British and Irish Longitudinal Studies of Ageing Meeting 31 st October – 1 st November 2013

1. Genetic approaches to understanding pathways associated with frailtyKris Mekli, Neil Pendleton and James Nazroo British and Irish Longitudinal Studies of Ageing Meeting 31st October – 1st November 2013

2. Introduction • 1. How much is a trait influenced by genes and the environment? • Family studies: heritability estimation – the fraction of phenotypic variation explained by • genetic variation (Heritability is a measure of the contribution of genetics to phenotype) • Human height: 0.8 • High blood pressure: 0.3-0.5 • Major depression: 0.34-0.42 • 2. Which genes and genetic variants are responsible for the trait? • In the genome: • CNV: large sections of the genome are affected >1000bp (insertions/deletions)-14,000 • Small number of repeats -1.4 M • 1 bp small genetic variants -38M • ↓ • Whole genome sequencing

3. 3 billion base pairs in human genome 99.5% genetic similarity between 2 unrelated people Focusing on Single Nucleotide Polymorphism (SNP) - - CATGATTGT G C CG - - Individual 1 - - CACGATTG TGA CG - - Individual 2 LD ~ 38 M SNPs in human genome → 5.3 M with frequency 10-50% With or without obvious function Not independent from each other: LD Catalogued as rs numbers: rs6296

4. Genetic association analysis Allele patterns that are overrepresented in in cases relative to controls Logistic regression: case-control phenotype Linear regression: assuming a linear relationship between mean value of the trait and genotype Phenotypic score Micro-array technology: measuring (genotyping) these small variants in the genome on a large scale • Scale: • Candidate gene • tests a hypothesis • few genes/SNPs • Genome-wide scan • hypothesis free • 100,000s SNPs (common, MAF>1%) SNP genotype From: Balding, 2006, Nat Rev Gen

5. Candidate gene approach: • We identify the pathway and genes involved beforehand (need a hypothesis) • We chose the SNPs (functional variants from literature) • Not easy to cover all pathways (often don’t know all of them) • GWAS approach • Large dataset: challenging • to handle, • chance of false results (→Bonferroni correction, currently: 5xE-08 ) • Good quality dataset is crucial • QC before • Inspection of the results (QQ plot) • Interpretation of results: can be challenging

6. Example of a candidate gene study • Frailty is: • clinically recognizable state of increased vulnerability, • resulting from aging-associated declinein reserve and function across • multiple physiologic systems, • ability to cope with everyday or acute stressors is compromised. • Research question: what causes frailty from the biological side? Changes in hormone levels with age Sex steroids decrease: testosterone, androstenedione, DHEA, DHEA-SO4 and estradiol → they down-regulate cytokines Inflammatory markers increase: • levels of some pro-inflammatory cytokines (IL-1, IL-6, TNF) • acute-phase reactant C-level protein (CRP) ↓ Frailty mechanism: chronic low level inflammation IL-6, TNF and CRP-levels associated with sarcopenia, mortality, functional decline and frailty Task: to find evidence for this mechanism via genetic association analysis

7. Frailty measures in the literature • Comprehensive measure including a wide range of conditions: health problems, physical activity level, mood, problems in everyday activities (~ 70 variables) Rockwood Index • Performance-based measure: A few specific criteria is applied (~ 5 variables) Fried Frailty Phenotype • fRaill study started with this measure • Easier to develop • Easier to relate to biological pathways

8. Paper: Fried et al. 2001 Frailty in Older Adults: Evidence for a Phenotype J Gerontol; 56(3):M146-M156. Aim: to establish a standardized definition of frailty Method: • population: from the Cardiovascular Health Study (CHS) 5,317 individuals (2,240 men and 3,077 women) 65 years and older • phenotype: questionnaires and physical examination 5 items: • sarcopenia • exhaustion • low physical activity • slowness • weakness Outcome Robust phenotype: positive for 0 item Pre-frail phenotype: positive for 1-2 items Frail phenotype: positive for 3-5 items

9. Frailty in the English Longitudinal Study of Ageing (ELSA) I. Measurement Nurse data • sarcopenia replaced with unintentional weight loss [measured, kg], positive if over 8% bodyweight • slowness: timed walk over 8 feet (~ 2.5 m) [measured, sec] positive for the slowest 20% of population • grip strength: using a dynamometer [measured, kg] positive for the weakest 20% of population Core dataset • exhaustion: questionnaire [self-reported] ‘everything they did during the past week was an effort’ and ‘could not get going much of the time during the past week’ positive if answer is yes to both questions • low physical activity [self-reported] positive if respondent does not work and takes part in no other physical activities Outcome: Robust: positive for 0 item Pre-frail: positive for 1-2 items Frail: positive for 3-5 items Assessment in W2 (n=7598) then thresholds applied in the population (n=3160) with genetic info available

10. II. Results Demographic results Wave 2, n=3160 Males = 1466, females =1694 Mean age = 68.3 years, spanning: 60-79 years Phenotypic results Presence of frailty (%) Genotyped sub-population, n=3160 Frailty prevalence increases W2→W4 (4 years, age effect) Females are more frail then males (gender effect)

