Multiple interval mapping for gene expression QTL analysis

Qi, Z., Han, X., Hou, M., Xin, D., Wang, Z., Zhu, R., Hu, Z., Jiang, H., Li, C., Liu, C., Hu, G. and Chen, Q. 2014. QTL analysis of soybean oil content under 17 environments. Can. J. Plant Sci. 94: 245–261. Soybean oil content is a key trait driver of successful soybean quality. Due to its complex nature, less stable quantitative trait loci (QTL) are known. The goal of this study was to identify important and stable QTL affecting soybean oil content using recombination inbred lines (RILs) derived from a cross between Charleston and Dongnong594. The plant materials were planted in three environments across 9 yr in China. The genetic effects were then partitioned into additive main effects (A), epistatic main effects (AA) and their environment interaction effects (AE and AAE) by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Fifty-six QTL were identified on 15 of 20 soybean chromosomes excluding LG C1, D2, E, M and O by composite interval mapping and multiple interval mapping methods. Seven oil content QTL detected on LG A1, 1 on LG A2, 5 on LG B1, 4 on LG B2, 8 on LG C2, 11 on LG D1a, 2 on LG D1b, 4 on LG F, 5 on LG G, 2 on LG H, 1 on LG I, 1 on LG J, 1 on LG K, 2 on LG L and 2 on LG N. Eight QTL showed a good stability across 17 environments. The additive main-effect QTL contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible to improve soybean oil content by marker-assisted selection.

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Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects

BMC Genetics ◽

10.1186/1471-2156-14-103 ◽

2013 ◽

Vol 14 (1) ◽

Cited By ~ 5

Author(s):

Ye Cheng ◽

Satyanarayana Rachagani ◽

Angela Cánovas ◽

Mary Sue Mayes ◽

Richard G Tait ◽

...

Keyword(s):

Gene Expression ◽

Body Composition ◽

Qtl Mapping ◽

Interaction Effects ◽

Expression Qtl

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Joint Estimation of QTL Positions and Effects in Multiple-Interval Mapping for Count Trait

Communications in Computer and Information Science - Bio-Inspired Computing - Theories and Applications ◽

10.1007/978-3-662-45049-9_60 ◽

2014 ◽

pp. 374-378

Author(s):

Xiaona Sheng ◽

Weijun Ma ◽

Ying Zhou

Keyword(s):

Interval Mapping ◽

Joint Estimation ◽

Multiple Interval Mapping

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QTL Analysis and Gene Expression Studies in White Clover

Molecular Breeding of Forage and Turf ◽

10.1007/978-0-387-79143-2_15 ◽

2008 ◽

pp. 1-10

Author(s):

Michael T. Abberton ◽

Athole Marshall ◽

Rosemary P. Collins ◽

Charlotte Jones ◽

Matthew Lowe

Keyword(s):

Gene Expression ◽

White Clover ◽

Qtl Analysis ◽

Expression Studies ◽

Gene Expression Studies

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Non-parametric interval mapping in half-sib designs: use of midranks to account for ties

Genetics Research ◽

10.1017/s0016672303006256 ◽

2003 ◽

Vol 81 (3) ◽

pp. 221-228 ◽

Cited By ~ 3

Author(s):

P. TILQUIN ◽

I. VAN KEILEGOM ◽

W. COPPIETERS ◽

E. LE BOULENGÉ ◽

P. V. BARET

Keyword(s):

Disease Resistance ◽

Infectious Diseases ◽

Qtl Analysis ◽

Statistical Power ◽

Interval Mapping ◽

Phenotypic Variance ◽

Total Phenotypic Variance ◽

Non Parametric ◽

Normally Distributed

In QTL analysis of non-normally distributed phenotypes, non-parametric approaches have been proposed as an alternative to the use of parametric tests on mathematically transformed data. The non-parametric interval mapping test uses random ranking to deal with ties. Another approach is to assign to each tied individual the average of the tied ranks (midranks). This approach is implemented and compared to the random ranking approach in terms of statistical power and accuracy of the QTL position. Non-normal phenotypes such as bacteria counts showing high numbers of zeros are simulated (0–80% zeros). We show that, for low proportions of zeros, the power estimates are similar but, for high proportions of zeros, the midrank approach is superior to the random ranking approach. For example, with a QTL accounting for 8% of the total phenotypic variance, a gain from 8% to 11% of power can be obtained. Furthermore, the accuracy of the estimated QTL location is increased when using midranks. Therefore, if non-parametric interval mapping is chosen, the midrank approach should be preferred. This test might be especially relevant for the analysis of disease resistance phenotypes such as those observed when mapping QTLs for resistance to infectious diseases.

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