A multivariable approach for risk markers from pooled molecular data with only partial overlap.

Publisher: BioMed Central Country of Publication: England NLM ID: 100968552 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2350 (Electronic) Linking ISSN: 14712350 NLM ISO Abbreviation: BMC Med Genet Subsets: MEDLINE

Original Publication: London : BioMed Central, [2000-

Models, Theoretical* ; Multivariate Analysis* ; Research Design*; Genome-Wide Association Study/*methods; Data Analysis ; Humans ; Polymorphism, Single Nucleotide ; Regression Analysis ; Risk Factors ; Software

Background: Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers - even after imputation.
Methods: Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection.
Results: Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals.
Conclusions: Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request.
Trial Registration: All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered.

BMC Med Genomics. 2016 Aug 12;9 Suppl 1:31. (PMID: 27535739)
Stat Appl Genet Mol Biol. 2012 Jan 06;11(1):Article 7. (PMID: 22499682)
Biometrics. 2006 Dec;62(4):961-71. (PMID: 17156269)
PLoS One. 2016 May 09;11(5):e0155226. (PMID: 27159447)
Am J Epidemiol. 2015 Mar 15;181(6):406-21. (PMID: 25713336)
Nature. 2016 Aug 4;536(7614):41-47. (PMID: 27398621)
Genet Epidemiol. 2010 Dec;34(8):879-91. (PMID: 21104890)
Nat Genet. 2014 Nov;46(11):1233-8. (PMID: 25261932)
Am J Hum Genet. 2014 Oct 2;95(4):462-71. (PMID: 25279986)
Stat Med. 2013 May 10;32(10):1778-91. (PMID: 22786659)
Ann Stat. 2014 Apr;42(2):413-468. (PMID: 25574062)
Stat Med. 2014 Jul 10;33(15):2567-76. (PMID: 24634227)
PLoS Genet. 2013;9(2):e1003264. (PMID: 23408905)
Nat Genet. 2013 Aug;45(8):868-76. (PMID: 23770605)
Genet Epidemiol. 2010 Nov;34(7):643-52. (PMID: 20842684)
Stat Appl Genet Mol Biol. 2008;7(1):Article12. (PMID: 18384265)
Hum Mol Genet. 2016 Apr 15;25(8):1663-76. (PMID: 27008888)
Nat Commun. 2016 Mar 09;7:10933. (PMID: 26956414)
J Natl Cancer Inst. 2015 Oct 12;107(12):djv279. (PMID: 26464424)
BMC Bioinformatics. 2016 Aug 30;17(1):327. (PMID: 27578050)
Nat Commun. 2015 Jan 08;6:5751. (PMID: 25569183)

Keywords: Consortium; Multivariable model; Partial overlap; Regularized regression; Single nucleotide polymorphism

Date Created: 20190721 Date Completed: 20191202 Latest Revision: 20200309

20240628

PMC6642584

10.1186/s12881-019-0849-0

31324155