How to use genomic markers in disparities studies

ED02-04

Classifying individuals into classes that represent heterogeneous racial/ethnic groups may simplify data collection and analysis, but it may also misclassify a person’s actual ancestral background and limit assessment of variation within racial/ethnic groups that is relevant for understanding disease risk or outcome.
 Admixture stratification (AS) refers to the existence of variation in genetic ancestry within a single race/ethnicity group. AS is not only present in recently admixed populations like African Americans and Latinos, but also in European-American populations and historically isolated populations including Icelanders. Consequences of AS include: increased allelic associations (i.e. increased linkage disequilibrium), deviations from Hardy-Weinberg equilibrium and bias in the estimates of genetic associations. Bias due to AS can induce both false positive and false negative associations. In order for bias due to AS to exist, both of the following must be true: (a) the frequency of the marker genotype of interest varies significantly by race/ethnicity and (b) the background disease prevalence varies significantly by race/ethnicity. If either of these is not fulfilled, bias due to AS cannot occur.
 Several methods have been developed to test for and/or adjust for AS, although no true consensus has been reached as to which method is best. When race/ethnicity can be accurately described in terms of actual ancestry and there is ancestral homogeneity in a study population, standard epidemiological approaches of matching or statistical adjustment by race/ethnicity may be sufficient to remove or reduce bias due to AS. However, most individuals cannot sufficiently report their ancestry, so multiple methods are now available to estimate one’s individual ancestry from sets of markers in the genome.
 These methods all utilize genotype information either from a set of random markers or from a set of selected ancestry informative markers (AIMs). AIMs are defined as markers that show large allele frequency differences between ancestral populations. These methods for testing for and/or adjusting for AS can be broadly classified into 3 classes: (1) Genomic control, (2) Structured Association and (3) Other.
 The following questions will be addressed in this session:
 (1) How do you select the proper markers for AS analysis?
 (2) What methods are available for ancestry estimation and for the testing and/or adjustment for AS and how do they compare?
 Because of the vast number of options now available for assessing and controlling for AS, care must be taken to ensure that all assumptions of the method are being met and that the method of choice is actually testing the intended hypothesis.