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Structural mapping: how to study the genetic architecture of a phenotypic trait through its formation mechanism.

Authors :
Tong, Chunfa
Shen, Lianying
Lv, Yafei
Wang, Zhong
Wang, Xiaoling
Feng, Sisi
Li, Xin
Sui, Yihan
Pang, Xiaoming
Wu, Rongling
Source :
Briefings in Bioinformatics. Jan2014, Vol. 15 Issue 1, p43-53. 11p.
Publication Year :
2014

Abstract

Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
15
Issue :
1
Database :
Academic Search Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
93914762
Full Text :
https://doi.org/10.1093/bib/bbs067