The expected power of genome-wide linkage disequilibrium (LD) testing for a low-frequency disease variant was examined using a simple genetic model in which the degree of LD between the disease variant and the adjacent single nucleotide polymorphism (SNP) marker decreases in proportion to the number of generations since the LD-generating event. In this study, the frequency of the SNP marker being in complete LD with a low-frequency disease variant at the LD-generating event was regarded as the random variable having the probability distribution expected from the neutral infinite sites model, which enables us to derive the formula for calculating the expected power of genome-wide LD testing without determining the allele frequency of the associated SNP marker. Such a treatment is essential for the evaluation of the power of LD testing, because the frequency of the associated marker allele is always unknown. The main results obtained are as follows: (1) genome-wide LD testing with a case-control design could identify a disease variant with a high penetrance, while a low-frequency disease variant showing a low penetrance is difficult to detect; (2) although the degree of LD increases as the number of markers increases, the power of LD testing does not necessarily increase after the significance level is adjusted by the šidák correction or the Bonferroni correction based on the number of testings; (3) the use of SNP markers with only high-frequency minor alleles is more powerful for detecting LD even with a low-frequency disease variant than the use of SNP markers with both high- and low-frequency minor alleles. Thus, the study design of LD testing must be evaluated prior to the investigation. The present study will provide a guideline for determining the number of SNP markers and the range of SNP allele frequencies suitable for genome-wide LD testing.

In order to study the matrilineal genetic composition in Cabo Verde (Republic of Cape Verde), an archipelago that used to serve as a Portuguese entrepôt of the Atlantic slave trade, we have analysed a total of 292 mtDNAs sampled from the seven inhabitated islands for the hypervariable segment I (HVS-I) and some characteristic RFLPs of the coding regions. The different settlement history of the northwestern group of the islands is well reflected in the mtDNA pool. The total Cabo Verde sample clearly displays the characteristic mitochondrial features of the Atlantic fringe of western Africa and testifies to almost no mitochondrial input from the Portuguese colonizers.

Ancient diversity in Sub-Saharan Africa is known to have been re-modulated to a large extent by Bantu migrations in the sub-Sahel region, in two southwards waves of advance through both the west and east coasts. Haplotype matching performed for Y-STR haplotypes in several sub-Saharan populations, both inside and outside the migration path, allowed the confirmation of a putative founder haplotype, and its one-step neighbours, of Bantu origin, and detected an increasing drift towards the south, with a stronger reduction of diversity along the western coast. A mixed frequency distribution for the Bantu haplotype core in South Africa, relative to the western and eastern pools, seems to provide evidence for the intermingling between both Bantu waves in that region. The proportion of male lineages considered as predating the Bantu expansion reached 8.8% in Mozambique. Further influence on sub-Saharan diversity may have occurred during the colonial period; in Mozambique, the European genetic impact in the male component was estimated to be around 5.9%, in significant contrast with the female counterpart where no European lineages were detected.

We have previously described extending our method of ‘model-free’ linkage analysis, implemented in the MFLINK program, in order to deal with liability classes. This allows a new form of conditional two-locus linkage analysis, meaning that the genotypes of a known risk locus can be used to define liability classes so that their effects can be incorporated in tests for linkage at additional loci. In this method, relationships between transmission models for different liability classes were constrained so that there was a constant multiplicative effect on penetrance values. Here we present further extensions to the method to allow for different relationships. In particular, rather than only having a multiplicative effect on risk of affection we now allow specification of a multiplicative effect on risk of non-affection, or a combination of both relationships, across liability classes. We now also allow specification of an additive effect on penetrance. By way of example, we apply these methods to genome scan data for Alzheimer's disease using apolipoprotein E genotype to define liability classes. We show that, although in general the different methods produce results which tend to be quite highly correlated, certain markers can produce quite different results according to the method applied and that these could well lead to differences of interpretation. Without knowing a priori which relationship is likely to be most appropriate to describe the overall combined effect of the two loci one might be obliged to apply a number of different methods. This in turn may lead to the familiar difficulties associated with multiple testing. Nevertheless, the new method allows researchers greater flexibility in analysing linkage data for diseases in which one or more risk polymorphisms have already been identified.

