Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL...
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Autor*in:	Tang, ZaiXiang [verfasserIn] Xiao, Jing [verfasserIn] Hu, WenMing [verfasserIn] Yu, Bo [verfasserIn] Xu, ChenWu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	complex trait chromosome segment substitution line (CSSL) epistasis stepwise regression Bayesian statistics

Übergeordnetes Werk:	Enthalten in: Chinese science bulletin - Beijing, China : Chinese Acad. of Sciences, 1997, 57(2012), 21 vom: 30. Apr., Seite 2666-2674
Übergeordnetes Werk:	volume:57 ; year:2012 ; number:21 ; day:30 ; month:04 ; pages:2666-2674

Links:	Volltext

DOI / URN:	10.1007/s11434-012-5195-y

Katalog-ID:	SPR01943698X

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245	1	0	\|a Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
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520			\|a Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
650		4	\|a complex trait \|7 (dpeaa)DE-He213
650		4	\|a chromosome segment substitution line (CSSL) \|7 (dpeaa)DE-He213
650		4	\|a epistasis \|7 (dpeaa)DE-He213
650		4	\|a stepwise regression \|7 (dpeaa)DE-He213
650		4	\|a Bayesian statistics \|7 (dpeaa)DE-He213
700	1		\|a Xiao, Jing \|e verfasserin \|4 aut
700	1		\|a Hu, WenMing \|e verfasserin \|4 aut
700	1		\|a Yu, Bo \|e verfasserin \|4 aut
700	1		\|a Xu, ChenWu \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Chinese science bulletin \|d Beijing, China : Chinese Acad. of Sciences, 1997 \|g 57(2012), 21 vom: 30. Apr., Seite 2666-2674 \|w (DE-627)341897809 \|w (DE-600)2069521-4 \|x 1861-9541 \|7 nnns
773	1	8	\|g volume:57 \|g year:2012 \|g number:21 \|g day:30 \|g month:04 \|g pages:2666-2674
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912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2005
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allfields	10.1007/s11434-012-5195-y doi (DE-627)SPR01943698X (SPR)s11434-012-5195-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Tang, ZaiXiang verfasserin aut Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects. complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Xiao, Jing verfasserin aut Hu, WenMing verfasserin aut Yu, Bo verfasserin aut Xu, ChenWu verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 57(2012), 21 vom: 30. Apr., Seite 2666-2674 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674 https://dx.doi.org/10.1007/s11434-012-5195-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 57 2012 21 30 04 2666-2674
spelling	10.1007/s11434-012-5195-y doi (DE-627)SPR01943698X (SPR)s11434-012-5195-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Tang, ZaiXiang verfasserin aut Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects. complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Xiao, Jing verfasserin aut Hu, WenMing verfasserin aut Yu, Bo verfasserin aut Xu, ChenWu verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 57(2012), 21 vom: 30. Apr., Seite 2666-2674 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674 https://dx.doi.org/10.1007/s11434-012-5195-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 57 2012 21 30 04 2666-2674
allfields_unstemmed	10.1007/s11434-012-5195-y doi (DE-627)SPR01943698X (SPR)s11434-012-5195-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Tang, ZaiXiang verfasserin aut Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects. complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Xiao, Jing verfasserin aut Hu, WenMing verfasserin aut Yu, Bo verfasserin aut Xu, ChenWu verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 57(2012), 21 vom: 30. Apr., Seite 2666-2674 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674 https://dx.doi.org/10.1007/s11434-012-5195-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 57 2012 21 30 04 2666-2674
allfieldsGer	10.1007/s11434-012-5195-y doi (DE-627)SPR01943698X (SPR)s11434-012-5195-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Tang, ZaiXiang verfasserin aut Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects. complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Xiao, Jing verfasserin aut Hu, WenMing verfasserin aut Yu, Bo verfasserin aut Xu, ChenWu verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 57(2012), 21 vom: 30. Apr., Seite 2666-2674 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674 https://dx.doi.org/10.1007/s11434-012-5195-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 57 2012 21 30 04 2666-2674
allfieldsSound	10.1007/s11434-012-5195-y doi (DE-627)SPR01943698X (SPR)s11434-012-5195-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Tang, ZaiXiang verfasserin aut Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects. complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213 Xiao, Jing verfasserin aut Hu, WenMing verfasserin aut Yu, Bo verfasserin aut Xu, ChenWu verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 57(2012), 21 vom: 30. Apr., Seite 2666-2674 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674 https://dx.doi.org/10.1007/s11434-012-5195-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 57 2012 21 30 04 2666-2674
language	English
source	Enthalten in Chinese science bulletin 57(2012), 21 vom: 30. Apr., Seite 2666-2674 volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674
sourceStr	Enthalten in Chinese science bulletin 57(2012), 21 vom: 30. Apr., Seite 2666-2674 volume:57 year:2012 number:21 day:30 month:04 pages:2666-2674
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	complex trait chromosome segment substitution line (CSSL) epistasis stepwise regression Bayesian statistics
dewey-raw	500
isfreeaccess_bool	true
container_title	Chinese science bulletin
authorswithroles_txt_mv	Tang, ZaiXiang @@aut@@ Xiao, Jing @@aut@@ Hu, WenMing @@aut@@ Yu, Bo @@aut@@ Xu, ChenWu @@aut@@
publishDateDaySort_date	2012-04-30T00:00:00Z
hierarchy_top_id	341897809
dewey-sort	3500
id	SPR01943698X
language_de	englisch
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author	Tang, ZaiXiang
spellingShingle	Tang, ZaiXiang ddc 500 bkl 30.00 misc complex trait misc chromosome segment substitution line (CSSL) misc epistasis misc stepwise regression misc Bayesian statistics Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
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topic_title	500 ASE 30.00 bkl Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines complex trait (dpeaa)DE-He213 chromosome segment substitution line (CSSL) (dpeaa)DE-He213 epistasis (dpeaa)DE-He213 stepwise regression (dpeaa)DE-He213 Bayesian statistics (dpeaa)DE-He213
topic	ddc 500 bkl 30.00 misc complex trait misc chromosome segment substitution line (CSSL) misc epistasis misc stepwise regression misc Bayesian statistics
topic_unstemmed	ddc 500 bkl 30.00 misc complex trait misc chromosome segment substitution line (CSSL) misc epistasis misc stepwise regression misc Bayesian statistics
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title	Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
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title_full	Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
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author_browse	Tang, ZaiXiang Xiao, Jing Hu, WenMing Yu, Bo Xu, ChenWu
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title_sort	bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
title_auth	Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
abstract	Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
abstractGer	Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
abstract_unstemmed	Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
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title_short	Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines
url	https://dx.doi.org/10.1007/s11434-012-5195-y
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author2	Xiao, Jing Hu, WenMing Yu, Bo Xu, ChenWu
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doi_str	10.1007/s11434-012-5195-y
up_date	2024-07-04T01:36:34.817Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR01943698X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111065927.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11434-012-5195-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR01943698X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11434-012-5195-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">500</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">30.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, ZaiXiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. 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Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

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