11. III. Genetic association analysis Approach: candidate gene (from ELSA DNA Repository 620 SNPs representing 89 genetic regions) Method: linear regression – genotypes in the model Phenotype: frail, pre-frail, robust (Fried Frailty Phenotype) Covariates: age, gender Parameters: HWE >0.01, MAF >0.05 → 590 SNPs (=89 sets)

12. Association results – most significant P-values for genetic regions (IL-1, IL8, IL10, IL-12A/B, IL-15, IL-18 and CRP: not significant)

13. TNF: pro-inflammatory cytokine, produced in macrophages controls and orchestrates the immune response on several levels Biological roles of TNF Apoptosis Programmed cell death Through caspases Cell shrinkage and breakdown Cancer Regression of tumours after bacterial infection Bone resorption Stimulating osteoclast differentiation of macrophages Necrosis? Traumatic cell death Part of an anti-pathogen response TNF Cell differentiation and Proliferation Including tumor cells Immune response regulator Adaptive immune system Phagocytosis Production of IL-1 and IL-6 (↑) Liver: production of CRP Insulin metabolism/obesity With IL-6 they inhibit insulin sensitivity (PPARγkey mediator)

14. How do the results fit in literature? • Elevated levels of TNF in frail individuals (Hubbard et al. 2009) • Molecular level: • rs1800629 A allele • Stronger transcriptional activator than G allele → more TNF (literature) • A allele is associated with increased frailty (our study) • Biomarker level: • rs1800629 A allele • associated with decreased levels of cholesterol and HDL (our study) • Septic shock: Increased (↑) TNF levels accompanied by decreased (↓)levels of cholesterol • (literature) • Anti-TNF treatment (↓) accompanied by increased (↑) levels of HDL (literature) • rs1800629 A was significantly associated with lower cholesterol level (literature) • decreasedlevels of HDL, LDL and total cholesterol levels are associated with frailty and • mortality (literature) • Immune system level: • IL-6 and IFN-γare also significant (TNF induces levels of these cytokines) • ↓ • Role of inflammation in frailty

15. Conclusion • Our results: • Support the role of inflammation in frailty (IL-6 and IFNγ), • although other key members were not significant (IL-1 and CRP) • We did not find evidence for the involvement of: • Members of the steroid hormone biosynthesis pathway for example SULT2A1/SULT1E1 (DHEA ↔ DHEA-SO4) or HSD11B1 (hydroxysteroid • dehydrogenase) were not significant • Future directions • regression analysis against the Rockwood Frailty Index (a more complex measure) • rs1800629 is associated with bipolar disorder • GWAS (2.5M SNPs) for discovering more pathways in frailty (Fried and Rockwood • measures), replication in the US Health and Retirement Study • cortisol and metabolomics • rs1800629 AA genotype is associated with increased salivary cortisol level (Rosmondet al. 2001)→cortisol is immune system suppressive

16. Example GWAS Dataset: HRS Number of individuals: 12,507 Genetic coverage: 1,270,585 SNPs Phenotype: mean CES-D score (0-8), calculated from min 3 observations Covariates: sex, self-reported race (corresponds very well to calculated from genetic data) Linear regression • CES-DCenter for Epidemiologic Studies Depression Scale • (Wave 2- 10) • Now think about the past week and the feelings you have experienced. Was it true for you much of the time during the past week, that • you felt depressed? • everything you did was an effort? • your sleep was restless? • you were happy? (positive item) • you felt lonely? • you felt sad? • you could not get going? • you enjoyed life? (positive item)

17. QQ plot Close adherence of P-values to the red line (=null hypothesis) → few systematic sources of spurious association Suggestive of multiple weak associations

18. Manhattan plot rs58682566 P=3.45E-08 CHR18 CHR8 CHR4 5x10-8 CHR3 CHRX

19. Most significant results Chr18: 24,421 bp Gene: EPG5 (Homo sapiens ectopic P-granules autophagy protein 5 homolog (C. elegans)) Function: intracellular catabolic processes Chr4: 131,022 bp Genes: TLR1, TLR6, TLR10: role in pathogen recognition and activation of innate immunity, mediate the production of cytokines Chr8: Region 1 (2 SNPs): desert Region 2 (4 SNPs): SAMD12 (Homo sapiens sterile alpha motif domaincontaining12) function? Chr3: Gene: FETUB (Homo sapiens fetuinB) Role in several diverse functions, osteogenesis and bone resorption, regulation of insulin receptors, response to systemic inflammation ChrX: 90,715 bp Gene: MAP7D2 (Homo sapiens MAP7 domaincontaining2) function?

20. Previous GWAS studies of depression

21. Conclusion • Only a few SNPs reach suggestive GW-significance: in line with previous findings in the literature • Top hit: may be associated with catabolic cell processes • Future directions • Replication: in the ELSA dataset (CES-D available, same SNPs genotyped) • Other related phenotypes: cognition Acknowledgement Dr MeenaKumari and Mr Jorgen Engmann at UCL for the genotype and phenotype data Collaborators in the US: Professor Jinkook Lee, professor Carol Prescott and Drystan Phillips All the participants in ELSA and HRS