Genetically predicted waist-to-hip circumference ratio and coronary artery disease: A sex-specific Mendelian randomization study

被引:0
作者
Ye, Qiang [1 ]
Taliun, Sarah A. Gagliano [2 ,3 ,4 ]
机构
[1] Univ Montreal, Fac Med, Montreal, PQ, Canada
[2] Univ Montreal, Dept Med, Montreal, PQ, Canada
[3] Univ Montreal, Fac Med, Dept Neurosci, Montreal, PQ, Canada
[4] Montreal Heart Inst, Montreal, PQ, Canada
来源
HUMAN GENETICS AND GENOMICS ADVANCES | 2023年 / 4卷 / 04期
基金
加拿大健康研究院;
关键词
body fat composition; CAD; coronary artery disease; Mendelian randomization; MR; sex-specific; waist-to-hip circumference ratio;
D O I
10.1016/j.xhgg.2023.100230
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Coronary artery disease (CAD) affects millions of individuals worldwide and results in a substantial burden to healthcare systems. Although it is established that CAD affects females differently than males, differences between the sexes are not routinely accounted for. Body mass index is a known risk factor for CAD. However, more accurate metrics of body fat, including waist-to-hip circumference ratio (WHR), could be more meaningful clinically. WHR exhibits sex differences due to sex hormones, differing effects at genetic risk loci, and other factors. It is unclear if WHR is a causal factor for CAD in one or both sexes, but this information will be crucial for improving heart health. Causal inference, however, can be challenging. Large-scale cohorts with genetic data allow for Mendelian randomization, which, given certain assumptions, tests whether there is a causal relationship between an exposure and the outcome using genetic variants. We conducted sex-specific, one-sample MR analyses using two-stage least-squares regression in the UK Biobank with genetic variants robustly associated with WHR. We found evidence of a causal relationship between WHR and CAD risk in females (OR [95% CI] = 1.16 [1.06-1.26]; p value = 7.5E-4), whereas in males, we did not find evidence of a causal relationship (OR [95% CI] = 1.40 [0.98-2.01]; p value = 0.063). Results were supported by two additional MR approaches (using a genetic risk score and two-sample MR using the inverse variance weighted approach). We encourage future work assessing sex-specific effects using causal inference techniques to better understand factors contributing to complex disease risk.
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页数:6
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共 15 条
  • [1] The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019
    Buniello, Annalisa
    MacArthur, Jacqueline A. L.
    Cerezo, Maria
    Harris, Laura W.
    Hayhurst, James
    Malangone, Cinzia
    McMahon, Aoife
    Morales, Joannella
    Mountjoy, Edward
    Sollis, Elliot
    Suveges, Daniel
    Vrousgou, Olga
    Whetzel, Patricia L.
    Amode, Ridwan
    Guillen, Jose A.
    Riat, Harpreet S.
    Trevanion, Stephen J.
    Hall, Peggy
    Junkins, Heather
    Flicek, Paul
    Burdett, Tony
    Hindorff, Lucia A.
    Cunningham, Fiona
    Parkinson, Helen
    [J]. NUCLEIC ACIDS RESEARCH, 2019, 47 (D1) : D1005 - D1012
  • [2] The UK Biobank resource with deep phenotyping and genomic data
    Bycroft, Clare
    Freeman, Colin
    Petkova, Desislava
    Band, Gavin
    Elliott, Lloyd T.
    Sharp, Kevin
    Motyer, Allan
    Vukcevic, Damjan
    Delaneau, Olivier
    O'Connell, Jared
    Cortes, Adrian
    Welsh, Samantha
    Young, Alan
    Effingham, Mark
    McVean, Gil
    Leslie, Stephen
    Allen, Naomi
    Donnelly, Peter
    Marchini, Jonathan
    [J]. NATURE, 2018, 562 (7726) : 203 - +
  • [3] Ten simple rules for conducting a mendelian randomization study
    Gagliano Taliun, Sarah A.
    Evans, David M.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2021, 17 (08)
  • [4] The MR-Base platform supports systematic causal inference across the human phenome
    Hemani, Gibran
    Zhengn, Jie
    Elsworth, Benjamin
    Wade, Kaitlin H.
    Haberland, Valeriia
    Baird, Denis
    Laurin, Charles
    Burgess, Stephen
    Bowden, Jack
    Langdon, Ryan
    Tan, Vanessa Y.
    Yarmolinsky, James
    Shihab, Hashem A.
    Timpson, Nicholas J.
    Evans, David M.
    Relton, Caroline
    Martin, Richard M.
    Smith, George Davey
    Gaunt, Tom R.
    Haycock, Philip C.
    [J]. ELIFE, 2018, 7
  • [5] PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations
    Kamat, Mihir A.
    Blackshaw, James A.
    Young, Robin
    Surendran, Praveen
    Burgess, Stephen
    Danesh, John
    Butterworth, Adam S.
    Staley, James R.
    [J]. BIOINFORMATICS, 2019, 35 (22) : 4851 - 4853
  • [6] Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
    Khera, Amit V.
    Chaffin, Mark
    Aragam, Krishna G.
    Haas, Mary E.
    Roselli, Carolina
    Choi, Seung Hoan
    Natarajan, Pradeep
    Lander, Eric S.
    Lubitz, Steven A.
    Ellinor, Patrick T.
    Kathiresan, Sekar
    [J]. NATURE GENETICS, 2018, 50 (09) : 1219 - +
  • [7] Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study
    Larsson, Susanna C.
    Back, Magnus
    Rees, Jessica M. B.
    Mason, Amy M.
    Burgess, Stephen
    [J]. EUROPEAN HEART JOURNAL, 2020, 41 (02) : 221 - +
  • [8] Waist-to-hip ratio versus BMI as predictors of cardiac risk in obese adult women
    Noble, RE
    [J]. WESTERN JOURNAL OF MEDICINE, 2001, 174 (04) : 240 - 241
  • [9] PLINK: A tool set for whole-genome association and population-based linkage analyses
    Purcell, Shaun
    Neale, Benjamin
    Todd-Brown, Kathe
    Thomas, Lori
    Ferreira, Manuel A. R.
    Bender, David
    Maller, Julian
    Sklar, Pamela
    de Bakker, Paul I. W.
    Daly, Mark J.
    Sham, Pak C.
    [J]. AMERICAN JOURNAL OF HUMAN GENETICS, 2007, 81 (03) : 559 - 575
  • [10] Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects
    Rask-Andersen, Mathias
    Karlsson, Torgny
    Ek, Weronica E.
    Johansson, Asa
    [J]. NATURE COMMUNICATIONS, 2019, 10 (1)