A crucial step beyond the identification of genetic linkage of a disease to a chromosomal region is the production of a physical map that will allow the identification of candidate genes. Although the process of physical map building has been facilitated by the flow of data released by the Human Genome Project, gathering all the information together requires significant effort. In a previous study, we reported linkage between Bipolar Affective Disorder and the chromosomal location 4p15.3–p16.1. In this review we use this example to describe how to collect publicly available sequence, DNA fingerprint, and genetic marker data and integrate these with empirical data to build a large scale high resolution physical map of a region. Methods used to identify new genetic markers and candidate genes within a circumscribed region are also presented.

Smith–Lemli–Opitz syndrome (SLOS) is caused by mutations in the DHCR7 gene leading to deficient activity of 7-dehydrocholesterol reductase (DHCR7; EC 1.3.1.21), the final enzyme of the cholesterol biosynthetic pathway, resulting in low cholesterol and high concentrations of its direct precursor 7-dehydrocholesterol in plasma and tissues. We here report mutations identified in the DHCR7 gene of 13 children diagnosed with SLOS by clinical and biochemical criteria. We found a high frequency of the previously described IVS8–1 G > C splice acceptor site mutation (two homozygotes, eight compound heterozygotes). In addition, 13 missense mutations and one splice acceptor mutation were detected in eleven patients with a mild to moderate SLOS-phenotype. The mutations include three novel missense mutations (W182L, C183Y, F255L) and one novel splice acceptor site mutation (IVS8–1 G > T).

Two patients, homozygous for the IVS8–1 G > C mutation, presented with a severe clinical phenotype and died shortly after birth. Seven patients with a mild to moderate SLOS-phenotype disclosed compound heterozygosity of the IVS8–1 G > C mutation in combination with different novel and known missense mutations.

Both hepatic lipase (HL) and apolipoprotein C-III (apoC-III) influence lipid metabolism. Common variation in promoters of both genes, LIPC -514C > T and APOC3 -482C > T, respectively, have been shown to affect plasma lipids and lipoproteins and glucose tolerance. We studied the interaction between both variants on parameters of glucose tolerance and lipid metabolism in 714 healthy young males participating in the second European Atherosclerosis Research Study (EARS-II). Approximately 18% of the subjects were carriers of at least one rare LIPC and APOC3 allele. These subjects exhibited, after fasting and oral fat loading, the highest values of triglyceride-rich lipoproteins, but there was no significant interactive effect on any lipid variable. However, interaction occurred on basal diastolic blood pressure (p =0.036) and, during oral glucose tolerance testing, on peak (p = 0.0065) and area under the curve for glucose (p =0.049), and insulin (p = 0.035). This resulted in the highest diastolic blood pressure and lowest glucose tolerance in carriers of at least one rare allele of both genes. Thus gene:gene interaction between LIPC and APOC3, even in these healthy young males, leads to changes in parameters that are typically characteristic of Syndrome-X.

HLA class I and class II alleles have been studied for the first time in the Turkish-speaking Tuvinian population, which lives in Russia, North of Mongolia and close to the Altai mountains. Comparisons have been done with about 11000 chromosomes from other worldwide populations, and extended haplotypes, genetic distances, neighbor joining dendrograms and correspondence analyses have been calculated. Tuvinians show an admixture of Mongoloid and Caucasoid characters, the latter probably coming from the ancient Kyrgyz background or, less feasibly, more recent Russian Caucasoid admixture. However, Siberian population traits are not found and thus Tuvinians are closer to Central Asian populations. Siberians are more related to Na-Dene and Eskimo American Indians; Amerindians (from nowadays Iberian–America) are not related to any other group, including Pacific Islanders, Siberians or other American Indians. The ‘more than one wave’ model for the peopling of the Americas is supported.

Six Y-chromosome linked microsatellites were typed in a sample of 135 unrelated males representing three different ethnic groups: Arabs, Berbers and Blacks of Jerba Island (Tunisia). Analysis of variation at the six Y chromosome STRs showed significant differences in allele distributions between the Black group and the two other Islander groups. The Black group revealed the highest level of genetic diversity as compared to Arabs and Berbers, while the latter group was the most homogenous. Allele frequencies obtained for the three islander groups analysed were compared to data available for some European, Mediterranean and African populations. Principal-coordinate analyses showed genetic differentiation between the three geographically closed groups of Jerba. The absence of the YAP insertion marker and the position of Arabs and Jerban Blacks near the European cluster would suggest their relative ‘admixture’ with European populations.

