the complexities of data analysis in human genetics marylyn deriggi ritchie, ph.d. center for human...
TRANSCRIPT
The Complexities of Data Analysis in Human Genetics
Marylyn DeRiggi Ritchie, Ph.D.Center for Human Genetics Research
Vanderbilt UniversityNashville, TN
Biology is complex
BioCarta
Single nucleotide polymorphisms (SNPs)
Mendelian Traits
Aa Aa
Aa
BB bb
AA
aa
BB Bb bbAa AA AaBB bb Bb
Locus 1
Locus 2
AABB AABb AAbb
AaBB AaBb Aabb
aaBB aaBb aabb
affected
affected
affected
Complex Traits
Aa Aa
Aa
BB Bb
AA
aa
BB Bb bbaa AA AaBB bb Bb
Locus 1
Locus 2
AABB AABb AAbb
AaBB AaBb Aabb
aaBB aaBb aabb
affected
affected
Complex Traits
• Complex trait implies the involvement of multiple genes and/or environmental factors
• Mendelian trait implies a single mutation
• Mendelian traits are generally rare
• Complex traits are common and of substantial public health impact
Genetic Analysis
• Two main areas of genetic analysis1. Linkage analysis
2. Association analysis
• Methods have been developed for each approach for a variety of different study designs
Association Analysis
• In disease studies, when the disease gene is unknown, we look for association between genetic markers and the disease
• If a marker occurs more frequently or less frequently in affected individuals than in unaffected individuals, then it is associated with the disease.
Association Analysis
• Case-control studies– Test for association between marker alleles
and the disease phenotype in a group of affected and unaffected individuals randomly from the population
• Family-based studies– Test for association between marker alleles
and the disease phenotype in a group of affected individuals and unaffected family members
Case-control data structureStatus SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 SNP9 SNP10
1 1 2 2 1 2 1 2 2 1 2
1 0 0 0 1 0 0 0 0 1 0
1 0 2 0 1 1 0 2 0 1 1
1 2 0 1 1 0 2 0 1 1 0
1 2 1 1 0 0 2 1 1 0 0
1 1 0 0 0 0 1 0 0 0 0
1 1 1 0 1 2 1 1 0 1 2
1 1 0 1 0 2 1 0 1 0 2
1 0 0 0 2 0 0 0 0 2 0
1 0 0 1 0 1 0 0 1 0 1
0 2 1 0 1 0 2 1 0 1 0
0 0 1 1 0 0 0 1 1 0 0
0 1 1 0 2 1 1 1 0 2 1
0 0 0 2 0 1 0 0 2 0 1
0 2 1 0 1 1 2 1 0 1 1
0 0 0 2 0 0 0 0 2 0 0
0 1 0 0 1 2 1 0 0 1 2
0 0 1 1 1 2 0 1 1 1 2
0 1 1 0 0 2 1 1 0 0 2
0 0 1 2 0 0 0 1 2 0 0
Association Analysis
• Single marker tests
• Haplotype association
• Epistasis
Single marker tests
SNP1
Disease DiseaseDisease
? ? ?
SNP2 SNP3
Haplotype
Haplotype Analysis
• May be able to increase power by testing for association with marker haplotype
• Haplotype is a block of DNA that stays intact through generations
• Do not directly observe marker haplotypes
• Use likelihood methods to infer
Haplotype Analysis
Epistasis: Gene-Gene InteractionsW. Bateson, Mendel’s Principles of Heredity (1909)
A.R. Templeton, In: Wade et al. (eds), Epistasis and the Evolutionary Process (2000)
• Epistasis first used by William Bateson (1909) • Literal translation is “standing upon” (I.e. one gene
masks the effects of another gene).
