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Gene Hunting: Design and statistics. Genotype:. AA. AC. CC. Schiz:. Phenotype:. Not Schiz:. Population- based Association Design: Qualitative Phenotype. Do c 2 test for association. Population- based Association Design: Quantitative Phenotype. Phenotype. 0 (AA). 1 (AC). 2 (CC).

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Gene Hunting: Design and statistics

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Gene hunting design and statistics

Gene Hunting:Design and statistics

Gene hunting design and statistics







Not Schiz:

Population-basedAssociation Design:Qualitative Phenotype

Do c2 test for association.

Gene hunting design and statistics

Population-basedAssociation Design:Quantitative Phenotype





Number of C alleles

Compute the correlation (or regression slope)

Gene hunting design and statistics

GWAS: Genome-wideAssociation Study

DNA arrays with 1,000s of SNPs scattered throughout the genome. (Current chips in 2009 has over 1,000, 000 different SNPs)

Select the SNPs so that they cover ALL the genome using haplotype blocks. (Some DNA chips oversample SNPs in protein coding regions)

Genotype patients and controls on all the SNPs(or genotype a random sample of the population).

Find the SNPs that differ patients from controls (or have a significant correlation with a quantitative phenotype).

Problem: number of statistical tests.

Gene hunting design and statistics

GWAS results as of 2012


Gene hunting design and statistics

GWAS and Quantitative Phenotype:Height (Weedon et al, 2007)

Note: Effect size = c. 0.2 inches, length of a housefly

Gene hunting design and statistics

Problems with GWAS

(1) Expensive.

(2) Large number of statistical tests.

(3) Need very, very large samples (10,000 or more.

Gene hunting design and statistics

Results from GWAS

(1) Good success in medicine.

(2) Limited success for psychiatric disorders (but things are improving)

(3) Virtually no success for normal behavioral traits (personality, IQ)

(4) Genetics of behavior is hyper-polygenic: many, many, many genes

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