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

BMC Bioinformatics

Jianwei Gou³  Yang Zhao⁶  Yongyue Wei⁶  Chen Wu⁴  Ruyang Zhang²  Yongyong Qiu⁶  Ping Zeng⁶  Wen Tan⁴  Dianke Yu⁴  Tangchun Wu¹  Zhibin Hu⁵  Dongxin Lin⁴  Hongbing Shen⁵  Feng Chen⁶ 

[1] Institute of Occupational Medicine and Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

[2] Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, China

[3] Department of Mathematical and Statistical Sciences, Nanjing Forestry University, Nanjing, China

[4] State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

[5] State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China

[6] Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China

关键词: Stability selection;    Smoothly clipped absolute deviation (SCAD);    Penalized logistic regression;    Least absolute shrinkage and selection operator (LASSO);    Interaction;    Genome-wide association study (GWAS);   

Others  :  1087606
DOI  :  10.1186/1471-2105-15-62

 received in 2013-05-30, accepted in 2014-02-18,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Gou et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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