The Importance of Genealogy in Determining Genetic Associations with Complex Traits

To the Editor: Most common diseases, such as asthma, type 2 diabetes, bipolar disorder, and cardiovascular disease, are known to have genetic components, but the susceptibility genes have been notoriously difficult to localize and to identify. These complex diseases likely have a large number of genetic and nongenetic risk factors that together have varying effects on phenotype. Many investigators have recommended founder populations for complex-trait mapping, with the expectation that fewer susceptibility alleles will be segregating in these restricted gene pools (Lander and Schork 1994; Wright et al. 1999; Shifman and Darvasi 2001). Some or all individuals in these populations are inbred, but often the exact relationships between all members are either unknown or not taken into account. It is tempting to use such populations for their presumed homogeneity, even in the absence of accurate pedigree information. The failure to take full pedigree information into account can either reduce the power to detect linkage (Dyer et al., in press) or inflate LOD scores (Miano et al. 2000). The failure to account for relatedness among individuals will also affect association studies. In particular, many statistical tests of association are not strictly valid, owing to the lack of true independence between individuals. Nonetheless, such populations have been used widely in association studies (e.g., de Silva et al. 1999; Laprise et al. 2000; Ospina-Duque et al. 2000; Summerhill et al. 2000; Bitti et al. 2001; Hegele et al. 2001), and some authors have even recommended the inclusion of founder populations in case-control studies, owing to their decreased heterogeneity (Shifman and Darvasi 2001). However, the impact that ignoring pedigree relationships has on tests of association has not been evaluated. The Hutterites are an extreme example of a large, complex pedigree with multiple inbreeding loops. We are in the unique position of having complete genealogical information on this 12,903-person, 13-generation pedigree (Abney et al. 2000). Additionally, we have extensive phenotype characterization and a dense microsatellite map (of 568 short-tandem-repeat–polymorphism markers) of ∼750 members of this population, who are descendants of just 64 Hutterite founders (Ober et al. 2000). Thus, we were able to assess the effect that ignoring pedigree information has on statistical tests of association. We performed two separate genomewide scans of association on each of three quantitative phenotypes: serum immunoglobulin E (IgE), serum LDL, and body-mass index (BMI). These phenotypes were chosen to represent quantitative traits associated with diverse complex diseases (asthma, cardiovascular disease, and diabetes, respectively). All phenotypes were adjusted for age and sex and were transformed so that the residuals were approximately normally distributed (Abney et al. 2001). The heritabilities of IgE and BMI were completely accounted for by additive genetic variance, with heritabilities of .63 and .54, respectively; the heritability of LDL had a strong dominance component in addition to additive genetic variance, with a broad heritability of .96 (discussed in detail by Abney et al. [2001]). To estimate the effect that each allele at each locus has on the trait values, we used a statistical test of association, developed specifically for use in large, inbred pedigrees (Ober et al. 2001 [in this issue]). Pedigree structure is taken into account by the use of variance components to model the polygenic background (Abney et al. 2000, 2001). When pedigree structure is ignored, the method is equivalent to a linear regression of the trait on age, sex, and genotype, with a Bonferroni correction applied to the P value for the t test for significance of genotype. In the first scan, we included the variance components and therefore took into account the relatedness between individuals. In the second scan, we did not include any pedigree information (additive and dominance variances were 0). In both scans, the observed P values were adjusted for multiple comparisons, by use of a Bonferroni correction. The two methods yielded dramatically different results. In general, the significance of association with a given marker was considerably inflated when pedigree structure was not included, although in some cases the reverse was true (see fig. 1). Only two loci were among the five most significant results, by both methods, for the three traits (the same marker at 7p21 was associated with IgE, by both methods, and the same marker at 8q12 was associated with LDL, by both methods). In addition, many more loci showed evidence of association when the pedigree structure was not included (see fig. 2). In fact, 10%–22% of all markers appeared to have a strong association (P<.01) with the phenotype, when pedigree structure was not included. Results for 58 single-nucleotide polymorphisms showed similar trends (data not shown). Figure 1 Results of two genome scans—one including pedigree structure (lighter bars) and one not including pedigree structure (darker bars)—for IgE (a), LDL (b), and BMI (c). The five most significant loci when structure is included (left sides ... Figure 2 Number of significantly associated (P<.01) loci when pedigree structure is included (lighter bars) and when pedigree structure is not included (darker bars). Although the Hutterites are an extreme example of a complex pedigree, there are several other populations known to have similar structures (Badner et al. 1990; Slutsky et al. 1997; Hsueh et al. 2000). Moreover, individuals from a variety of smaller, island populations who either have been or are currently being studied may be more related to each other than can be discerned from the recently collected pedigree data (de Silva et al. 1999; Mathias et al. 2000; Bitti et al. 2001). In fact, even presumably outbred populations may contain hidden consanguinity (Broman and Weber 1999), and cryptic relatedness may be a problem in association studies of rare disorders (Bacanu et al. 2000). This problem may be avoided by the use of statistical tools designed to detect misspecified or cryptic relationships (McPeek and Sun 2000; Sun et al., in press). We cannot prove that the inclusion of the pedigree structure in the method results in true associations, until the alleles contributing to these quantitative traits are found; however, we believe that the number of associations found when structure is ignored is unrealistic. Presumably, a profound failure of the assumption of independence between individuals, in method 2, results in a dramatically increased number of type 1 errors. Overall, our data suggest that failing to take into account extended-familial relationships can result in a large number of false-positive results, and some “true” associations may be missed. In addition, the level of significance could be overestimated by several orders of magnitude. In an association study in which it is not possible to take into account all familial relationships, as we have done with the Hutterites, another option is to use genomic controls (Devlin and Roeder 1999). Otherwise, naive approaches to genetic-association analysis could result in an enormous amount of time and of money spent in following up artifactual associations.