1. Guimin Gao, Wenan Chen, & Xi Gao Department of
Biostatistics, VCU Association Tests for Rare Variants Using
Sequence Data
2. Introduction to Association tests: two hypotheses Common
variant-common disease Common variant: Minor allele frequencies
(MAF) >= 5% Using linkage disequilibrium(LD) Rare variant-common
disease Rare variant: MAF < 1% (or 5%) High allelic
heterogeneity: collectively by multiple rare variants with moderate
to high penetrances Associations through LD would not be suitable
3. Association tests for Common variants Test a single marker each
time Cochran-Armitages trend test (CATT) (assuming additive (ADD))
Power: High for additive (ADD) or Multiplicative (MUL); low
recessive (REC) or Dominant (DOM) Genotype association test (GAT)
using chi-square statistic Power: a little lower for ADD, higher
for REC MAX3 = maximum of three trend test statistics across the
REC, ADD, and DOM models (Freidlin et al. 2002 Hum Hered.) Power:
lower than CATT under ADD higher than CATT & CAT under REC 4.
Association tests for Common variants Test for single marker (CATT,
GAT, & MAX3) Low power when MAF CAST (WSM) > CMC may not be
true in other situations Can be applied to rare variants &
common variants Disadvantage: Give very high weights to very rare
alleles (singleton), very low weights to common variants. 13. An
evaluation of the CMC method and Weighted sum method by using GAW
17 data Both methods are powerful (based on the authors simulation)
Our evaluation based on simulated datasets from GAW 17 GAW 17 data:
a subset of genes with real sequence data available in the 1000
genome project Simulated phenotypes Unrelated individuals, families
Dataset of 697 unrelated individuals 24487 SNPs in 3205 genes from
22 autosomal chromosomes Only test for the 2196 genes with
non-synonymous SNPs 14. GAW 17 dataset of unrelated individuals
Four phenotypes: Q1, Q2, Q4 and disease status. Q1, Q2, and Q4 are
quantitative traits Q1 associated with 39 SNP in 9 genes, Q2
associated with 72 SNPs in 13 genes Q4: not related to any genes
Disease status is a binary trait: affected or unaffected,
associated with 37 genes 200 simulated phenotype replicates Only
one replicate of genotype data (original data) 15. Methods:
case-control design Transform Q1, Q2, Q4 into binary traits
Splitting at the top 30% percentile of the distributions
Transforming Phenotypes 16. Criteria for evaluation of Tests
Familywise error rate (FWER) 2196 genes with non-synonymous SNPs,
2196 tests 2196 null hypotheses Hj0: gene not associated with the
trait Q1 associated in 9 genes, 9 null hypotheses are not true.
(2196-9) null hypotheses are true FWER = Pr(reject at least one
true null hypothesis) = Nf/200 Nf : No. of replicates, at least one
true hypothesis are rejected Average Power Mean of power for all
the 9 genes that affect the phenotypes Evaluating power: Q1, Q2,
Disease 17. Distribution of MAF in the GAW 17 dataset Figure 1.
Distribution of MAF of 24487 SNPs in GAW 17 18. Figure 1. Group
SNPs based on MAFs for CMC 0 - 0.01 0.01 - 0.1 >=0.1 Similar to
Madsen & Browning (2009) 19. Table 1: Average power Traits CMC
method Weighted sum method Q1 0.144 0.112 Q2 0.00615 0.00308
Disease 0.00444 0.00500 20. Table 2: FWER (nominal = 0.05) Trait
CMC method Weighted sum method Q4 0.115 0.0100 CMC has FWER
inflation Population stratification or admixture, Samples from
Asian, Europe, Relatedness among samples Similar results in Power
and FWER were reported at GAW 17 21. Variable-Threshold Approach
(Price et al 2010) Given a threshold T, calculate a score for indiv
j Iij = 0, 1, 2, the count of the minor allele of indiv i at locus
j Calculate the sum of score for cases: Calculate Z(T) =
V(T)/Var(V(T)) Find T to maximize Z(T), Zmax = max (Z(T))
Permutation to estimate p-value for Zmax Power: >CMC; Extended
to quantitative traits L i ijj ITV 1 )( DN j j TVTV 1 )()( 22. A
weighted approach (Price et al 2010) Calculate a weighted score for
indiv j Iij = 0, 1, 2 Calculate the sum of score for cases Possible
weight Power: similar to the weighted sum method (Madsen &
Browning 09) L i ijij IwV 1 )1(/1 iii qqw DN j jVV 1 23. A weighted
approach (Price et al 2010) Calculate the sum of score for cases
Iij = 0, 1, 2 Calculate weight by the prediction of functional
effects PolyPhen-2 is used to predict damaging effects of missense
mutations with probabilistic scores. Probabilistic scores as
weights may reduce the noise of non-functional variants. Higher
Power than other methods L i ijij IwV 1 DN j jVV 1 24. A
data-adaptive sum test (Han & Pan 2010, Hum Hered) Logistic
model xij = 0, 1, 2, the count of the minor allele of indiv i at
locus j Effect on opposite directions If j
