PREDICTION MODELS USING GENOMIC PROFILINGH. Zhang

E. Warner

D. Zhao

GENOMIC PROFILINGTesting genes at multiple loci simultaneously

Complex diseases-complex causal pathway

High number of weak predictors Single genes–of limited predictive value

QUESTIONS OF INTERESTIs testing low-risk genes at multiple loci useful clinically -- discriminative accuracy?

Can we predict individual genetic risk from GWAS?

Value for assessing susceptibility to common diseases and targeting interventions?

PREDICTIVE TESTING FOR COMPLEX DISEASES USING MULTIPLE GENES: FACT OR FICTION?

Cecile J. W. Janssens, Yurii S. Aulchenko, Stefano Elefante, Gerard J J. M Borsboom, Ewout W. Steyerberg, Cornelia M van Dujuin

Genet Med 2006: 8(7):395-400

METHODSSimulation test: 100,000 subjects up to 400 genes Each gene: two alleles Hardy-Weinberg Equilibrium Disease risk associated with genetic profiles:

Bayes’ theorem Multiplicative model No LD between genes All genes predictive of the disease are known

METHODS (CONTINUED)

Part I: genes had the same risk allele frequencies and the same effect on the disease (same ORs)

Part II: genes with varying ORs and allele frequencies

DISCRIMINATIVE ACCURACY

AUC (Area Under the ROC Curve)

Probability that the test correctly identifies the diseased subject

EXAMPLES OF AUC

FIG 1. DISCRIMINATIVE ACCURACY OF

GENETIC PROFILING (CONSTANT ORS)

FIG 2. DISCRIMINATIVE ACCURACY OF GENETIC PROFILING (VARYING ORS)

FIG. 3. RELATIONSHIP BETWEEN HERITABILITY AND DISCRIMINATIVE ACCURACY

CONCLUSIONS Discriminative accuracy depends on

Number of genes Frequency of risk alleles Risk associated with the genotypes Heritability (few strong predictors or large

number of common susceptibility genes)

Level of discriminative accuracy required for clinical application depends on the goal of testing, burden of disease, cost, treatment availability etc.

PREDICTION OF INDIVIDUAL GENETIC RISK TO DISEASE FROM GENOME-WIDE ASSOCIATION STUDIES

Naomi R. Wray, Michael E. Goddard and Peter M Visscher

Genome Res. 2007 17: 1520-1528

PURPOSE • Research Question

– Can we identify high risk genetic profiles consisting of multiple risk alleles with small effects at any given locus?

• Aims– Investigate the relationship between the RR of

genetic loci and the number of loci that contribute to disease risk

– Investigate the number of loci underlying complex disease of a given disease prevalence and heritability

– Simulate a case control study to investigate the prediction of genetic risk of disease from multiple loci in a genome wise association study (GWAS)

– Use SNPs selected from the simulation to see how accurately they predict the risk in a random sample

METHODS• Repeated simulations• Parameters

– Disease prevalence: 0.05 or 0.10– Heritability: 0.1 or 0.2– Allele frequency distribution: uniform

(common disease-common variant) or U-shaped (neutral allele hypothesis)

– GWAS• 500,000 SNPs• Number of disease risk loci: 10, 20, 50, 100, 300,

1000• 1000 or 10,000 cases and controls

RESULTS: # OF LOCI AND AVERAGE RELATIVE RISK

RESULTS: RISK ALLELE MODELS

RESULTS: SELECTED SNPS

RESULTS: ACCURACY

ASSUMPTIONS AND LIMITATIONS• True causal SNPs were always included in

GWAS• All genetic variance was attributable to

variants of frequency 0.01 to 0.99• No population stratification • All genotypes are in Hardy-Weinberg

equilibrium• No LD between SNPs• Did not consider gene-gene or gene-

environment interactions

CONCLUSIONSPrediction of genetic risk is

possible, even if there are hundreds of risk variants, each of small effect

Genomic profiling may not be appropriate for rare diseases

Implementation of these procedures doesn’t require knowledge of causal mechanism

AN EPIDEMIOLOGIC ASSESSMENT OF GENOMIC PROFILING FOR MEASURING SUSCEPTIBILITY TO COMMON DISEASES AND TARGETING INTERVENTIONS

Muin J. Khoury, Quanhe Yang, Marta Gwinn, Julian Little, W. Dana Flanders

Genet Med 2004:6(1):38-47

PURPOSE Normal epidemiological methods

already provide information about important exposure-disease associations that can be used to reduce disease burden

What value does genomic profiling/genetic testing to predict susceptibility add to usual epidemiological methods?

TWO ASPECTS OF “VALUE” Clinical value – individual level

Clinical validity – can genetic testing help predict future disease positive and negatives?

Clinical utility – can genetic testing help lower disease risks for people with a “positive” genetic test

See the task force on genetic testing and the Secretary's Advisory Committee on Genetic Testing – references in paper

Public health value – population level Public health utility – how does reduction of disease

burden in population based on genetic profiling compare to population-wide interventions

GENERAL METHODS Model: Risk = Baseline + Gene1 + Gene2 +

Gene3 + Modifiable exposure Posited hypothetical but “likely” data by

varying the following parameters: Lifetime risk of a disease Number of loci in genetic test Frequency of genotypes Strength of association between these loci and

the disease Strength of association between exposure and

the disease Calculated value for these hypothetical

data by calculating impact of targeted intervention on the exposure

CLINICAL VALIDITY AND UTILITY

Technical validity is assumed

CLINICAL VALIDITY AND UTILITY

Technical validity is assumed

PUBLIC HEALTH UTILITY

Calculate the ratio of PAFt – reduction of disease burden due to targeted intervention – to PAF

PUBLIC HEALTH UTILITY

Calculate the ratio of PAFt – reduction of disease burden due to targeted intervention – to PAF

IMPLICATIONS There are other parameters that could be

varied: Higher synergy will lead to higher predictive

values and population impact Epistasis will lead to higher predictive values

Tension between targeted and population interventions Screening may be a good compromise:

population-wide intervention of education and awareness + targeted intervention

Genetic testing has different added values under different conditions and epidemiological methods can be used to determine the extent of its added value

SUMMARYGenetic tests should involve multiple lociDiscriminative accuracy improves with

higher heritabilityNumber of loci needed to accurately

identify associationFunction of heritability, prevalence and RR

Complicated relationships between accuracy, allele effect sizes and allele frequency

SUMMARYThe accuracy of the predictive

models in the presence of gene-gene/environment interactions may be overstated

Genetic testing must be applied to all subjects and can be resource-intensive