Number of genetic variants contributing to genetic etiology implications for study strategy

Many late-onset diseases result from the cumulative breakdown of numerous quantitatively varying physiological systems. For example, high blood pressure can follow from abnormalities in cardiac output, blood vessel architecture, renal function, fat distribution, endothelial function and central nervous system integration, or failure of diverse homeostatic mechanisms, including baro-receptors, natriuretic peptides, renin-angiotensin-aldosterone, kinin-kallikrein, adrenergic receptors and local vasodilator mechanisms - each under varying degrees of independent genetic control.

If common diseases are truly polygenic and determined by variation at very many loci, most individual effects will be too small and peripheral to the disease mechanism to be useful. The genetic (locus) complexity depends firstly on the physiological complexity of the trait. Hirschsprung disease, for example, is a rare and highly specific lack of enteric ganglia, in which eight causal loci have been identified but much of the disease variance is due to three interacting loci. In contrast, coronary artery disease is a highly complex multidimensional trait with an exponentially increasing lifetime risk which is influenced by more than 280 risk factors (Wright et al., 2002). Secondly, the distribution of gene effects is clearly important, since if most of the variance is determined by a handful of potentially detectable genes, the total number is irrelevant.

Animal models and studies of the effect of inbreeding in humans (Rudan et al., 2003) suggest that the genetic basis of many complex diseases and traits is polygenic (rather than oligogenic). An estimate of the number of genetic factors underlying complex disease can be obtained by considering the fall in disease risk with decreasing degree of relationship to index cases (Risch, 1990; Schliekelman and Slatkin, 2002). A more rapid fall in risk indicates the involvement of a larger number of genetic variants. A truly polygenic model with large numbers of genetic variants each responsible for a small proportion of the phenotypic variance of a trait would require a greatly increased sample size. The implications for study design are similar to those discussed for variants which are rare or of small effect size above.

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