Accepted_test

Efficiency of a new multi-trait approach applied to back pain-related phenotypes

by Nadezhda M. Belonogova | Elizaveta E. Elgaeva | Irina V. Zorkoltseva | Anatoliy V. Kirichenko | Gulnara R. Svishcheva | Maxim B. Freidin | Frances M. K. Williams | Pradeep Suri | Tatiana I. Axenovich | Yakov A. Tsepilov | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosi-birsk, Russia | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosi-birsk, Russia | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia | Department of Biology, School of Biological and Behavioral Sciences, Queen Mary University of London, London EC1M 6BQ, UK | Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK | Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle WA 98108, USA. | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia | Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia

Abstract ID: 308

Event: BGRS-abstracts

Sections: [Sym 4] Section “Genome-wide association studies”

Back pain (BP) is a major contributor to disability worldwide. Less than half of the heritability is explained by common genetic variants identified by GWAS. More powerful methods of statistical analysis may offer additional insights. In this study, we utilized a novel multi-trait approach to merge three BP-related phenotypes: CBP, dorsalgia, and IDD, which have been previously used in BP studies.

Using a discovery sample from the UK Biobank, the GWAS summary statistics for the three BP-related phenotypes were obtained. They were used for the calculation of the multi-trait summary statistics. Then we performed a gene-based association analysis of the individual and multi-trait phenotypes. For all identified genes, conditional analysis was applied. Using the FinnGen database, we conducted a replication study by applying GBA to the 32 genes identified in the first step and using GWAS summary statistics for the two BP-related traits available on the FinnGen sample.

In total, we identified and replicated 16 genes significantly associated with BP-related traits. Thirteen genes were detected on the multi-trait phenotype that is in accordance with high genetic correlation between BP-related traits. Some genes have been previously described as associated with BP or with the genetically correlated traits, or as included in pathways associated with BP. Our results verify the role of these genes in BP-related traits and provide new insights into the genetics of back pain.