The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hrishikesh Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study. Copyright © 2006 John Wiley & Sons, Ltd.
Pulak Ghosh - One of the best experts on this subject based on the ideXlab platform.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study. Copyright © 2006 John Wiley & Sons, Ltd.
Robin C Buell - One of the best experts on this subject based on the ideXlab platform.
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multiple qtl mapping in autopolyploids a random Effect Model approach with application in a hexaploid sweetpotato full sib population
Genetics, 2020Co-Authors: Guilherme Da Silva Pereira, Dorcus C Gemenet, Marcelo Mollinari, Bode A Olukolu, Joshua C Wood, Federico Diaz, Veronica Mosquera, Wolfgang J Gruneberg, Awais Khan, Robin C BuellAbstract:In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. ( 2 n = 6 x = 90 ) , is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-Effect Models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-Effect Model approach to map multiple QTL based on score statistics in a sweetpotato biparental population (‘Beauregard’ × ‘Tanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-Effect Models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele Effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.
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multiple qtl mapping in autopolyploids a random Effect Model approach with application in a hexaploid sweetpotato full sib population
bioRxiv, 2019Co-Authors: Guilherme Da Silva Pereira, Dorcus C Gemenet, Marcelo Mollinari, Bode A Olukolu, Joshua C Wood, Federico Diaz, Veronica Mosquera, Wolfgang J Gruneberg, Awais Khan, Robin C BuellAbstract:ABSTRACT In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-Effect Models can only fit a single QTL and are generally hard to interpret. Here we report the use of a random-Effect Model approach to map multiple QTL based on score statistics in a sweetpotato bi-parental population (‘Beauregard’ × ‘Tanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly predicted means were obtained using mixed-Effect Models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every cM position. Multiple interval mapping was performed using our R package QTLPOLY and detected a total of 41 QTL, ranging from one to ten QTL per trait. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits and provided basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions allowed us to characterize additive allele Effects as well as to compute QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.
Marcia D Branco - One of the best experts on this subject based on the ideXlab platform.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study.
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bivariate random Effect Model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random Effects Models are commonly used to Model correlated data. Mostly in the longitudinal data setting we assume that the random Effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed Model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential Models and illustrate the procedure using a real data set from HIV study. Copyright © 2006 John Wiley & Sons, Ltd.
Guilherme Da Silva Pereira - One of the best experts on this subject based on the ideXlab platform.
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multiple qtl mapping in autopolyploids a random Effect Model approach with application in a hexaploid sweetpotato full sib population
Genetics, 2020Co-Authors: Guilherme Da Silva Pereira, Dorcus C Gemenet, Marcelo Mollinari, Bode A Olukolu, Joshua C Wood, Federico Diaz, Veronica Mosquera, Wolfgang J Gruneberg, Awais Khan, Robin C BuellAbstract:In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. ( 2 n = 6 x = 90 ) , is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-Effect Models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-Effect Model approach to map multiple QTL based on score statistics in a sweetpotato biparental population (‘Beauregard’ × ‘Tanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-Effect Models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele Effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.
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multiple qtl mapping in autopolyploids a random Effect Model approach with application in a hexaploid sweetpotato full sib population
bioRxiv, 2019Co-Authors: Guilherme Da Silva Pereira, Dorcus C Gemenet, Marcelo Mollinari, Bode A Olukolu, Joshua C Wood, Federico Diaz, Veronica Mosquera, Wolfgang J Gruneberg, Awais Khan, Robin C BuellAbstract:ABSTRACT In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-Effect Models can only fit a single QTL and are generally hard to interpret. Here we report the use of a random-Effect Model approach to map multiple QTL based on score statistics in a sweetpotato bi-parental population (‘Beauregard’ × ‘Tanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly predicted means were obtained using mixed-Effect Models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every cM position. Multiple interval mapping was performed using our R package QTLPOLY and detected a total of 41 QTL, ranging from one to ten QTL per trait. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits and provided basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions allowed us to characterize additive allele Effects as well as to compute QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.