Combining UK Biobank genetics and MRI imaging data to identify genetic factors associated with higher body fat % but lower risk of diabetes.

Transcription

1 Combining genetics and MRI imaging data to identify genetic factors associated with higher body fat % but lower risk of diabetes. Hanieh Yaghootkar Diabetes UK RD Lawrence Fellow 21 June 2018 meeting - London

2 Some people develop diabetes at lower BMIs and some people are healthy at higher BMIs which is partly genetically defined. Percentage of individuals Type 2 diabetic patients (N=12,333; mean age 61) Non-diabetic individuals (N=370,112mean age 57) Data from Body mass index (BMI)

3 Some people develop diabetes at lower BMIs and some people are healthy at higher BMIs which is partly genetically defined. Percentage of individuals Type 2 diabetic patients (N=12,333; mean age 61) Non-diabetic individuals (N=370,112mean age 57) Data from Body mass index (BMI)

4 Genetic factors associated with favourable adiposity will help understand mechanisms that delay or protect individuals from type 2 diabetes. Type 2 diabetes Odds ratio Body fat % Type 2 diabetes Odds ratio Body fat % Unfavourable adiposity Favourable adiposity FTO variant PPARG variant Data from

5 We characterized 14 genetic variants associated with favourable adiposity. Body fat % (n = 500,000) Published genome-wide association analysis Negative correlation Positive correlation Body fat % (n= 70,000) Lu et al. Nature communications (2016). Adiponectin (N=29,400) Dastani et al. PLoS Genet (2012). SHBG (N=21,800) Coviello et al. PLoS Genet (2012). HDL-C (N=99,900) Teslovich et al. Nature (2010). Fasting insulin (N=51,800) Manning et al. Nature genetics (2012). Triglycerides (N=96,600) Teslovich et al. Nature (2010). Alanine transaminase (N=55,500) Chambers et al. Nature genetics (2011). [DNAH10] VEGFA [] C6orf223 AEBP2 [] PDE3A [DNAH10] LYPLAL1 [] SLC30A10 VEGFA [] C6orf223 [FAM13A] AEBP2 [] PDE3A [PEPD] LYPLAL1 [] SLC30A10 CITED2 [] [FAM13A] NYAP2 [] IRS1 [PEPD] CITED2 [] GRB14 [] COBLL1 NYAP2 [] IRS1 [PPARG] GRB14 [] COBLL1 ANKRD55 [] MAP3K1 [PPARG] [MAFF] ANKRD55 [] MAP3K1 KLF14 [] MKLN1 [MAFF] TRIB1 [] [DNAH10] KLF14 [] MKLN1 [TNFSF12 TNFSF13] VEGFA [] C6orf223 TRIB1 [] [TNFSF12 TNFSF13] [ACO2] AEBP2 [] PDE3A [ACO2] [RORA] LYPLAL1 [] SLC30A10 [RORA] PCDH17 [] DIAPH3 [FAM13A] [PEPD] PCDH17 [] DIAPH3 [TCF7L2] [TCF7L2] [POM121C] CITED2 [] [POM121C] [TET1] NYAP2 [] IRS1 [TET1] [KIAA1755] GRB14 [] COBLL1 [KIAA1755] [PPARG] ZBTB7C [] CTIF ZBTB7C [] CTIF ANKRD55 [] MAP3K1 MYCN [] FAM49A MYCN [] FAM49A [MAFF] [ZZEF1] [ZZEF1] KLF14 [] MKLN1 PRMT6 [] NTNG1 TRIB1 [] [PPP1R21] [TNFSF12 TNFSF13] [FTO] [ACO2] [ACMSD] [RORA] Associated with higher body fat % but better metabolic profile Associated with higher body fat % and adverse metabolic profile

6 Favourable adiposity variants together were associated with higher adiposity but lower risk of type 2 diabetes, heart disease and hypertension Body fat % N = 443,000 Type 2 diabetes (Odds ratio) 14,371 cases, 428,017 controls Heart disease (Odds ratio) Systolic blood pressure (mmhg) Diastolic blood pressure (mmhg) 37,741 cases, 318,892 controls N = 450,075 N = 450,075 Effects Per 5+ alleles x x x x x10-24 Data from

7 The favourable adiposity effect is not driven by altered body shape in men detectable by waist-to-hip ratio Women 207,126 Men 175,776 Waist circumference Hip circumference Waist circumference Hip circumference Data from

8 We investigated the associations between the favourable adiposity variants and MRI/CT scan measures of abdominal adipose tissue. Visceral and subcutaneous fat (N = 27,766) Liver fat (N = 9,210) Image from: West J. et al. Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies. PloS One, 2016 Image from: Wilman HR. et al. Characterisation of liver fat in the cohort. PloS One, 2017 (N = 5,045) The Netherlands Epidemiology of Obesity Study () (N = 2,236) Tübingen Family Study for Type-2 Diabetes (TÜF) (N = 833) (N = 1,320) Published GWAS* (N = 18,332) (N = 5,045) The Netherlands Epidemiology of Obesity Study () (N = 1,821) Tübingen Family Study for Type-2 Diabetes (TÜF) (N = 911) (N = 1,433) *Chu A. et al. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation. Nature Genetics, 2016

9 Favourable adiposity variants together were associated with higher subcutaneous fat but lower liver fat. Subcutaneous fat (Litres) All Chu et al Visceral fat (Litres) All Chu et al All Liver fat (%) Women Chu et al Women Chu et al Women Men Chu et al Men Chu et al Men

10 Favourable adiposity alleles have similar effects in South Asians comparing to Europeans, but are less frequent in South Asians Body fat % BMI (kg/m 2 ) Type 2 diabetes (Odds ratio) Heart disease (Odds ratio) Effects Per 5+ alleles Average number of alleles 10.5 vs 11.6; P< ,000 Europeans 10,203 South Asians Genetic score of favourable adiposity Data from

11 The identification of favourable adiposity alleles locate genes that may be targets for novel insulin-sensitizing agents. Thiazolidinediones (PPAR-γ agonists) redistributes fat away from liver towards an expanded subcutaneous depot

12 Acknowledgements The Genetics of Complex Traits team at Exeter: Analysis of MRI image data of abdominal & liver fat: : Jimmy Bell, Alex Blakemore and Andrianos Yiorkas Robin Beaumont Tim Frayling Rachel Freathy James Harrison Yingjie Ji Sam Jones Anna Murray study: Francesca Frau, Louise Thomas, Karla V. Allebrandt, Ewan Pearson study: Dennis Mook-Kanamori, Renée de Mutsert, Kate Ruth Seth Sharp William Thompson Marcus Tuke Jessica Tyrrell Mike Weedon Andy Wood TÜF study: Norbert Stefan, Harald Staiger

13 The identification of favourable adiposity alleles locate genes that may be targets for novel insulin-sensitizing agents. Thiazolidinediones (PPAR-γ agonists) redistributes fat away from liver towards an expanded subcutaneous depot TRIB1 adipose tissue mass and lipolysis. VEGF-A maintains healthy expansion of adipose tissue and protects from lipotoxicity. CITED2 is required for optimal PPARγ activation. FAM13A is in the insulin signaling cascade. IRS1, CCDC92 and DNAH10 are associated with adipogenesis, lipid accumulation and adipocyte differentiation. KLF14 is a master regulator of gene expression in adipose tissue and associated with adipocyte cell size. MAP3K1 regulates expression of IRS1. LYPLAL1 maintains triglycerides metabolism.