Abstract
Interval mapping approaches have been playing significant role for quantitative trait locus (QTL) mapping to discover genetic architecture of diseases or traits with molecular markers. Composite interval mapping (CIM) is one of the superior approaches of the interval mapping for discovering both linked and unlinked putative QTL positions. However, estimators of this approach are not robust against phenotypic outliers. As a result, it fails to detect true QTL positions in presence of outliers. In this study, we investigated the performance of β-Composite Interval Mapping (BetaCIM) for detecting both linked and unlinked important QTLs positions from the robustness points of views. Performance of this approach depends on the value of tuning parameter β. It reduces to the classical CIM approach for β →0. We described and formulated the cross-validation procedure for selecting trait specific optimum value of β. It was observed that the optimum value of β depends on both amount of contaminated observations and their scatteredness. BetaCIM approach discover similar QTL positions as classical IM/CIM in absence of phenotypic outliers, but gives better results in presence of phenotypic outliers in terms of detecting true QTLs and effects estimation. We formulated the generalized forms of robust QTL analysis and developed an R-package named “BetaCIM” by implementing this robust approach. Left and right kidney weight data sets of mouse intercross population (129 S1/SvlmJ × A/J) were analyzed by using BetaCIM, CIM, and IM approaches. For right kidney weight (RKW) CIM and BetaCIM provided similar LOD score profile, and both approaches identified 3 QTL positions. IM approach also identified 3 QTL positions. For left kidney weight (LKW), there was evidence of one outlying observation; and in this case the BetaCIM approach identified 2 QTL positions. However, none of the QTLs were significant by CIM and IM approaches at 5% level of significance. Gene expression ontology (GEO) search showed that the candidate genes (Otof and A330033J07Rik) of the identified QTLs for LKW were expressed in kidney. Both simulation and real data analysis results showed that BetaCIM approach improves the performance over the existing methods in presence of phenotypic outliers. Otherwise, it keeps almost equal performance.
Highlights
quantitative trait locus (QTL) mapping approaches have been used successfully to discover the genetic variants by using experimental cross populations [1, 2]
Simulations studies were conducted for Backcross (BC) and Intercross (F2) populations based on the assumptions of multiple linked and unlinked QTLs, and in presence and absence of phenotypic outliers
LOD scores were calculated for classical interval mapping and composite interval mapping approaches and β-LOD scores were calculated for BetaCIM approach that is equivalent to classical LOD scores for β
Summary
QTL mapping approaches have been used successfully to discover the genetic variants by using experimental cross populations [1, 2]. Composite interval mapping approach use the estimators derived from classical likelihood function to estimate genetic parameters, which might produce false positive or reduce detecting power of true loci in presence of phenotypic outliers. Instead of classical estimators of genetic parameters, β-Composite Interval Mapping uses the robust estimators derived from beta-likelihood function for robustly estimating the QTL positions in presence and absence of phenotypic outliers.
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