Análisis morfológico

Population stratification is a potential source of bias in any genetic association study conducted in an admixed population, like the CBCS. Population stratification occurs when a population is composed of multiple ancestral groups, and the ancestral groups have different allelic frequencies for the genetic marker of interest (46, 53). If the outcome is more common in one ancestral group, then members of that subpopulation are more likely to be among the case group, and any genotype present at a higher frequency in that subpopulation will be associated with the outcome, regardless of whether it is in linkage disequilibrium with the true causal allele (54).

Although self-identified race is expected to be correlated with ancestry, there is still potential for residual bias due to population stratification because of admixture of African and European ancestry in African Americans (46). There is also the possibility of cryptic stratification among white CBCS participants who are descended from multiple European populations with distinct genetic backgrounds (54). Studies have reported that African

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Americans have approximately 5% to 20% European ancestry (33, 45, 55-59). Others have demonstrated that there is identifiable population substructure in Americans of European descent (60). Population stratification can also occur when the amount of admixture in the population varies among individuals (53).

2.5.1 Methods of assessing population stratification

There are several methods of assessing population stratification in genetic association studies. One method is to estimate individual ancestry and adjust parameter estimates for ancestry. Individual estimates of ancestry can be calculated using maximum likelihood estimation (MLE), and was described previously Barnholtz-Sloan et al. (56). This method requires that the study population is genotyped for a set of AIMs specifically selected to maximize the differences between ancestral populations, and knowledge of the allele

frequencies in the ancestral populations (46). The MLE maximizes a log-likelihood equation that is a function of the observed allele frequencies in the admixed population, the

contributions from the ancestral populations, and the difference in allele frequency between the ancestral populations (56). The likelihood is maximized using the Newton-Raphson grid search method, yielding proportions of ancestry for each ancestral population that sum to 1(56).

Structured association methods can also be used to estimate individual ancestry. Structured association uses genetic marker information to infer subpopulation membership, based on the pre-specified number of subpopulations [reviewed by (46)]. Structured

association can use markers pre-selected to differ between ancestral populations (AIMs) or random genetic markers (46). Structure, a commonly used structured association program, uses Bayesian Markov Chain Monte Carlo estimation to simultaneously estimate allele

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frequencies in subpopulations and individual ancestry proportions (54, 58, 61, 62). If the number of subpopulations is unknown ancestry can be inferred based on the posterior

probability for a range of subpopulations, though the number of subpopulations may not have a valid interpretation in the context of the data (58). Studies comparing maximum likelihood and structured association ancestry estimates report that the two methods are highly

correlated (55, 59, 63).

Genomic control was proposed by Devlin and Roeder to eliminate the problems of type I error that may occur due to population stratification or cryptic relatedness in case control studies (64). Association test statistics can be inflated when there is population substructure (64). The genomic control method of adjusting for population stratification involves calculating a variance inflation factor for a set of random, unlinked SNPs across the genome, and adjusting all SNP association tests for the extra variance due to population substructure (64, 65). Genomic control assumes that the variance inflation is roughly constant across all loci being tested, but this is not always true leading to possible over- or under- adjustment of variance for different loci. Case-control simulations by Devlin and Roeder showed that despite controlling for type I error, genomic control also resulted in reduced power to detect risk alleles (64). Marchini et al. (66) demonstrated that using too few markers for genomic control can lead to false positives, and the degree of bias increases with

increasing sample size. While using more markers can reduce the chance of type I error it can also lead to loss of power (66).

