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Searching for the missing heritability of complex diseases

Recent genome-wide association (GWA) studies have vindicated the common disease-common variant (CDCV) hypothesis identifying a new repertoire of disease susceptibility loci of unknown function for many complex diseases www.genome.gov/ gwastudies. Because these variants are typically common they can underscore a significant proportion of disease incidence. However, even collectively, they fail to account for a large proportion of the empirical familial clustering of the disease, and there remains considerable ‘‘missing heritability’’ [Eichler et al., 2010; Manolio et al., 2009]. This is well illustrated by recent GWA studies of cancer. Although multiple susceptibility loci have been identified for many of the common cancers few account for more than 1% of the familial relative risk (FRR) [Chung et al., 2010], even collectively the identified SNPs for breast and colorectal cancers are thought to account for no more than 10 and 6% of the respective FRRs [Fletcher and Houlston, 2010]. Such discrepancies appear to challenge an overarching tenet of the CDCV paradigm [Hemminki et al., 2006]. This apparent failure has led to considerable interest in alternative models of inherited susceptibility based on rare disease-causing variants [Bodmer and Bonilla, 2008]. We believe it may be highly presumptuous to jettison the CDCV model at this juncture. We show that if the identified variants are simply markers of rarer causal alleles the loci can potentially explain a much larger proportion of the familial risk, and impacting significantly on the apparent ‘‘missing heritability’’ [Eichler et al., 2010; Manolio et al., 2009]. We have recently pointed out that it is not possible to predict FRRs conveyed by gene variants unless these are directly causal and that estimating the impact of loci on FRR on the basis of markers, instead of the functional variants, usually leads to an underestimation of FRR [Hemminki et al., 2008]. The SNPs used in GWA studies are generally unlikely to be directly causal; hence, they invariably do not give accurate predictions of the effects of correlated causal variants. In the present article, we illustrate the FRR relationships between the marker and the causal variant by a real-life example from gene discovery for age-related macular degeneration (AMD) that shows a sibling FRR of threeto sixfold [Edwards et al., 2005; Maller et al., 2006]. We then develop normograms for the prediction of familial effects from marker SNPs, assuming plausible allele frequencies and linkage disequilibrium (LD) between the causal variant and its marker. Edwards and coworkers [2005] genotyped a set of SNPs on AMD cases and controls over a candidate locus at 1q25–31.3. We plotted part of their results at the regulation of complement activation locus at 1q31.3 in Figure 1, showing the obtained P-values (A) and the FRRs calculated by us (B). We highlight two SNPs, rs800292, in which the C allele is ‘‘the marker’’ and rs1061170, in which the C allele is ‘‘the causal variant.’’ In rs1061170, the T-to-C transition causes a tyrosine to histidine amino acid change in complement factor H, CFH [Edwards et al., 2005]. The P-value for the marker was 9 10 4 in the genotyping study, compared to the P-value of 4 10 8 for the causal variant (Fig. 1A). The marker C allele (frequency 5 0.76) was in LD with the C allele of the causal variant (frequency 5 0.34, D0 1.0, r 0.1). The genotype relative risks (GRRs 5 odds ratios) for rs800292 were GRRhet 5 1.9, GRRhom 5 2.8; for rs1061170 they were GRRhet 5 2.8, GRRhom 5 4.6. The conferred FRRs that we calculated for these variants were 1.03 and 1.15, respectively (Fig. 1B). Thus, had Edwards et al. only genotyped the marker, they would have concluded that the FRR was only 1.03, thereby missing 80% (1–0.03/0.15) of the heritability at this locus. We derive here the relationships for FRR between marker and causal alleles. In the simulation, based on the previously published code [Hemminki and Lorenzo Bermejo, 2007; Hemminki et al., 2008], and exactly applying parameters from the CFH locus, we let the marker allele M tag the causal variant C; M is more frequent than C. There are thus three haplotypes: c-m, c-M, and C-M. The association signal for M is entirely due to the causal effect of C. The frequency of C is kept constant (pC 5 0.34, equal to rs1061170) and the frequency of the marker allele frequency (pM) is increased. We further assume an additive model, GRRC_Het 5 (GRRC_Hom11)/2. Also, data for a dominant (GRRC_Het 5 GRRC_Hom, GRR 5 10) and recessive models are shown. In the first simulation, C is always found together with M, that is, D05 1.0. According to all models the marker captures 100% of the FRR when pM 5 pC (Fig. 2A). However, the percentage of capture drops fast when pM4pC. The ratio of 2.24 for pM/pC was the ratio of allele frequencies for rs800292 and rs1061170 at the CFH locus. The position of rs800292 is shown on the additive plot and we can read that the excess FRR (FRR-1.0) of rs800292 should be 20% of that of rs1061170, consistent with Figure 1B. It can be noted that variation of D0 (dominant model) does not change the curves that are superimposable (Fig. 2B). Variation in r causes a large drop in marker FRR (additive model) (Fig. 2C). OFFICIAL JOURNAL