Fifty years of genetic epidemiology, with special reference to Japan

Genetic Epidemiology and Public Health: The Evolution From Theory to Technology

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done.

The Yin and Yang of linkage disequilibrium: mapping of genes and nucleotides conferring insecticide resistance in insect disease vectors

Genetic technologies developed in the last 20 years have lead to novel and exciting methods to identify genes and specific nucleotides within genes that control phenotypes in field collected organisms. In this review we define and explain two of these methods: linkage disequilibrium (LD) mapping and quantitative trait nucleotide (QTN) mapping. The power to detect valid genotype-phenotype associations with LD or QTN mapping depends critically on the extent to which segregating sites in a genome assort independently. LD mapping depends on markers being in disequilibrium with the genes that condition expression of the phenotype. In contrast, QTN mapping depends critically upon most proximal loci being at equilibrium. We show that both patterns actually exist in the genome of Anapheles gambiae, the most important malaria vector in sub-Saharan Africa while segregating sites appear to be largely in equilibrium throughout the genome of Aedes aegypti, the vector of Dengue and Yellow fever flaviviruses. We discuss additional approaches that will be needed to identify genes and nucleotides that control phenotypes in field collected organisms, focusing specifically on ongoing studies of genes conferring resistance to insecticides.

Into the post-HapMap era

The HapMap Project has shifted genetic epidemiology of complex inheritance away from linkage into association mapping of genes affecting disease and response to therapy. Starting with a physical map produced by the Human Genome Project and recent investigation of structural polymorphisms in HapMap samples, population-specific linkage disequilibrium (LD) maps that accurately reflect the fine structure of blocks and steps have been created for use in association mapping, and by interpolation to increase the resolution of linkage maps. All this evidence can be integrated by meta-analysis if expressed as an estimated location and its standard error, a property apparently unique to composite likelihood, recently freed from autocorrelation by permutation of affection status. Methods that do not estimate a standard error are easier to apply, but may be misleading if a causal marker has not been typed. The month of June 2007 saw advances in genome-wide association scans (GWAS) for several diseases. Many questions remain to be answered if genetic epidemiology is to continue the significant contribution to medicine that its definition promises and its history illustrates.

Genome scanning by composite likelihood

Ambitious programs have recently been advocated or launched to create genomewide databases for meta-analysis of association between DNA markers and phenotypes of medical and/or social concern. A necessary but not sufficient condition for success in association mapping is that the data give accurate estimates of both genomic location and its standard error, which are provided for multifactorial phenotypes by composite likelihood. That class includes the Malecot model, which we here apply with an illustrative example. This preliminary analysis leads to five inferences: permutation of cases and controls provides a test of association free of autocorrelation; two hypotheses give similar estimates, but one is consistently more accurate; estimation of the false-discovery rate is extended to causal genes in a small proportion of regions; the minimal data for successful meta-analysis are inferred; and power is robust for all genomic factors except minor-allele frequency. An extension to meta-analysis is proposed. Other approaches to genome scanning and meta-analysis should, if possible, be similarly extended so that their operating characteristics can be compared.