Re: Association of polymorphism in cytochrome P450 2C9 with susceptibility to head and neck cancer and treatment outcome: Pragmatic use of Hardy-Weinberg equilibrium and statistical interaction analysis

In a recent issue of the journal, the research article “Association of polymorphism in cytochrome P450 2C9 with susceptibility to head and neck cancer and treatment outcome”, by Yadav et al. (2014) reported that cytochrome P450 2C9 single nucleotide polymorphisms (SNPs), CYP2C9*2 (rs1799853) and CYP2C9*3 (rs1057910), showed a risk association with head and neck squamous cell carcinoma (HNSCC). They also reported the statistical interaction of these polymorphisms with tobacco smoking, tobacco chewing, and alcohol consumption. In this study, the author underscored several important points, however, failed to address several limitations. The author reported significant deviation from the Hardy-Weinberg equilibrium (HWE) for controls for both polymorphisms (Yadav et al., 2014). The Hardy-Weinberg disequilibrium (HWD) in controls is a major limitation and results should be interpreted cautiously (Salanti et al., 2005). Use of the Cochran-Armitage test of trend has been suggested if there is a significant deviation (P-value b 0.05) from HWE (Lewis and Knight, 2012). HWE elucidates that in a large population, alleles segregate randomly (Lewis and Knight, 2012; Lunetta, 2008). Allele and genotype frequencies in the population do not change until there is selection, mutation, or migration. Departure from HWE in controls may be due to several issues, including cryptic population structure (Salanti et al., 2005; Lewis and Knight, 2012; Lunetta, 2008). It has been recommended to exclude SNPs which show significant deviation from HWE in controls to avoid spurious associations. However, the criterion of exclusion depends on several factors (Lunetta, 2008). The Indian population is very heterogeneous, with several subpopulation structures among ethnic groups existing across India (Basu et al., 2003; Indian Genomic Variation Consortium, 2008; Rich et al., 2009). The North Indian population is also not homogenous. Even pooling samples by state of origin can mask population substructure (Indian Genomic Variation Consortium, 2008). Counting them as a homogenous population will increase false-positive disease associations due to systematic ancestry difference between cases and controls (Indian Genomic Variation Consortium, 2008; Rich et al., 2009). In this study, the author did not account for population stratification as a major confounder. Population stratification can be avoided by classifying individuals into known subgroups (e.g., by ethnicity or caste) in cases and controls, to avoid confounding (Lewis and Knight, 2012; Lunetta, 2008). Most importantly, the results should be adjusted for any covariates related to population structure (Lunetta, 2008). Genomic controls can also be added to reduce confounding by alleles.