Download Common trait genetics

Common trait genetics
Chris Cotsapas
Traits and (sub?)phenotypes
• Categorical
– Disease/healthy
– Tall/medium/short
– Super-skinny/skinny/normal/fat/super-fat
• Continuous
– Weight, height, BMI, WHR, LDL
– Gene expression, methylation, DNase I
– EDSS, MRI lesion volumes, antibody titers,
pSTAT3 in Th17 cells
Heritability
• Proportion of differences due to genetic
factors
– From family and twin studies
• Broad sense (H2)
– Additive, dominant and epistatic
• Narrow sense (h2)
– Additive only
• How many genetic factors explain
heritability?
– Mendel v Galton
Mendelian – one variant, “all” H2/h2
Necessary and sufficient
(expressivity,
penetrance)
Galton - multigenicity
Multigenicity
Multigenic
0.05
0.10
% variance explained
0.4
0.00
0.2
0.0
% variance explained
0.6
0.8
0.15
Oligogenic
0
5
10
Number of risk variants
15
20
0
5
10
Number of risk variants
15
20
GENETIC STUDY DESIGN
Family vs cohort
Genetic data
SNP
Ind1
Ind2
Ind3
Ind4
1
AA
AG
AA
GG
2
TC
CC
CC
CC
3
GT
GG
TT
GG
4
AC
CC
AC
AC
5
AT
AA
TT
AA
Linkage analysis and TDT
Case/control association
• H0: Frequency of ‘A1’ is independent of case/control status.
A1
A2
Cases
w
x
Controls
y
z
c2 = (O-E)2/E
[Pearson’s chi-Square]
Odds Ratio (OR): Odds of Allele
occurring in cases to the odds of
Allele occurring in controls:
w/x
y/z
=
wz
xy
Power
Small sample size
Large sample size
Freq
Freq
Cases
Controls
Cases
Controls
Regression analysis
• Analysis of the relationship between a dependent or outcome
variable (phenotype) with one or more independent or
predictor variables (SNP genotype)
Yi = b0 + b1Xi + ei
Continuous Trait Value
Linear Regression Equation
Slope: b1
b0
Logistic Regression Equation
pi
ln (1-pi) = b0 + b1Xi + ei
(
)
0
1
Number of A1 Alleles
2
Manhattan plots
META-ANALYSIS
Combining data – meta-analysis
Replication cohort
GWA
S
GWAS
GWAS
We can simply combine statistics
• Fisher’s method:
• Sum of
χ2
k = number of studies
These give similar, though not
Identical answers for P-values
We can calculate meta-statistics
POWER
Meta Z
Sum from 1 to m
Where m is # Z’s
Each study Z
wi is each test weight
wt is the sum of the weights
We can work on betas too…
• For regression we can get a test statistic
from the beta
Regression estimat
Test statistic
Variance of the beta
WARNING: must be same phenotype and scale (e.g. height in cm across all
studies)
For meta-analysis
Bi = study beta; σ2 = variance of each individual betas
OBSERVATIONS
http://www.ebi.ac.uk/fgpt/gwas/images/timeseries/gwas-latest.png
Effect sizes – T1D
Petretto, Liu and Aitman NG 2007
MS GWAS risk effect: NFKB1 locus
97 MS risk loci;
IMSGC, Nat Genet 2013
MS patients show altered NFκB
+
signaling in CD4 T cells
Figure 1. Naïve CD4 cells from patients with MS
exhibit increased phospho-p65 NFκB. Flow
cytometry of PBMCs from age-m atched healthy
+ T cells show
control
(HC) CD4
and relapsing-remitting
MS (RRM
S)
ex vivo
higher
patients stained for CD4, CD45RA, CD45RO, and
p-p65
submitted)
pS529
p65 (Housley
NFκ B. M FI ofet
p65al,
results
are shown
+
+
gated on naïve CD 4 CD45RA CD45RO T-cells.
CD4+ T cells from MS patients
proliferate more rapidly after
stimulus (Kofler et al JCI 2014)
MS risk effect near NFKB1 alters
signaling in CD4+ cells
Nuclear localization
rs228614
p= 0.037
p50 NFkB
30
20
10
0
Housley, submitted
GG
AA
30
p= 0.05
GG
AA
20
10
0
0
15
30
Minutes
GWAS loci harbor many NFκB
genes
Will Housley, David Hafler
Model: NFκB signaling variation
p50
External
stimulus
P-p50
p65
*NFκB
Activation
Proliferation
Survival
*NFκB
Activation
Proliferation
Survival
Broader
phenotype?
p50
P-p50
GV in NFκB pathway
New gene activation
patterns by NFκB
GV in NFκB TFBS
p65