibiosis, accounting for 14.1% (rem1) and 14.7% (pr1) of the phenotypic variation. An additional QTL, represented by umc85 on the short arm of chromosome 6, affected antibiosis (R2 = 15.2%), but did not affect the synthesis of the C-glycosyl flavones.

Download Full-text

The Application of Bayesian Statistics in Calculating Project Buffer

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1018 ◽

2011 ◽

Vol 58-60 ◽

pp. 1018-1024

Author(s):

Feng Ye ◽

Gui Chen Xu ◽

Di Kang Zhu

Keyword(s):

Bayesian Statistics ◽

Statistical Approach ◽

Implementation Process ◽

Current Method ◽

Practical Application ◽

Great Flexibility ◽

Crystal Ball ◽

Simulation Results ◽

The Stability

This paper reviews several current methods of calculating buffer on the basis of pointing out each merits and pitfalls and then introduces Bayesian statistical approach to CCS / BM domain to calculate the size of the project buffer, to overcome that the current method of the buffer calculation is too subjective and the defect on lacking of practical application. In Crystal Ball, we compare the simulation results of implementation process on the benchmark of C&PM, RESM and SM. The results show that the buffer using this method can ensure the stability of the project’s completion probability, and this method has great flexibility.

Download Full-text

QTL analysis of self-selected macronutrient diet intake: fat, carbohydrate, and total kilocalories

Physiological Genomics ◽

10.1152/physiolgenomics.00037.2002 ◽

2002 ◽

Vol 11 (3) ◽

pp. 205-217 ◽

Cited By ~ 47

Author(s):

Brenda K. Smith Richards ◽

Brenda N. Belton ◽

Angela C. Poole ◽

James J. Mancuso ◽

Gary A. Churchill ◽

...

Keyword(s):

Body Weight ◽

Qtl Analysis ◽

Genetic Basis ◽

Body Weight Gain ◽

Macronutrient Intake ◽

Naturally Occurring ◽

Trait Locus ◽

Diet Intake ◽

Insight Into ◽

Protein Quantitative Trait Locus

The present study investigated the inheritance of dietary fat, carbohydrate, and kilocalorie intake traits in an F2 population derived from an intercross between C57BL/6J (fat-preferring) and CAST/EiJ (carbohydrate-preferring) mice. Mice were phenotyped for self-selected food intake in a paradigm which provided for 10 days a choice between two macronutrient diets containing 78/22% of energy as a composite of either fat/protein or carbohydrate/protein. Quantitative trait locus (QTL) analysis identified six significant loci for macronutrient intake: three for fat intake on chromosomes (Chrs) 8 ( Mnif1), 18 ( Mnif2), and X ( Mnif3), and three for carbohydrate intake on Chrs 17 ( Mnic1), 6 ( Mnic2), and X ( Mnic3). An absence of interactions among these QTL suggests the existence of separate mechanisms controlling the intake of fat and carbohydrate. Two significant QTL for cumulative kilocalorie intake, adjusted for baseline body weight, were found on Chrs 17 ( Kcal1) and 18 ( Kcal2). Without body weight adjustment, another significant kcal locus appeared on distal Chr 2 ( Kcal3). These macronutrient and kilocalorie QTL, with the exception of loci on Chrs 8 and X, encompassed chromosomal regions influencing body weight gain and adiposity in this F2 population. These results provide new insight into the genetic basis of naturally occurring variation in nutrient intake phenotypes.

Download Full-text