The Complexities of Data Analysis in Human Genetics

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The Complexities of Data Analysis in Human
Genetics

• Marylyn DeRiggi Ritchie, Ph.D.
• Center for Human Genetics Research
• Vanderbilt University
• Nashville, TN

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Biology is complex
BioCarta
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(No Transcript)
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Single nucleotide polymorphisms (SNPs)
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Mendelian Traits
affected
Aa
Aa
BB
bb
Locus 2
Aa
AA
Aa
BB
bb
Bb
BB
Bb
bb
AA
AABB
AABb
AAbb
affected
Aa
Locus 1
AaBB
AaBb
Aabb
affected
aa
aaBB
aaBb
aabb
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Complex Traits
Aa
Aa
BB
Bb
Locus 2
BB
Bb
bb
aa
AA
Aa
BB
bb
Bb
AA
AABB
AABb
AAbb
Aa
Locus 1
AaBB
AaBb
Aabb
affected
aa
aaBB
aaBb
aabb
affected
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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

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Genetic Analysis

• Two main areas of genetic analysis
• Linkage analysis
• Association analysis
• Methods have been developed for each approach for
  a variety of different study designs

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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.

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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

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Case-control data structure
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Association Analysis

• Single marker tests
• Haplotype association
• Epistasis

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Single marker tests
?
?
?
?
SNP1
SNP2
SNP3
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Haplotype
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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

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Haplotype Analysis
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Epistasis Gene-Gene InteractionsW. Bateson,
Mendels 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).

Cordell, Human Molecular Genetics 112463-8 (2002)
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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

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Curse of Dimensionality
N 100
50 Cases, 50 Controls
SNP 1
AA
Aa
aa
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Curse of Dimensionality
N 100
50 Cases, 50 Controls
SNP 1
BB
SNP 2
Bb
bb
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Curse of Dimensionality
N 100
50 Cases, 50 Controls
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Three Other Issues to Consider

• 1. Variable selection
• Model selection
• Interpretation

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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

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How many combinations are there?

• 500,000 SNPs span 80 of common variation in
  genome (HapMap)

Number of Possible Combinations
SNPs in each subset
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How many combinations are there?

• 500,000 SNPs span 80 of common variation in
  genome (HapMap)

Number of Possible Combinations
SNPs in each subset
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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

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Finding the best model
Choose variable subset Choose statistical
model Evaluate model fitness Best model
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Simple Fitness Landscape
Fitness
Model
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Complex Fitness Landscape
Fitness
Model
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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

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3. Interpretation

• Strategies to assess biological interpretation of
  gene-gene interaction models
• Consider current knowledge about the biochemistry
  of the system and the biological plausibility of
  the models
• Perform experiments in the wet lab to measure the
  effect of small perturbations to the system
• Computer simulation algorithms to model
  biochemical systems

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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

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Heterogeneity
Thornton-Wells TA, Moore JH, Haines JL. Trends in
Genetics, 200420(12)640-7. .

• 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

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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)

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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