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Assessment of genomewide association studies Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia.

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Presentation on theme: "Assessment of genomewide association studies Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia."— Presentation transcript:

1 Assessment of genomewide association studies Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia

2 WHICH GENES ? Gene variants ?

3 False positive problem

4 Candidate gene studies: reproducibility problem 600 positive associations between common gene variants and disease reported 1986-2000 J N Hirschhorn et al. Genetics in Medicine 2002 166 were studied 3+ times 6 have been consistently replicated

5 Introduction to genomewide association studies

6 Genomewide association studies (GWA) Revolution in gene search Hypothesis-free driven approach Scan 100,000-500,000 gene variants (SNPs) Case – control design (>1000 individuals) Massive number of tests of hypothesis

7 Recent GWA studies in osteoporosis Styrkarsdottir U, et al (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355- 2365. van Meurs JB, et al (2008) Large-scale analysis of association between LRP5 and LRP6 variants and osteoporosis. JAMA 299:1277-1290. Richards JB, et al (2008) Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371:1505-1512.

8 Some gene variants from GWA Gene variant (SNP)Gene or locationTrait and P-value rs373622811q13 (LRP5)BMD (p = 2.6 × 10 -9 ) Fracture (p = 0.02) rs373622811q13 (LRP5)BMD (p = 6.3 × 10 -12 ) Fracture (p = 0.002) rs4355801LRP5 rs498832111q13 (LRP5)BMD (p = 3.3 × 10 -8 ) Fracture (p = 0.002) rs230268512p12 (LRP6)BMD (p = 0.97) Fracture (p = 0.95) rs43558018q24 (TNFRSF11B)BMD (p = 7.6 × 10 -10 ) rs75241021p36 (ZBTB40)BMD (p = 9.2 × 10 -19 ) Fracture (p = 8.4 × 10 -4 ) rs66969811p36 (close to ZBTB40)BMD (p = 1.7 × 10 -7 ) Fracture (p = 2.4 × 10 -4 ) rs31303406p21 ()BMD (p = 1.2 × 10 -7 ) Fracture (p = 0.008) rs94790556q25 (1)BMD (p = 6.2 × 10 -7 ) rs48700446q25 (1)BMD (p = 1.6 × 10 -11 ) rs10383046q25 (1)BMD (p = 4.0 × 10 -11 ) rs69291376q25 (1)BMD (p = 2.5 × 10 -10 ) rs19998056q25 (1)BMD (p = 2.2 × 10 -8 ) rs69938138q24 (OPG)BMD (p = 1.8 × 10 -14 ) Fracture (p = 0.04) rs64698048q24 (OPG)BMD (p = 7.4 × 10 -15 ) Fracture (p = 0.052) rs959473813q14 (RANKL)BMD (p = 2.0 × 10 -21 ) rs959475913q14 (RANKL)BMD (p = 1.1 × 10 -16 ) rs118985052p16 (SPTBN1)Fracture (p = 1.8 × 10 -4 ) rs301836218q21 (RANK)Fracture (p = 0.005) rs230603311p11 (LRP4)Fracture (p = 0.007) rs793534611p11 (LRP4)Fracture (p = 0.02)

9 What is the credibility of a GWA finding ?

10 An observed association with p<0.05 does not necessarily mean that the association exists. In 100,000 tests, 5000 positive findings could be false positive

11 Diagnostic test and association test Diseased YES +ve-ve NO +ve-ve Association True +ve-ve False Sensitivity P(+ve | D) False +vePowerP-value P(+ve | False) +ve -ve

12 What do want we to know? Probability of association given observed data (eg posterior probability of association) or Probability of observing data if there is no association (P-value)

13 Posterior probability of association Prior probability of association (  ) Power = Pr(significance | association) Sample size P-value = Pr(significance | no association) Effect size is a function of