Genotype at Locus A
Genotype at Locus B
BB Bb bb
AA White Grey Grey
Aa Black Grey Grey
Aa Black Grey Grey
Cordell, Human Molecular Genetics 11:2463-8 (2002)
Gene-gene Interactions
• Searching for gene-gene interactions brings about a whole new suite of problems and challenges
• Types of interactions– Additive– Multiplicative– Epistatic
• Curse of dimensionality – big problem
Curse of Dimensionality
AA Aa aa
SNP 1
N = 100 50 Cases, 50 Controls
SNP 2
AA Aa aa
BB
Bb
bb
N = 100 50 Cases, 50 Controls
SNP 1
Curse of Dimensionality
N = 100
50 Cases, 50 Controls
AA Aa aaBBBbbb
CC Cc cc
DD
Dd
dd
AA Aa aaAA Aa aa
BBBbbb
BBBbbb
SNP 1 SNP 1 SNP 1
SN
P 2
SN
P 2
SN
P 2
SN
P 4
SNP 3
Curse of Dimensionality
Three Other Issues to Consider
1. Variable selection
2. Model selection
3. Interpretation
1. Variable Selection
• How can you determine which variables to select?
• Not computationally feasible to evaluate all possible combinations
• Need to select correct variables to detect interactions
How many combinations are there?• ~500,000 SNPs span 80% of common variation in genome (HapMap)
SNPs in each subset
1 2 3 4 5
5 x 105
2 x 1016
1 x 1011
3 x 1021
2 x 1026
Num
ber
of P
ossi
ble
Com
bina
tions
How many combinations are there?• ~500,000 SNPs span 80% of common variation in genome (HapMap)
SNPs in each subset
1 2 3 4 5
5 x 105
2 x 1016
1 x 1011
3 x 1021
2 x 1026
Num
ber
of P
ossi
ble
Com
bina
tions 2 x 1026 combinations
* 1 combination per second
* 86400 seconds per day
---------
2.979536 x 1021 days to complete
(8.163113 x 1018 years)
2. Model Selection
• For each variable subset, evaluate a statistical model
• Goal is to identify the best subset of variables that compose the best model
Finding the best model
Choose variable subset
Choose statistical model
Evaluate model fitness
Best model
Simple Fitness Landscape
Model
Fitn
ess
Complex Fitness LandscapeF
itnes
s
Model
3. Interpretation
• Selection of best statistical model in a vast search space of possible models
• Statistical or computational model may not translate into biology
• May not be able to identify prevention or treatment strategies directly
• Wet lab experiments will be necessary, but may not be sufficient
3. Interpretation
• Strategies to assess biological interpretation of gene-gene interaction models
1. Consider current knowledge about the biochemistry of the system and the biological plausibility of the models
2. Perform experiments in the wet lab to measure the effect of small perturbations to the system
3. Computer simulation algorithms to model biochemical systems
Additional Challenges(true of all association studies)
• Sample size and power/type I error
• Population specific effects– Age, gender
• Poorly matched cases and controls– Ethnic background– Controls must be “at risk”
• Bias
• Heterogeneity
Heterogeneity
• Phenotypic (Clinical, Trait)– Affected individuals vary in clinical expression
• Genetic– Different inheritance patterns for same disease
• Locus– Different genes lead to the same disease
• Allelic– Different alleles at the same gene lead to
same/different disease
Thornton-Wells TA, Moore JH, Haines JL. Trends in Genetics, 2004;20(12):640-7. .
New Statistical Approaches• Data Reduction
– Combinatorial Partitioning Method (CPM)– Multifactor Dimensionality Reduction (MDR)– Detection of informative combined effects (DICE)– Logic Regression– Set Association Analysis
• Pattern Recognition– Symbolic Discriminant Analysis (SDA)– Cellular Automata (CA)– Neural Networks (NN)
Areas of Future Work(possible collaborations)
• More analytical methods for gene-gene and gene-environment interactions– Especially including categorical and
continuous variables simultaneously
• Inclusion of pathway information into analyses
• Ways of dealing with heterogeneity of all kinds