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Study Designs in GWAS. Jess Paulus, ScD January 30, 2013. Today’s topics. Case-control studies Population based Hospital based Nested studies Selection bias Introduction to population stratification. Genetic Association Study Design. Case-Control: Dichotomous endpoints

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study designs in gwas

Study Designs in GWAS

Jess Paulus, ScD

January 30, 2013

today s topics
Today’s topics

Case-control studies

Population based

Hospital based

Nested studies

Selection bias

Introduction to population stratification

genetic association study design
Genetic Association Study Design
  • Case-Control: Dichotomous endpoints
    • Diabetes: yes versus no
  • Continuous or Quantitative traits
    • HgA1C
  • Family Studies

Association Study


Sample Size

Family Study


Low Heritability High

High Genetic complexity Low

hierarchy of study designs



Hierarchy of Study Designs

Systematic Reviews & Meta Analysis

Randomized Controlled Trials

Cohort studies

Case-control studies

Cross-sectional studies

Ecologic studies

Case reports




Cohort Study: Selection into study on basis of exposure status



Basis on which groups are selected at beginning of study



cohort studies in genetic epidemiology
Cohort studies in genetic epidemiology
  • Allows study of multiple disease endpoints – extends efficiency of effort to genotype
  • Selection bias is generally limited
cohort study limitations for genetic epidemiology
Cohort study limitations for genetic epidemiology
  • Loss-to-follow-up bias
  • Need for repeated questionnaire assessments for most up to date covariate information
  • Very costly and logistically challenging to genotype entire cohort and survey for disease endpoints
    • Due to this reason, genetic epidemiologic studies of full cohorts are rare

Case-Control: Selection based on disease status




Basis on which groups are selected at beginning of study

case control designs for genetic exposures
Case-control designs for genetic exposures
  • Appropriate for rare diseases, like cancer
  • Can be retrospective or prospective (nested case-control design)
  • Efficient sampling of an underlying cohort
control selection
Control selection
  • The biggest threat to most case-control studies
  • Controls must be drawn from the source population that gave rise to the cases
  • The ideal controls should:
    • Represent the exposure distribution in the source population that gave rise to the cases
    • Be those who, had they developed the case disease, would have been included in your study as a case
  • Failure to select appropriate controls generates selection bias
    • Selection of participants based on joint probability of exposure and outcome
population case control study
Population case-control study
  • Cases arise from a given population, and controls are randomly sampled from that population (assuming population is enumerated)
  • Example: cases from CT state tumor registry, controls drawn from state census tract listings
  • Reduces potential for selection bias since source of controls is well-defined
limitations of the population based case control study for genetic epidemiology
Limitations of the population-based case-control study for genetic epidemiology
  • Lower participation rates than hospital-based studies, especially given need for biological samples
  • Implementation of specimen collection and processing protocols can be challenging outside a clinical setting
  • If interest in following participants for survival outcomes, tracing can be difficult
hospital based case control study
Hospital-based case-control study
  • Appropriate for genetic epidemiology studies:
    • Hospital setting facilitates subject enrollment and biological specimen collection and analysis
    • Recruitment by medical staff can aid enrollment
    • Smaller geographic area to cover than a population-based study – reduce processing/shipping time
    • Aids in collection of specimens in a timely fashion after disease diagnosis, limiting possibility for reverse causation
  • When cases are hospital-recruited, source population is the catchment population of the clinic
    • The collection of all the people who would have been notified as a case, had they developed disease
hospital based case control study limitations
Hospital-based case-control study limitations
  • Retrospective nature opens door to:
    • Recall bias
    • Reverse causation
    • Selection bias
  • Selection bias in particular is a risk because it is difficult to identify the source population that gave rise to the cases
    • Ideal control: Who would have presented as a case to Hospital X had they in fact become ill?
    • Attempt to identify catchment population can be challenging
  • Sometimes, a control disease (sick controls) is chosen to limit potential for selection bias and differential recall of past exposure
    • Control illness must not be associated with the gene of interest
nested case control study
Nested case-control study
  • A type of population-based control sampling
  • Any case-control can be conceived as resting within a cohort of exposed and unexposed
  • When the cohort is very well defined this is called a nested case-control study
  • Sampling from within the cohort (rather than doing full cohort analysis) is usually motivated by efficiency concerns
  • Important applications for genetic epidemiology where it would be too costly to genotype the full cohort
nested case control study design advantages
Nested case-control study design advantages
  • Limited potential for selection bias because full cohort is enumerated and can randomly sample controls from roster
  • Often prospective – limits potential for gene/biomarker to be affected by disease process
cohort sources of nested case control studies
Cohort sources of nested case-control studies
  • EPIC cohort:
  • Nurses Health Study:
  • NCI Breast and Prostate Cancer Cohort Consortium (BPC3):
  • Multiethnic Cohort (MEC) study:
  • Alpha-Tocopherol, Beta-Carotene Cancer Prevention cohort:
  • Framingham Heart Study:
analysis of case control gwa studies
Analysis of case-control GWA studies
  • Univariate analysis: Pearson χ2 or Fisher exact test, Armitage trend test
  • Multivariate analysis: Logistic regression (if unmatched) or conditional logistic regression (if matched)