Atelier 1: Interpretation von Gentests (d)

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1 Atelier 1: Interpretation von Gentests (d) Prof., Technische Universität München Prof. Dr. erforscht die Grundlagen der Anpassung des menschlichen Stoffwechsels an die Veränderungen in der Zufuhr von Nährstoffen und nicht-nutritiven Inhaltsstoffen der Nahrung. Anwendungsnahe Forschungsfelder sind die umfassende Analyse des Phänotyps und darauf aufbauend Konzepte der personalisierten Ernährung. studierte an der Justus-Liebig-Universität Gießen Ernährungswissenschaft und war nach Promotion und Habilitation im Fach Biochemie der Ernährung u.a. an der University of Glasgow/UK sowie ca. 3 Jahre an der School of Medicine in Pittsburgh/USA tätig erhielt sie den Ruf auf den Lehrstuhl für Biochemie der Ernährung an der Universität Gießen und vertritt seit 1998 an der TU-München den Lehrstuhl für Ernährungsphysiologie. Prof. Daniel ist Mitglied der Akademie der Wissenschaften Leopoldina, des Bioökonomierates, der Advisory Group der EU- Kommission für Horizon 2020 sowie weiterer nationaler und internationaler Gremien und Fachgesellschaften. Moderation: Dr. Annette Matzke, SGE

2 Bern, Sept Atelier: Genotypisierung und Interpretation EXPOSOME HUMAN GENOME MICROBIOME HUMAN GENOME MICROBIOME EXPOSOME EXPOSOME PHENOME HUMAN GENOME MICROBIOME HUMAN GENOME MICROBIOME DNA Phenotype 3 x bases age gender body weight (BMI) blood pressure disease history fasting blood sample GWAS Examples of consilidated NUTRIGENETICS approaches Untargeted (mostly GWAS) FTO and other obesity/bmi genes TCF7L2 and other type 2 diabetes risk genes Genetics of cardiovascular diseases Targeted/candidate gene MTHFR, B-vitamin status and blood pressure FADS, fatty acids status and cardio-metabolic health Vitamin D status and UV exposure APOE: Impact on health and disease risk, & response to lifestyle APOA5, CVD risk and TG metabolism 8

3 Evidence for genetic determinants of BMI and obesity state Genetics of obesity, type II diabetes mellitus and CVD Evidence for genetic determinants of BMI and obesity state Evidence T2DM susceptibility for genetic determinants genes of type II diabetes mellitus (NIDDM) >80 disease associated genes OR: (15.8) Genetic variants identified through GWAS explain only 10% heritibility for T2DM. -> missing heritability Brunetti A et al. World J Diabetes 2014;5:128 Brunetti A et al. World J Diabetes 2014;5:128

4 Evidence for genetic determinants in lipid handling & CVD risk via ApoE JAMA Sep 19;298(11): Association of apolipoprotein E genotypes with lipid levels and coronary risk. Bennet AM et al. Evidence for genetic determinants of blood LDL-levels via ApoE Genetics of dietary status Evidence for genetic determinants in blood pressure via MTHFR & Folate MTHFR genotype, B-vitamin status and blood pressure Vitamin B2 MTHFR Reilly R et al., 2014 MTHFR 677C T genotype: MTHFR 677 TC ~ 40% reduced activity and homocysteine levels MTHFR 677 TT ~ 70% reduced activity & CVD risk and BLOOD PRESSURE Evidence for genetic determinants in blood pressure via MTHFR & Folate Evidence for genetic determinants in blood pressure via MTHFR & Folate Identification of loci affecting vitamin status Identification of loci affecting vitamin status De Vos et al., Am. J. Clin. Nutr. 88, 2008 Hazra A et al. Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet Dec 1;18(23):