Principal components analysis is a method of transforming correlated variables into new, uncorrelated variables based on the linear relationships that can be defined within the data (67). Applied to population genetics, the goal is to identify the principal components that

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describe the variation in a set of genetic markers (68). Price et al. (60) described the use of principal components analysis to control for population stratification by identifying the axes of genetic variation, continuously adjusting genotypes and phenotypes for ancestry along each axis, and calculating associations using the adjusted genotypes and phenotypes. One of the major advantages of principal components compared to structured association is that it is much more efficient at determining population structure using a very large number of

markers. This is not an advantage for ancestry estimation in the CBCS because of the prior decision to genotype using the 1536 marker custom GoldenGate platform instead of a much larger GWAS platform. Furthermore, the principal components method has a higher rate of type I error when the number of markers used to identify genetic variation is low (60). This method is more suited to large GWAS datasets rather than candidate gene studies with a limited number of SNPs, such as the CBCS genotyping panel.

MLE, structured association, and principal components all require some prior knowledge of the number and types of subpopulations present in the data, either for AIM selection or interpretation of the number of populations inferred from the data. However, unlike MLE and structured association methods, genomic control and principal components analysis do not explicitly model individual ancestry estimates. As such, MLE and structured association provide more information that allows for characterization of the distribution of ancestry and admixture in the study population as opposed to providing methods mainly intended to adjust for population stratification.

2.5.2 African and European ancestry in the CBCS

144 ancestry informative markers (AIMs) were genotyped in the same Illumina GoldenGate assay as the candidate gene SNPs, using methods described above. Individual

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proportions of African and European ancestry were estimated using maximum likelihood and structured association. Under the maximum likelihood method, the ancestry proportions were estimated for each study subject by solving likelihood equations described by Barnholtz- Sloan et al. (56) using the Newton-Raphson method. AIMs were selected to describe ancestry with regard to African and European populations only, and the maximum likelihood equation is restricted such that the ancestry proportions add up to 1 (56). The proportion of African ancestry in CBCS subjects is described and utilized in the remainder of this dissertation; the proportion of European ancestry is equal to one minus the proportion of African ancestry.

The structured association estimates were generated using Structure v.2.0, which uses Bayesian estimation to determine the proportion of a subject’s genome that belongs to each ancestral population cluster, K (58, 61, 62). Preliminary estimates were calculated for K=1 through K=5 and the most likely number of populations was determined to be 2, based on a plateau reach in the estimated log probability of the data (69). Cluster membership and ancestry estimates were re-calculated for K=2 using the admixture and correlated allele frequencies models (58).

Results from the maximum likelihood ancestry estimation are shown in Figure 2.4. The median proportion of African ancestry was 81% in African Americans and 6% in whites. Subjects who reported that they were white, Hispanic, and Asian/Pacific Islander had less than 50% African ancestry. Subjects who described themselves as American Indian or Eskimo had varying amounts of African ancestry, ranging from 2% to 89%. The majority of African Americans had between 50% and 96% African ancestry. There were some self- identified white subjects with relatively high proportions of African ancestry, and some self- identified African Americans with relatively low proportions of African ancestry (Figure

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2.4). Subjects with seemingly discordant race and ancestry results are not concentrated in any particular genotyping plate, column, or row, so it is unlikely that these results are due to some systematic error during plating and/or genotyping (data not shown).

Ancestry estimates obtained from Structure were similar to MLE estimates, but were skewed towards the ends of the distributions (Figure 2.5). The median Structure-determined African ancestry was 92% in African Americans and 1% in whites. Ancestry estimates from the two methods were highly correlated (all subjects r2 = 0.97, P < 0.0001; African American r2 = 0.99, P < 0.0001; non-African American r2= 0.87, P < 0.0001) (Figure 2.6).

Recent studies using MLE and Structure have estimated that African Americans have between 77% and 87% African ancestry (45, 55, 56, 58, 59). Parra et al. (33) reviewed early studies of ancestry estimation and found that estimated African ancestry in African

Americans from South Carolina and Georgia varied from approximately 85% to 96%. Others have reported that MLE and Structure ancestry estimates are highly correlated in admixed populations (55, 59, 63). Shriver et al. (59) also reported that the correlation between Structure and MLE individual ancestry estimates was higher in African Americans from Washington, DC compared to white Americans from State College, PA, which is what was observed in the CBCS.