Statistical Issues in GWAS

For both SNP and CNV data, data quality can be improved if we have data available from multiple individuals in the same family through checking the consistency of called genotypes across all family members. MendeUan inheritance can be checked for each marker to identify markers not consistent with Mendelian inheritance. For example, if the genotypes of the parents and their child are 11, 12, and 22, then 

Although intensity data can be used for association analysis, from this point on, we assume that each individual s genotype has been called and the association analysis is done between the trait of interest and the called genotypes, either SNPs or CNVs. 

Devlin and Roeder (1999) were the first to point out that this confounding problem may be addressed if there are many markers available on each individual and the overall genetic heterogeneity roughly has the same effects on all the markers. They proposed the genomic control method to realize this idea. The basic approach is as follows. First, a standard association test is conducted between the trait and each individual marker. Under the null hypothesis of no association, the distribution of all the test statistics should follow a specific distribution, for example, a chi-square distribution for a test based on a contingency table. With hundreds of thousands of markers, the asymptotic distribution should provide a very good 

---