5 Evidence for genetic determinants in blood pressure via MTHFR & Folate Blood pressure response to riboflavin (VitB2) by MTHFR genotype in premature CVD patients Riboflavin treatment (1.6 mg/d for 16 wk) A total of 83 patients (representing all 3 genotypes) who participated in a placebo-controlled riboflavin intervention for 16 wk in 2004 agreed to take part. Nested within this follow-up, those with the TT genotype (n = 31) proceeded to intervention with riboflavin (1.6 mg/d for 16 wk) or placebo, conducted in a crossover style. Wilson CP,et al. Riboflavin offers a targeted strategy for managing hypertension in patients with the MTHFR 677TT genotype: a 4-y follow-up. Am J Clin Nutr Mar;95(3): Food4Me: a Paneuropean study on Personalized Nutrition Evidence for genetic determinants for plasma 25-OH-Vitamin D levels food frequency questionaire physical activity monitoring anthropometric measurements DNA from buccal swaps dry blood spots for LC/GC-MS analysis GC NADSYN1 DHCR7 ACADSB CYP2R1 Genome-wide associations of circulating 25-hydroxy vitamin D graphed by chromosome position and log10 P-value based on the five GWAS cohorts (n = 4501). Only P values of >10 16 are plotted, with the most significant variants in the GC gene on chromosome 4 not being shown (rs , rs and rs7041; P-values of , and , respectively). Jiyoung Ahn et al. Genome-wide association study of circulating vitamin D levels. Hum Mol Genet Jul 1; 19(13): Evidence for genetic determinants for plasma 25-OH-Vitamin D levels Associations between blood 25 OH- Vitamin D3 concentrations, socio-demographic and lifestyle characteristics DHCR7 binding protein Jonatan D. Lindh et al. Vitamin D and drug-metabolising enzymes Photochem. Photobiol. Sci., 2012,11, Associations between blood 25 OH- Vitamin D3 concentrations and relevant SNP s U Hoeller, M et al. Application of dried blood spots to determine vitamin D status in a large nutritional study with unsupervised sampling: the Food4Me project, British Journal of Nutrition, Volume 115 / Issue 02 / January 2016, pp

6 Concentration of blood 25 OH- vitamin D3 according to vitamin D genetic risk score. Minor alleles for GC rs , GC rs4588, GC rs7041, VDR rs , VDR rs were coded as 0, 1 and Associations between blood 25 OH- Vitamin D3 concentrations and relevant SNP s 25-OH Vitamin D3 (nmol/l) * ** 45 0 to 2 3 to 5 > 6 Vitamin D risk score (number of risk alleles) 2 and summed to generate the risk score and grouped into 0-2, 3 to 5 or 6 or more minor alleles. Values represent adjusted means and SE. Analyses were adjusted for age, sex, ethnicity, season, country, BMI, vitamin D intake from foods and vitamin D supplementation. Evidence for genetic determinants in blood PUFA levels across Europe Evidence for genetic determinants in blood PUFA levels across Europe n-3 acids n-6 acids Heterogenous reponses in plasma triglyceride levels to a fish oil supplement g EPA+DHA per day for 8 weeks, n=312 Dietary PUFA of the series 3 and series 6 % change in triglycerides R109: +150% n=118/312 TG Group average (n=312) -11% R245: -250% Caslake MJ, Miles EA, Kofler BM, Lietz G, Curtis P, Armah CK, Kimber AC, Grew JP, Farrell L, Stannard J, Napper FL, Sala-Vila A, West AL, Mathers JC, Packard C, Williams CM, Calder PC, Minihane AM. AJCN, 2008;88: Evidence for genetic determinants in blood PUFA levels across Europe Conversion of PUFA into eicosanoids Evidence for genetic determinants in blood PUFA levels across Europe Conversion of PUFA via FADS1 Evidence for diet X genetics in blood PUFA levels across Europe Evidence for diet X genetics in blood PUFA levels across Europe n-3 acids n-6 acids Models explained 22-33%of the variability in PUFA concentrations in the test set. Predicting fatty acid profiles in blood based on food intake and the FADS1 rs SNP. Hallmann J, Kolossa S, Gedrich K, Celis-Morales C, Forster H, O'Donovan CB, Woolhead C, Macready AL, Fallaize R, Marsaux CF, Lambrinou CP, Mavrogianni C, Moschonis G, Navas-Carretero S, San-Cristobal R, Godlewska M, Surwiłło A, Mathers JC, Gibney ER, Brennan L, Walsh MC, Lovegrove JA, Saris WH, Manios Y, Martinez JA, Traczyk I, Gibney MJ, Daniel H; Food4Me Study.

7 Genotypisierung im kommerziellen Umfeld Genotypisierung im kommerziellen Umfeld Genotypisierung im kommerziellen Umfeld Genotypisierung im kommerziellen Umfeld My Genome App displaying individual risk alleles Genotypisierung im privaten Umfeld Nanopore Technologies DNA-Sequence analysis Evidence for genetic determinants in human nutrition and health EASAC agree with the European society of human genetics statement that nutrigenomic testing should be discouraged primarily because of its linkage with the sale of dietary supplements or no proven value.

8 Zusammenfassung Die Effektgrößen einzelner SNP s oder von Haplotypen sind generell sehr niedrig. Auch die kumulativen Effekte der Risiko-Allele erklären kaum 10% der Varianz. Bisher bieten Genotypisierungen für die durch Lebensstil bedingten Erkrankungen keine über die konventionellen Anamnese und Diagnostik hinausgehenden Nutzen. Bisher gibt es kaum (und wenn, nur kleine) prospektive Studien, die den Nutzen von spezifischen Ernährungsmaßnahmen auf der Grundlage selektionierter Genotypen geprüft haben. Genotypisierung ist nicht essentiell für Ansätze zur Personalisierten Ernährung.