Abstract
The detection of genetic loci associated with Alzheimer’s disease (AD) requires large numbers of cases and controls because variant effect sizes are mostly small. We hypothesized that variant effect sizes should increase when individuals who represent the extreme ends of a disease spectrum are considered, as their genomes are assumed to be maximally enriched or depleted with disease-associated genetic variants. We used 1,073 extensively phenotyped AD cases with relatively young age at onset as extreme cases (66.3 ± 7.9 years), 1,664 age-matched controls (66.0 ± 6.5 years) and 255 cognitively healthy centenarians as extreme controls (101.4 ± 1.3 years). We estimated the effect size of 29 variants that were previously associated with AD in genome-wide association studies. Comparing extreme AD cases with centenarian controls increased the variant effect size relative to published effect sizes by on average 1.90-fold (SE = 0.29, p = 9.0 × 10−4). The effect size increase was largest for the rare high-impact TREM2 (R74H) variant (6.5-fold), and significant for variants in/near ECHDC3 (4.6-fold), SLC24A4-RIN3 (4.5-fold), NME8 (3.8-fold), PLCG2 (3.3-fold), APOE-ε2 (2.2-fold), and APOE-ε4 (twofold). Comparing extreme phenotypes enabled us to replicate the AD association for 10 variants (p < 0.05) in relatively small samples. The increase in effect sizes depended mainly on using centenarians as extreme controls: the average variant effect size was not increased in a comparison of extreme AD cases and age-matched controls (0.94-fold, p = 6.8 × 10−1), suggesting that on average the tested genetic variants did not explain the extremity of the AD cases. Concluding, using centenarians as extreme controls in AD case–control studies boosts the variant effect size by on average twofold, allowing the replication of disease-association in relatively small samples.
Highlights
[3] About 30% of the genetic risk is attributable to the ε4 allele of APOE gene, and large collaborative efforts have identified over two dozen additional genetic loci that are associated with a slight modification of the risk of Alzheimer’s disease (AD). [4,5,6,7,8,9,10,11,12,13,14,15,16,17] The design of these association studies relies on the comparison of very large numbers of cases with age-matched controls, such that detected associations can be attributed to the disease
The effect sizes for six genetic variants were not increased in our extreme phenotype analysis compared with previously reported effect sizes (EEkAÀEC between 0 and 1): in or near TREM2 (R62H), KANSL1, CR1, ABCA7 (G > C), CLU, and INPP5D
This was almost identical to the average increase in effect size when we compared the extreme cases with centenarian controls (EEAÀEC = 1.90 ± 0.29; p = 9.0 × 10−4) (Fig. 3)
Summary
[4,5,6,7,8,9,10,11,12,13,14,15,16,17] The design of these association studies relies on the comparison of very large numbers of cases with age-matched controls, such that detected associations can be attributed to the disease. As the AD risk for future cases likely involves the same genetic variants, using age-matched controls may quench variant association signals. This may, in part, explain the mostly small variant effect sizes associated with common variants. [19] Rare genetic variants often have larger effect sizes than common variants, but as there are fewer carriers available in the population, the requirement for large sample sizes stands. GWAS studies mostly compare common genetic variants that are widely propagated in the population; as a consequence, these have mostly small effects on AD risk. [19] Rare genetic variants often have larger effect sizes than common variants, but as there are fewer carriers available in the population, the requirement for large sample sizes stands. [20]
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