f) Results from alternative approaches: principal components (PCA) and hierarchical clustering. Several alternative approaches to analyses of genome wide association datasets can also provide interesting results that assess the structure of the pool-to-pool variance, based on data from 500 or 600k SNP sets. Principal components analyses (PCA) of the pool-to-pool differences in 500k data from sample sets from European-American, African-American and Asian samples divides the data along these racial/ethnic lines, as we might expect. However, PCA analyses also subdivide data from experiments studying two distinct United States samples of nominally-equivalent genetic background, Sample 1 NIDA European-American samples recruited in Baltimore, Maryland vs Sample 3 COGA European-American samples recruited in St Louis, Bronx New York, San Diego, California and other sites. Similarly, these PCA analyses separate the Asian samples recruited in Japan from those recruited in or near Taipei that are self-characterized as Han Chinese. Each of these results underscores the need for extremely careful matching of the racial/ethnic backgrounds of control and disease samples.
