Every science has roots that antedate acceptance as a discipline. Neglecting these intimations, there is a consensus that population genetics became a recognised science about 1920, and that it split into two branches about 1960. One branch is concerned with historical processes, uniting genetics with anthropology and (in other species) with paleontology and taxonomy. The other branch is concerned with inheritance in contemporary populations, uniting genetics with epidemiology, medicine, psychology, and (in other species) with agriculture. Anthony Edwards (1995) identified this dichotomy as ‘differential-equation biology’ vs ‘statistical biology’. The distinction becomes fastidious when current gene frequencies are taken as indirect evidence for selection by disease in the remote past: such inference from a unique event is more characteristic of evolutionary genetics but concerns all population geneticists. From time to time interest groups are formed around twins, behaviour, demography, or quantitative traits, but they are generally recognised as aspects of genetic epidemiology. If the young science is in jeopardy, it is not by fission but by exclusion of studies that are only indirectly related to disease, which provides a focus but does not specify the contents. I will argue that every aspect of population genetics that is not primarily concerned with evolution is part of genetic epidemiology, and that the field must suffer if it is not inclusive. As with all human activity progress is punctuated, often cyclical, and therefore suggests the real and apparent movement of celestial bodies for which the language of astronomy is appropriate.

Through linkage analysis and the identification of structural chromosome rearrangements, a number of disease genes have been mapped to the pericentromeric region of the long arm of chromosome 13. Structural rearrangements, or deletions, of the 13q12 region have been implicated in a range of myeloproliferative neoplasms, and other haematopoietic malignancies. In particular, seven cases of a t(8;13) (p11; q12·1) rearrangement have been noted in patients with an atypical myeloproliferative disorder associated with T-cell leukemia and eosinophilia. We have previously identified a CEPH megaYAC, 943E4, which crosses the translocation breakpoint in archival tumour samples from two patients with this t(8;13) translocation. As an initial step in the characterisation of this translocation breakpoint, we have generated a fine structure physical map of this 1·9 Mb YAC. We have used the method of YAC fragmentation to generate a series of deletion constructs of known size, which provide discreet physical landmarks convenient for mapping genetic markers along the 943E4 YAC. Analysis of these deletion constructs defined the order of ESTs and microsatellite markers in 943E4 as: cen-NIB1257-(ATP1AL1/D13S283)- D13S179E-(D13S504E/D13S505E)-D13S824E-D13S182E-D13S221-tel. These markers have also been assigned to physically defined regions relative to the fragmented YAC endpoints and a derived NotI restriction map.

When dominant mutations of different genes may lead to the same disease, it is often difficult to detect in a particular patient which gene is involved. A strategy is to make genealogical extensions to find affected relatives that should have inherited the same mutation. In particular, for diseases with late age of onset or short survival time, only poor information may be obtained from close relatives of probands and it can be particularly efficient to make genealogical extensions to detect pairs of distantly related affected individuals. Such a pair of affecteds may provide information concerning the region of the genome where the mutated gene should map. Two situations may be encountered depending on whether or not prior information on the location of mutated genes involved in the disease are available. If we already know, from previous linkage studies, that a gene located in a given region R of the genome may be involved in the disease, the problem is then to confirm that it is indeed a mutation of this gene that is involved in the affected pair. Once the implication of a gene in region R has been confirmed the affected pair of relatives may give information to restrict the length of this region R. In this paper we discuss these two points by deriving analytically first the lod score expected and second the expected reduction of the length of the region where the mutation is suspected to map as a function of the number of meioses between the two affected individuals and of the polymorphism of the markers available in the region.

