DOI: 10.3724/SP.J.1006.2009.00239

Acta Agronomica Sinica (作物学报) 2009/35:2 PP.239-245

Inclusive Composite Interval Mapping of Quantitative Trait Genes

Rapid increase in the availability of fine-scale genetic marker maps has led to the intensive use of QTL mapping in the genetic study of quantitative traits. Composite interval mapping (CIM) is one of the most commonly used methods for QTL mapping with populations derived from biparental crosses. However, the algorithm used in CIM cannot completely ensure that the effect of QTL at current testing interval is not absorbed by the background marker variables, and may result in biased estimation of QTL effect. We proposed a statistical method for QTL mapping, which was called inclusive composite interval mapping (ICIM). Two steps were included in ICIM. In the first step, stepwise regression was applied to identify the most significant regression variables. In the second step, a one-dimensional scanning or interval mapping was conducted for detecting additive (and dominance) QTL and a two-dimensional scanning was conducted for detecting digenic epistasis. ICIM provides intuitive statistics for testing additive, dominance and epistasis, and can be used for most experimental populations derived from two inbred parental lines. The EM algorithm used in ICIM has a fast convergence speed and is therefore less computing intensive. ICIM retains all advantages of CIM over interval mapping, and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. A doubled haploid (DH) population in barley was used to demonstrate the application of ICIM in mapping additive QTL and additive by additive interacting QTL.

Key words:Quantitative trait,QTL mapping,Inclusive composite interval mapping,Additive and dominance effects,Epistatic interaction

ReleaseDate:2014-07-22 16:12:30

[1] Lynch M, Walsh B. Genetic and Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates, 1998

[2] Zhai H-Q(翟虎渠), Wang J-K(王建康). Applied Quantitative Genetics (应用数量遗传). Beijing: China Agricultural Scientech Press, 2007 (in Chinese)

[3] Lander E S, Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps.Genetics,1989,121:185-199

[4] Broman K W, Speed T P. A model selection approach for the identification of quantitative trait loci in experimental crosses.J Roy Statist Soc B,2002,64:641-656

[5] Carlborg Ö, Kerje S, Schütz K, Jacobsson L, Jensen P, Andersson L. A global search reveals epistatic interaction between QTL for early growth in the chicken.Genome Res,2003,13:413-421

[6] Doerge R W. Mapping and analysis of quantitative trait loci in experiment populations.Nat Rev Genet,2002,3:43-52

[7] Feenstra B, Skovgaard I M, Broman K W. Mapping quantitative trait loci by an extension of the Haley-Knott regression method using estimating equations.Genetics,2006,173:2269-2282

[8] Sen S, Churchill G A. A statistical framework for quantitative trait mapping.Genetics,2001,159:371-387

[9] Kao C H, Zeng Z B, Teasdale R D. Multiple interval mapping for quantitative trait loci.Genetics,1999,152:1203-1206

[10] Zhang Y, Xu S. A penalized maximum likelihood method for estimating epistatic effects of QTL.Heredity,2005,95:96-104

[11] Satagopan J M, Yandell B S, Newton M A, Osborn T C. A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo.Genetics,1996,144:805-816

[12] Wang H, Zhang Y, Li X, Masinde G, Mohan S, Baylink D, Xu S. Bayesian shrinkage estimation of quantitative trait loci parameters.Genetics,2005,170:465-480

[13] Xu S, Jia Z. Genome-wide analysis of epistatic effects for quantitive traits in barley.Genetics,2007,175:1955-1963

[14] Frary A N, Nesbitt T C, Frary A M, Grandillo S, Knaap E V D, Cong B, Liu J P, Meller J, Elber R, Alpert K B, Tanksley S D.fw2.2: A quantitative trait locus key to the evolution of tomato fruit size.Science,2000,289:85-88

[15] Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice.Nat Genet,2008,40:761-767

[16] Wan X, Weng J, Zhai H, Wang J, Liu X, Guo T, Su N, Wan J. QTL analysis for rice grain width and fine mapping of an identified QTL allele gw-5 in a recombination hotspot region on chromosome 5.Genetics,2008,179:2239-2252

[17] Wang J, Wan X, Li H, Pfeiffer W, Crouch J, Wan J. Application of identified QTL-marker associations in rice quality improvement through a design breeding approach.Theor Appl Genet,2007,115:87-100

[18] Wang J, Wan X, Crossa J, Crouch J, Weng J, Zhai H, Wan J. QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines.Genet Res,2006,88:93-104

[19] Haley C S, Knott S A. A simple regression method for mapping quantitative loci in line crosses using flanking markers.Heredity,1992,69:315-324

[20] Zeng Z B. Precision mapping of quantitative trait loci.Genetics,1994,136:1457-1468

[21] Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping.Genetics,2007,175:361-374

[22] Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations.Theor Appl Genet,2008,116:243-260

[23] Zhang L, Li H, Li Z, Wang J. Interactions between markers can be caused by the dominance effect of QTL.Genetics,2008,180:1177-1190

[24] Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm.J Royal Stat Soc B,1977,39:1-38

[25] Tinker N A, Mather D E, Rossnagel B G, Kasha K J, Kleinhofs A, Hayes P M, Falk D E, Ferguson T, Shugar L P, Legge W G, Irvine R B, Choo T M, Briggs K G, Ullrich S E, Franckowiak J D, Blake T K, Graf R J, Dofing S M, Saghai-Maroof M A, Scoles G J, Hoffman D, Dahleen L S, Kilian A, Chen F, Biyashev R M, Kudrna D A, Steffenson B J. Regions of the genome that affect agronomic performance in two-row barley.Crop Sci,1996,36:1053-1062