14 What is the prior probability of association for a gene variant ?

15 Gene search = finding small needles in a VERY large haystack Human genome ~3 billion base pairs long BUT: Most are vanishingly rare 99.9% identical between any two individuals ~90% differences between any two individuals is due to common variants

16 Hypotheses Common disease / common variants (CD/CV) ( Reich & Lander 2001, Pritchard et al 2005 ) ~90% differences between any two individuals is due to common variants

17 Prior probability of association (  ) Common variants in the human population: 10 million ( Kruglyak and Nickerson Net Gent 2001 ) No. of genetic variants associated with a common disease ~100 or less ( Yang et al, Int J Epidemiol 2005) Prior probability of association  = 0.000001

18 A Bayesian interpretation of association 10,000,000 common variants True association (100) No association (9,999,900) Significant (95) Non-significant (5) Significant (100) Non-significant (9,999,800) P(True association given a significant result) = 95 / (95+195) = 48% Power = 95%; P-value=0.00001

19 A Bayesian interpretation of association 10,000,000 common variants True association (100) No association (9,999,900) Significant (95) Non-significant (5) Significant (1) Non-significant (9,999,800) P(True association given a significant result) = 95 / (95+1) = 99% Power = 95%; P-value=0.00000001

20 P-value and “true” association P-value in the range of 5% - 0.1% will virtually be false positives even in large scale studies

21 P-value for a reliable association P < 5 x 10 -5 or P < 5 x 10 -8 For 1000 cases and 1000 controls, p< 10 -8 are more likely to be true than false

22 Some gene variants from GWA Gene variant (SNP)Gene or locationTrait and P-value rs373622811q13 (LRP5)BMD (p = 2.6 × 10 -9 ) Fracture (p = 0.02) rs373622811q13 (LRP5)BMD (p = 6.3 × 10 -12 ) Fracture (p = 0.002) rs4355801LRP5 rs498832111q13 (LRP5)BMD (p = 3.3 × 10 -8 ) Fracture (p = 0.002) rs43558018q24 (TNFRSF11B)BMD (p = 7.6 × 10 -10 ) rs75241021p36 (ZBTB40)BMD (p = 9.2 × 10 -19 ) Fracture (p = 8.4 × 10 -4 ) rs94790556q25 (1)BMD (p = 6.2 × 10 -7 ) rs48700446q25 (1)BMD (p = 1.6 × 10 -11 ) rs10383046q25 (1)BMD (p = 4.0 × 10 -11 ) rs69291376q25 (1)BMD (p = 2.5 × 10 -10 ) rs19998056q25 (1)BMD (p = 2.2 × 10 -8 ) rs69938138q24 (OPG)BMD (p = 1.8 × 10 -14 ) Fracture (p = 0.04) rs64698048q24 (OPG)BMD (p = 7.4 × 10 -15 ) Fracture (p = 0.052) rs959473813q14 (RANKL)BMD (p = 2.0 × 10 -21 ) rs959475913q14 (RANKL)BMD (p = 1.1 × 10 -16 ) rs118985052p16 (SPTBN1)Fracture (p = 1.8 × 10 -4 ) rs301836218q21 (RANK)Fracture (p = 0.005) rs230603311p11 (LRP4)Fracture (p = 0.007) rs793534611p11 (LRP4)Fracture (p = 0.02)

23 Number of individuals needed to screen in population and family Hypothetical geneFracture risk in PopulationFamily Relative risk510 Cumulative risk40%80% Cumulative risk after Rx20%40% Number needed to treat52.5 Frequency of risk “genotype” 0.2%50% Number needed to screen25005

24 How many genes are required for a “good” fracture prognosis ? Odds ratio Genotype frequency Number of genes needed for AUC of 0.700.800.900.95 1.15%>400 10%330>400 30%150>400 1.55%33100280>400 10%1950150330 30%92370160

25 Assessment of GWA finding Genetic components of BMD and fracture Finding genes of osteoporosis: a challenge Genes can help improve the prognosis of fracture

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