ArticlePloS one2018
β-composite Interval Mapping for robust QTL analysis.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Robust regression based genome-wide multi-trait QTL analysis.Molecular genetics and genomics : MGG · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.