Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations

PLOS ONE RESEARCH ARTICLE Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations Chisom Soremekun1,2,3, Tafadzwa Machipisa4,5,6,7,8, Opeyemi Soremekun1,9, Fraser Pirie10, Nashiru Oyekanmi ID3, Ayesha A. Motala10, Tinashe Chikowore11,12, Segun Fatumo ID1,3,13* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Soremekun C, Machipisa T, Soremekun O, Pirie F, Oyekanmi N, Motala AA, et al. (2023) Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. PLoS ONE 18(2): e0280344. https:// doi.org/10.1371/journal.pone.0280344 Editor: Hui-Qi Qu, Children’s hospital of philadelphia, UNITED STATES 1 The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda, 2 Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda, 3 H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria, 4 Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa, 5 Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, University of Cape Town, Cape Town, South Africa, 6 Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada, 7 Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada, 8 Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada, 9 Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa, 10 Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa, 11 Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, 12 Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, 13 Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom * Abstract Received: July 27, 2022 Accepted: December 27, 2022 Background Published: February 21, 2023 Liver disease is any condition that causes liver damage and inflammation and may likely affect the function of the liver. Vital biochemical screening tools that can be used to evaluate the health of the liver and help diagnose, prevent, monitor, and control the development of liver disease are known as liver function tests (LFT). LFTs are performed to estimate the level of liver biomarkers in the blood. Several factors are associated with differences in concentration levels of LFTs in individuals, such as genetic and environmental factors. The aim of our study was to identify genetic loci associated with liver biomarker levels with a shared genetic basis in continental Africans, using a multivariate genome-wide association study (GWAS) approach. Copyright: © 2023 Soremekun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data used in this study have been deposited in public databases like the EGA and GWAS catalogue. European Genomephenome Archive (EGA, https://www.ebi.ac.uk/ega/ , accession numbers EGAS00001001558/ EGAD00010000965, EGAS00001000545/ EGAD00001001639 and EGAS00001000545/ EGAD00001005346. Funding: CS is a Commonwealth Scholar, funded by the UK government and a fellow of the African Center for Translational Genomics (ACTG), an Methods We used two distinct African populations, the Ugandan Genome Resource (UGR = 6,407) and South African Zulu cohort (SZC = 2,598). The six LFTs used in our analysis were: aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, and albumin. A multivariate GWAS of PLOS ONE | https://doi.org/10.1371/journal.pone.0280344 February 21, 2023 1 / 14 PLOS ONE initiative of 54gene. TM was supported by scholarships from the UCT (Mayosi Research Group Fellowships, the Crasnow Travel Fellowship, the Departmental Research Committee (DRC) of Medicine and the Pan African Society of Cardiology Award), Wellcome Trust, and Population Health Research Institute (PHRI) and McMaster University. TC is an international training fellow supported by the Wellcome Trust grant (214205/Z/ 18/Z). SF is an international Intermediate Fellow funded by the Wellcome Trust grant (220740/Z/20/ Z) at the MRC/UVRI and LSHTM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors declare that they have no competing interests. Abbreviations: LFTs, Liver Function Tests; ALT, Alanine Transaminase; AST, Aspartate Transaminase; ALP, Alkaline Phosphatase; GGT, Gamma-Glutamyl Transferase; GWAS, GenomeWide Association Study; LMM, Linear Mixed Model; QQ, Quantile-Quantile; GEMMA, Genomewide Efficient Mixed-Model Association; UGR, Ugandan Genome Resource; SZC, South Africa Zulu cohort; GPC, General Population Cohort; DDS, Durban Diabetes Study; DCC, Diabetes Case Control; SNPs, Single Nucleotide Polymorphism; SST, Serum Separation Tube; LD, Linkage Disequilibrium; PheWAS, Phenome-Wide Association Studies; FUMA, Functional Mapping and Annotation; MAGMA, Multi-marker Analysis of GenoMic Annotation; MsigDB, Molecular Signatures Database; GO, Gene Ontology. Multivariate GWAS of liver function in Africans LFTs was conducted using the exact linear mixed model (mvLMM) approach implemented in GEMMA and the resulting P-values were presented in Manhattan and quantile-quantile (QQ) plots. First, we attempted to replicate the findings of the UGR cohort in SZC. Secondly, given that the genetic architecture of UGR is different from that of SZC, we further undertook similar analysis in the SZC and discussed the results separately. Results A total of 59 SNPs reached genome-wide significance (P = 5x10-8) in the UGR cohort and with 13 SNPs successfully replicated in SZC. These included a novel lead SNP near the RHPN1 locus (lead SNP rs374279268, P-value = 4.79x10-9, Effect Allele Frequency (EAF) = 0.989) and a lead SNP at the RGS11 locus (lead SNP rs148110594, P-value = 2.34x10-8, EAF = 0.928). 17 SNPs were significant in the SZC, while all the SNPs fall within a signal on chromosome 2, rs1976391 mapped to UGT1A was identified as the lead SNP within this region. Conclusions Using (...truncated)