TY - JOUR
T1 - An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease
AU - Ruotsalainen, Sanni E.
AU - Partanen, Juulia J.
AU - Cichonska, Anna
AU - Lin, Jake
AU - Benner, Christian
AU - Surakka, Ida
AU - FinnGen
AU - Palotie, Aarno
AU - Daly, Mark
AU - Jacob, Howard
AU - Matakidou, Athena
AU - Runz, Heiko
AU - John, Sally
AU - Plenge, Robert
AU - McCarthy, Mark
AU - Hunkapiller, Julie
AU - Ehm, Meg
AU - Waterworth, Dawn
AU - Fox, Caroline
AU - Malarstig, Anders
AU - Partanen, Jukka
AU - Savinainen, Kimmo
AU - Kosma, Veli Matti
AU - Schleutker, Johanna
AU - Laaksonen, Reijo
AU - Mannermaa, Arto
AU - Peltola, Jukka
AU - Rinne, Juha
AU - Jussila, Airi
AU - Isomäki, Pia
AU - Laitinen, Tarja
AU - Kankaanranta, Hannu
AU - Kähönen, Mika
AU - Auranen, Annika
AU - Uusitalo, Hannu
AU - Uusitalo-Järvinen, Hannele
AU - Salmi, Teea
AU - Harju, Jarmo
AU - Wahlfors, Tiina
AU - Mannermaa, Arto
AU - Kononen, Juha
AU - Shcherban, Anastasia
AU - Laivuori, Hannele
AU - Siirtola, Harri
AU - Gracia Tabuenca, Javier
AU - Partanen, Jukka
AU - Pirinen, Matti
N1 - Funding Information:
Acknowledgements We would like to thank Lea Urpa for proofreading, and Sari Kivikko, Huei-Yi Shen, and Ulla Tuomainen for management assistance. We would like to thank all participants of the FINRISK, FinnGen and UKBB studies for their generous participation. The FINRISK data used for the research were obtained from THL Biobank. This research has been conducted using the UK Bio-bank Resource with application number 22627. This work was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics [Grant No 312062 to SR, 312076 to MP, 312074 to AP, 312075 to MD]; Academy of Finland [Grant No 285380 to SR, 288509 to MP, 128650 to AP]; the Finnish Foundation for Cardiovascular Research [to SR, VS, and AP]; the Sigrid Jusélius Foundation [to SR, MP, and AP]; University of Helsinki HiLIFE Fellow grants 2017-2020 [to SR and MP]; Foundation and the Horizon 2020 Research and Innovation Programme [grant number 667301 (COSYN) to AP]; the Doctoral Programme in Population Health, University of Helsinki [to JJP and SER]; and The Finnish Medical Foundation [to JJP]. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and nine industry partners (AbbVie, AstraZeneca, Biogen, Celgene, Genentech, GSK, MSD, Pfizer and Sanofi). Following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/en), THL Biobank (https:// thl.fi/fi/web/thl-biopankki), Helsinki Biobank (https://www. terveyskyla.fi/helsinginbiopankki/en), Northern Finland Biobank Borealis (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki), Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Resea rch_and_development/Finnish_Clinical_Biobank_Tampere), Bio-bank of Eastern Finland (https://ita-suomenbiopankki.fi/), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.bloodservice. fi/Research%20Projects/biobanking). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to European Society of Human Genetics.
PY - 2020/10/27
Y1 - 2020/10/27
N2 - Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10–4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.
AB - Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10–4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.
U2 - 10.1038/s41431-020-00730-8
DO - 10.1038/s41431-020-00730-8
M3 - Article
C2 - 33110245
AN - SCOPUS:85100741308
SN - 1018-4813
VL - 29
SP - 309
EP - 324
JO - EUROPEAN JOURNAL OF HUMAN GENETICS
JF - EUROPEAN JOURNAL OF HUMAN GENETICS
IS - 2
ER -