Guimin Gao, Wenan Chen, & Xi Gao Department of
Biostatistics, VCU Association Tests for Rare Variants Using
Sequence Data Slide 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 Slide 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 Slide 4 Association tests for Common
variants Test for single marker (CATT, GAT, & MAX3) Low power
when MAF Weighted Sum Method (Madsen & Browning 09) Power
comparison: Simulations assuming genotypic relative risk is
proportion to MAF at disease loci (Madsen & Browning 09)
Weighted Sum Method (WSM) > CMC > 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. Slide 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 Slide 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) Slide 15 Methods:
case-control design Transform Q1, Q2, Q4 into binary traits
Splitting at the top 30% percentile of the distributions
Transforming Phenotypes Slide 16 Criteria for evaluation of Tests
Familywise error rate (FWER) 2196 genes with non-synonymous SNPs,
2196 tests 2196 null hypotheses H j0 : 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) = N f /200 N f : 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 Evaluate FWER: Q4 Slide 17 Distribution of MAF in the
GAW 17 dataset Figure 1. Distribution of MAF of 24487 SNPs in GAW
17 Slide 18 Figure 1. Group SNPs based on MAFs for CMC 0 - 0.010.01
- 0.1>=0.1 Similar to Madsen & Browning (2009) Slide 19
Table 1: Average power TraitsCMC methodWeighted sum method
Q10.1440.112 Q20.006150.00308 Disease0.004440.00500 Slide 20 Table
2: FWER (nominal = 0.05) TraitCMC methodWeighted sum method
Q40.1150.0100 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 Slide 21
Variable-Threshold Approach (Price et al 2010) Given a threshold T,
calculate a score for indiv j I ij = 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), Z
max = max (Z(T)) Permutation to estimate p-value for Z max Power:
>CMC; Extended to quantitative traits Slide 22 A weighted
approach (Price et al 2010) Calculate a weighted score for indiv j
I ij = 0, 1, 2 Calculate the sum of score for cases Possible weight
Power: similar to the weighted sum method (Madsen & Browning
09) Slide 23 A weighted approach (Price et al 2010) Calculate the
sum of score for cases I ij = 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 Slide 24 A
data-adaptive sum test (Han & Pan 2010, Hum Hered) Logistic
model x ij = 0, 1, 2, the count of the minor allele of indiv i at
locus j Effect on opposite directions If j
