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Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.

ABSTRACT: BACKGROUND: Evidence suggests that common complex diseases may be partially due to SNP-SNP interactions, but such detection is yet to be fully established in a high-dimensional small-sample (small-n-large-p) study. A number of penalized regression techniques are gaining popularity within the statistical community, and are now being applied to detect interactions. These techniques tend to be over-fitting, and are prone to false positives. The recently developed stability least absolute shrinkage and selection operator (SLASSO) has been used to control family-wise error rate, but often at the expense of power (and thus false negative results). RESULTS: Here, we propose an alternative stability selection procedure known as stability smoothly clipped absolute deviation (SSCAD). Briefly, this method applies a smoothly clipped absolute deviation (SCAD) algorithm to multiple sub-samples, and then identifies cluster ensemble of interactions across the sub-samples. The proposed method was compared with SLASSO and two kinds of traditional penalized methods by intensive simulation. The simulation revealed higher power and lower false discovery rate (FDR) with SSCAD. An analysis using the new method on the previously published GWAS of lung cancer confirmed all significant interactions identified with SLASSO, and identified two additional interactions not reported with SLASSO analysis. CONCLUSIONS: Based on the results obtained in this study, SSCAD presents to be a powerful procedure for the detection of SNP-SNP interactions in large-scale genomic data.

SUBMITTER: Gou J

PROVIDER: S-EPMC3984751 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.

Gou Jianwei J Zhao Yang Y Wei Yongyue Y Wu Chen C Zhang Ruyang R Qiu Yongyong Y Zeng Ping P Tan Wen W Yu Dianke D Wu Tangchun T Hu Zhibin Z Lin Dongxin D Shen Hongbing H Chen Feng F

BMC bioinformatics 20140301

<h4>Background</h4>Evidence suggests that common complex diseases may be partially due to SNP-SNP interactions, but such detection is yet to be fully established in a high-dimensional small-sample (small-n-large-p) study. A number of penalized regression techniques are gaining popularity within the statistical community, and are now being applied to detect interactions. These techniques tend to be over-fitting, and are prone to false positives. The recently developed stability least absolute shr ...[more]

PMID: 24580776

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Project description:Objective: 1) To develop a machine learning approach for detecting stroke cases and subtypes from hospitalization data, 2) to assess algorithm performance and predictors on real-world data collected by a large-scale epidemiology study in the US; and 3) to identify directions for future development of high-precision stroke phenotypic signatures.Materials and methods: We utilized 8,131 hospitalization events (ICD-9 codes 430-438) collected from the Greater Cincinnati/Northern Kentucky Stroke Study in 2005 and 2010. Detailed information from patients' medical records was abstracted for each event by trained research nurses. By analyzing the broad list of demographic and clinical variables, the machine learning algorithms predicted whether an event was a stroke case and, if so, the stroke subtype. The performance was validated on gold-standard labels adjudicated by stroke physicians, and results were compared with stroke classifications based on ICD-9 discharge codes, as well as labels determined by study nurses.Results: The best performing machine learning algorithm achieved a performance of 88.57%/93.81%/92.80%/93.30%/89.84%/98.01% (accuracy/precision/recall/F-measure/area under ROC curve/area under precision-recall curve) on stroke case detection. For detecting stroke subtypes, the algorithm yielded an overall accuracy of 87.39% and greater than 85% precision on individual subtypes. The machine learning algorithms significantly outperformed the ICD-9 method on all measures (P value<0.001). Their performance was comparable to that of study nurses, with better tradeoff between precision and recall. The feature selection uncovered a subset of predictive variables that could facilitate future development of effective stroke phenotyping algorithms.Discussion and conclusions: By analyzing a broad array of patient data, the machine learning technologies held promise for improving detection of stroke diagnosis, thus unlocking high statistical power for subsequent genetic and genomic studies.

Dataset Information

Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.

Publications

Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.

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