Seven Y-chromosome STR loci, DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392 and DYS393 have been analysed in population samples of Angolares, Forros and Tongas, three ethnic groups from the African archipelago of São Tomé e Príncipe (Gulf of Guinea). Complete typings were obtained for 103 chromosomes, which belonged to 79 different haplotypes. The mean heterozygosity per locus in the overall São Tomean sample was 0.566, with the highest value found among Forros and the lowest among Angolares. Angolares also showed the lowest level of haplotype diversity. On average, the mean pairwise difference between two random haplotypes from Angolares, Forros and Tongas was 4.69, 6.74 and 6.23 repeats, respectively. The genetic distances were found to be statistically significant between Angolares and Forros or Tongas. In accordance, AMOVA revealed that the percentage of variation attributable to differences among groups was only significant when we distinguished between Angolares and non-Angolares. Globally, these results indicate that, with respect to the pool of male lineages of São Tomé e Príncipe, some genetic sub-structuring does exist, basically determined by the Angolares ethnic group.

In this work we present results on DYS434, DYS437, DYS438 and DYS439 loci studied in three population samples from North Portugal (N: 69), Macao (N: 59) and Mozambique (N: 64). Gene and haplotype diversity values are presented and compared. Gene diversity values ranged from 0.0290 to 0.0620 for DYS434; 0.0615 to 0.5959 for DYS437; 0.4906 to 0.6424 for DYS438; 0.5873 to 0.7038 for DYS439. The highest average gene diversity was found in Macao and the lowest in Mozambique. Haplotype diversity in North Portugal, Macao and Mozambique was calculated to be 0.925, 0.928 and 0.787, respectively.

Allele and haplotype frequency distributions for all markers were compared between the three populations. At the haplotype level all the differences between pairs of samples were significant. Sequencing analysis was performed for all alleles found in the four loci. Identical flanking sequences were observed with most of the alleles differing only by the repeat number. However, two variations were found in allele 10 of DYS438: one in a Macao sample presenting a TTTTA before the last TTTTC repetition and one in a Portuguese sample showing a base substitution (A/C) at position 7 downstream from the tandem repeat. A base substitution (C/T) at position 3 upstream from the repeat motif was also found in allele 14 of DYS437 in a sample from Mozambique.

Transmission Disequilibrium Test (TDT)-based methods have been advocated by several authors for testing that a marker-phenotype association is actually due to linkage and not to uncontrolled stratification. As a pre-requisite of TDT-type methods is the presence of an association between marker and phenotype, one may wish to first investigate the association using a classical association study, and then to check by a TDT approach whether this association is actually due to linkage. We propose an estimating equation (EE) procedure, to compute analytically the minimum sample size of sibship data required to detect the association between a marker and a quantitative phenotype, and that required to confirm it by two TDT methods. We show that, when the marker allele frequency is low or high, the number of informative sibs needed in TDT-type methods can be lower than the number required in an association analysis, and even more so when the familial clustering is strong. However, in all cases, the number of sibs that need to be sampled to get the appropriate number of informative sibs for analysis is always larger for TDT methods than for an association study. In a phenotype-first strategy, this number may be critical when investigating costly phenotypes.

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes with random population samples. We develop a LOD score approach for exclusion analyses of candidate genes with random population samples. Under this approach, specific genetic effects and inheritance models at candidate genes can be analysed and if a LOD score is [les ] − 2.0, the locus can be excluded from having an effect larger than that specified. Computer simulations show that, with sample sizes often employed in association studies, this approach has high power to exclude a gene from having moderate genetic effects. In contrast to regular association analyses, population admixture will not affect the robustness of our analyses; in fact, it renders our analyses more conservative and thus any significant exclusion result is robust. Our exclusion analysis complements association analysis for candidate genes in random population samples and is parallel to the exclusion mapping analyses that may be conducted in linkage analyses with pedigrees or relative pairs. The usefulness of the approach is demonstrated by an application to test the importance of vitamin D receptor and estrogen receptor genes underlying the differential risk to osteoporotic fractures.