Suppose a researcher conducts a genome-wide association study (GWAS) protocol. Phenotypic data for a number of traits, including egg-laying interval, are collected from a large population of 1,526 chickens, including a case and control group. Over 2 million single-nucleotide polymorphisms (SNPs) are genotyped for each chicken sample. After an initial analysis, the data suggest that the egg-laying interval trait evolved differently in different groups of the chicken population, and, therefore, it appears there is some bias in the sample for another trait, comb size. The results of the GWAS for egg-laying interval show the SNPs are also associated with comb size and have a very strong p-value. Select the methods that the researcher may use to determine if the causal mutation of the comb size trait is in the same region as the marker for the egg-laying interval trait or if the association is a result of the inherent bias in the data. Remove the genetic data associated with comb size because eliminating data should remove the effect of comb size on the results for egg-laying interval. Collect additional individual samples and rerun the analysis. Run the association analysis again, including comb size as a covariate of egg-laying interval, which should remove the effect of comb size on the results. Run a principal component analysis to identify if historical population structure is creating the spurious results. Perform an analysis of linkage disequilibrium (LD) on the genetic data, and determine whether the associated SNPs are in LD.