The GM immunoglobulin (Ig) allotype distributions of 49 native Amerindian populations from North to South America were analysed by a new technique called ‘Mobile Sites Method’ (MSM). This allows the global interpretation of genetic diversity in space by means of a distorted geographic map called a ‘genetic similarity map’. This approach has been improved by superimposing in the distorted geographic map both the haplotype set (represented by hypothetical populations having a 100% frequency of the haplotype considered) and the ‘geography-genetics discontinuities’ (i.e. the zones between homogeneous population clusters). This bidimensional representation completes the interpretation of the genetic distances between populations in terms of local genetic diversity and possible migrations. Our results concerning the spatial distribution of the Amerindian populations show: (i) a great interdependence of the geographic locations and the GM haplotype distributions (the importance of the geographic factor was checked with the usual technique of ‘random sampling’ and the percentage of explained distance variability decreases from 78% with the observed data to a level less than 67% with the random data); (ii) a parallelism between genetics and linguistics groups as indicated by the population clusters in the similarity map, and (iii) a complex distorted map revealing the presence of multiple population migrations and admixtures in the course of time. A particular distortion of South America suggests possible migrations by sea along the western and eastern coasts of Central America, or multiple migration waves without population admixture across Central America.

A powerful test for population association of a disease with alleles at a bi-allelic marker locus is the transmission/disequilibrium test (TDT). A generalization of the test to multi-allelic marker locus is proposed which utilizes the maximal association of individual alleles with the disease, given by the maximum TDT statistic, TDT(max). To overcome the multiple testing problem encountered when using the maximal association to test the null hypothesis of no disease-marker association, a randomization procedure is developed. An investigation of the power of the test suggests that the randomization procedure performs almost as well as a recently proposed likelihood based test of linkage disequilibrium. The advantage of the new test is that it can be applied sequentially, based on a one-sided version of the TDT statistic, for investigating patterns of association of several individual alleles with the disease.

Application of the affected sib-pair method of linkage analysis to sibships with variable numbers of affected and unaffected members requires a scheme for weighting the contributions from the sibships in the calculation of an overall test statistic. Currently accepted weighting schemes are based on the concepts of independent pairs and Shannon information content. Here, we show that the weighting scheme with maximum power to detect linkage can be determined from the theoretical means and variances of the sibship contributions under the null and alternative hypotheses. We derive the theoretical means and variances of the contributions from different types of sibships under a generalized single locus model. We use these theoretical means and variances to obtain the optimal weights for a variety of single locus models, and compare the power of existing weighting schemes relative to the optimum. The results suggest that, under a range of plausible assumptions, optimal power is nearly obtained by weighting to all sibpairs equally, except those occuring in sibships with so many affected siblings (usually five or more) that the probability of parental homozygosity for the disease allele becomes substantial. A corollary is that, in affected sib-pair analysis, the ‘informativeness’ of a sibship is more nearly proportional to the number of affected sib-pairs than to the number of affected siblings, up to about five affected siblings.

Nonrandom associations between DNA polymorphisms are commonly tested by Fisher's exact test in spite of it is seriously conservative. Theoretical statistical studies have shown that the exact test with Tocher's correction does not present this problem but Tocher's correction is never used by experimentalists for detecting gametic associations. We have examined the practical consequences of using these two alternative tests for the detection of nonrandom associations in populations. A total of 1566 pairs of RFLPs of eleven gene regions and sixteen populations from previously published human and Drosophila disequilibrium data were examined. The analysis reveals remarkable differences between the two tests for detecting gametic associations. In some gene regions, the exact test with Tocher's correction detects a percentage of significant associations between RFLPS which is twice or three times higher than that detected by the exact test without correction. Therefore, the widely used exact test can be seriously underestimating disequilibrium in populations. In addition, the study shows that the standard chi-square test for independence in 2 × 2 tables detects a similar percentage of significant associations between RFLPs than Tocher's test.

The robust sib-pair method introduced by Haseman & Elston (1972) is one of the most widely circulated allele-sharing methods for linkage analysis. The procedure evaluates linkage by significance testing of a regression coefficient and, hence, a standard t-test has traditionally been applied despite known violations of the statistical assumptions underlying the test. We present a permutation based reference distribution for the estimate of the regression coefficient that is motivated by genetic principles rather than by standard regression testing procedures. The permutation test approximates Mendelian co-segregation under the null hypothesis of no linkage, making it a very natural approach. Theory and simulations show that the conventional t-test approximates the permutation test quite well, even when dependent sib pairs are used for analysis. These results thus indirectly address concerns over the t-test. To illustrate the permutation test using real data we applied the procedure to two lipoprotein systems that have been well characterized.