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Genetic epidemiology of complex traits: issues and methods

Genetic epidemiology of complex traits: issues and methods. M.W.Zuurman, Werkbespreking Medische Biologie 28 november 2005. Breedte strategie. The presentation. Background Issues Methods. What do we want anyway?. We want to cure disease!. We want to explain disease!.

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Genetic epidemiology of complex traits: issues and methods

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  1. Genetic epidemiology of complex traits: issues and methods M.W.Zuurman, Werkbespreking Medische Biologie 28 november 2005 Breedtestrategie

  2. The presentation • Background • Issues • Methods

  3. What do we want anyway? We want to cure disease! We want to explain disease! We want to counteract disease!

  4. Let’s explain disease Breedtestrategie: Let’s explain Cardiovascular and Renal disease What is disease?

  5. What is disease? Disease is a condition in the organism that impairs normal function of the organism

  6. Normal: • Conforming with or constituting a norm or standard or level or type or social norm • In accordance with scientific laws • Being approximately average or within certain limits • Convention: something regarded as a normative example • A statistical measure of usually observed structures, typical, or representative type Subjectivity of ‘normal’ vs ‘diseased’: A disease is any abnormal condition of the body or mind that causes discomfort, dysfunction, or distress to the person affected or those in contact with the person.(Wikipedia) Disease: Disease is a condition in the organism that impairs normal function of the organism

  7. Medical/Research practice: Disease (Platonic) + + + Symptoms (Phenotypes!) Causes: Nurture/Nature + - - Intervention

  8. Organisms are born with a set of genes in a certain environment Disease Genotype Environment Nature versus Nurture Mulcaster (1581): “that treasure bestowed on them by nature, to be bettered in them by nurture” Genetic versus environmental influence In reality, you can’t have one without the other:

  9. Summary (1) When seeking to explain disease: • Define disease clearly • What is normal and why? • It will determine the extend of ‘abnormal’ • Define phenotypes clearly • Make them quantifiable with sufficient Specificity : the probability to detect a negative result (e.g. ‘healthy’ or ‘control’) and Sensitivity the probability to detect a positive result (e.g. ‘diseased’ or ‘case’)

  10. Main issue Genetic epidemiology of complex traits Research Question: What is the genetic basis of complex traits (=disease/disease phenotypes)?

  11. Genetic variation Locus (e.g. QTL) Gene (e.g. expression arrays) Single Nucleotide Polymorphisms (SNP) (genotyping): ~AATGCCGA~ ~AATACCGA~ ~TTACGGCT~ ~TTATGGCT~ Divided in wild type and mutated alleles Has the genotype form of AA AB BB Can be functional Can be a neighbor of a functional variation (haplotype) Can be none of those

  12. Complex Traits • Mendelian traits: a single gene  phenotype • - e.g. eye colour, curly hair etc. • - also called dichotomous traits • - irrespective of environment in most cases • Continuously variable trait: polygenic and/or pleiotropic • polygenic : multiple genes affect a single trait • pleiotropic : one gene affects multiple traits • Note: pure polygenic/pleiotropic (without environmental • influences) hardly exist • Complex Trait: polygenic- and pleiotropic gene-environment interaction • Examples: stature, atherosclerosis, blood pressure regulation, and many • many more.

  13. Context effect of genetic variance in complex traits 2 SNPs (4 alleles, 9 possible combinations) 1,00 1,27 1,30 1,34 1,24 250 200 Count 150 100 50 2,00 1,33 1,39 1,33 1,32 3,00 1,40 1,39 1,40 1,41 250 200 Count 150 100 50 1,00 2,00 3,00 HDL

  14. Power drainage One SNP Two SNPs Three SNPs

  15. Methods (1) • We need methods to: • Preserve power • Reduce noise • Lift shadows of stronger determinants

  16. FGClustor Hypothesis driven Exploration via FGClustor Conceptual thinking: Given any outcome parameter measured in a population one is able to detect differences in frequency of a combination of geno- or phenotypes along the range of the parameter when compared to the prevalence of that combination in the whole population.

  17. FGClustor FGClustor principle y f1a a f2a f3a f4a f1b b f2b f3b f4b f1c f2c f3c f4c c d f1d f2d f3d f4d HDL-c f1e e f2e f3e f4e Frequency of combination n Frequency of combination 1 Frequency of combination 2 Frequency of combination 3 f f1f f2f f3f f4f g f1g f2g f3g f4g h f1h f2h f3h f4h 0 0 combinations combinations

  18. FGClustor

  19. FGClustor

  20. FGClustor FGClustor and strong confounders Phenotype: Systolic blood pressureComplex Trait: Quartiles Cholesterol + Gender Chi-square Test M1 M2 M3 M4 F2 F3 F4 F1

  21. FGClustor FGClustor and SNPs Phenotype : HDL-cholesterolComplex Trait: SNP1 + SNP2 Chi-square Test ABCC ABDD AACC

  22. FGClustor Summary Pro: • FGClustor used in hypothesis driven approach can shed light on relationships of covariates of interest • FGClustor can visualize context-based main effects of parameters of interest • “Standard” statistical methods are needed in conjunction with FGClustor output to confirm context-based main effects Con: • FGClustor is not statistically powerful

  23. MDR Multifactor Dimensionality Reduction • What is MDR? • Nonparametric and genetic model-free • Alternative to logistic regression • Detecting nonlinear interactions among discrete genetic • and environmental attributes. • The MDR method combines • attribute selection, • attribute construction, • classification, • cross-validation and • visualization • http://www.epistasis.org/mdr.html Moore (Expert Review of Molecular Diagnostics, 4:795-803, 2004)

  24. MDR Worked example: SBP (dichotomous by median) Covariates: Sex and Quartiles of Total cholesterol

  25. MDR MDR Worked example: SBP (dichotomous by median) Covariates: Sex and Quartiles of Total cholesterol Best Model output:

  26. MDR MDR Worked example: HDL-c (dichotomous by <= 1 mmol/L) Covariates: SNP1 SNP2 Best Model output: AACC ABCC ABDD

  27. MDR Summary Pro: • Includes cross-validation in the same population • Can be used as dataminer, not necessarily hypothesis driven • Statistically powerful to uncover also weak (genotype) effects Con: • Can be used as dataminer, not necessarily hypothesis driven • Limited by categorical data only

  28. Discussion • Standard methods in genetic epidemiology only show very strong association in case of direct or extremely close relationship between gene and outcome parameter of interest. • Complex traits are build of individual contributors (genetic variants, environmental parameters) that each in itself have a weak main effect on the trait. • Noise and strong confounders limit detection of the weaker contributors in complex traits by standard statistics • Main effects of the individual contributors can be visualized using novel tools (e.g. FGClustor, MDR) in a context dependent approach at the background of solid hypothesis

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