It is important that case-control samples be drawn from a genetically homogeneous population in order to avoid artefactual false positive results and to enhance power to detect disease mutations and markers in linkage disequilibrium with them. Tests which simply compare overall marker allele frequencies between cases and controls will fail to identify a relatively small number of subjects drawn from a different genetic background who could usefully be discarded from the sample. Such subjects can be identified using multilocus tests, but previously described tests have been unnecessarily complex and cumbersome for this simple application. We describe a straightforward test, implemented in the CHECKHET program, which uses a measure of genetic difference and permutation procedures to rapidly identify such subjects using genotypes from multiple unlinked markers. It seems to perform reasonably well on simulated data, and with real data appears to identify two abnormal subjects within a case-control sample. We recommend that such tests be routinely applied to case-control samples once sufficient numbers of markers have been genotyped within them.

A popular method for the analysis of case-control association studies is to compare the frequencies of the alleles between cases and controls by means of Pearson's χ2-statistic. Here, an approach for computing the power of this test is presented, which by computer simulation is shown to be more reliable than a previously published power approximation. Since the test based on Pearson's χ2- statistic can be anti-conservative if there is an excess of homozygotes for the susceptibility allele in the general population, it has been proposed to analyze case-control association studies by means of a trend test based on genotypes instead of alleles. We present an accurate power approximation for the trend test. The power approximations are implemented in an available computer program ‘GenOdyPower’, which in addition has an option to determine the empirical power of these tests by simulations.

Sibship methods have been shown to be more powerful than traditional sib-pair methods in mapping quantative trait loci. We propose a statistical procedure which integrates data on sibships into a so-called ‘contrast function’, a natural extension of the classical squared sib-pair trait difference proposed by Haseman & Elston (1972). We also develop a combined mean and contrast function which provides more information on linkage compared to the contrast function. Our method is extended to multiple, epistatically interacting trait loci. Monte-Carlo simulations are included to compare the efficiencies of the proposed procedures with some currently used methods. An application of our proposed method is presented using data on alcohol dependence.

Bone mineral content (BMC) and/or bone mineral density (BMD, i.e. BMC scaled by bone size) are major determinants for osteoporosis, which is a serious health problem. The major determinant of variation in BMD/BMC is genetic. The few studies now available are inconsistent in the identification and/or even in the existence of major gene(s) for BMD/BMC. In 51 human pedigrees with 941 individuals (526 measured for phenotypes) identified via probands with extreme BMD values, we performed complex segregation analyses to test the existence of a genetic locus with a major effect on BMD/BMC variation. We analyzed BMD and BMC at the spine, hip and wrist jointly by employing, as the study phenotype, factor scores (FS) of the principle component that explains ∼75% of the total BMD/BMC variation at the three sites. The results indicate that a major gene exists with a codominant effect that is responsible for ∼16% of the FS variation when adjusted for significant effects of sex, body weight and age. A significant genotype-×-sex-×-age interaction was found, which may explain ∼14% of the FS variation after adjusting for body weight. Testing of various models did not provide support for shared familial environmental effects but suggested the existence of residual polygenic effects, which may explain ∼50% of the FS variation when adjusting for sex, body weight and age. This study indicates a promising aspect of studies to identify a major gene for BMD/BMC variation in our pedigrees identified via extreme probands.

Ornithine transcarbamylase (OTC, EC 2.1.3.3) deficiency, the most common inborn error of urea cycle, is caused by a vast number of point mutations, deletions and insertions in the respective gene. Furthermore, 12 single nucleotide polymorphisms (SNPs) have been described in the OTC gene, four of them causing an amino acid change. We have studied the frequency of these markers in two populations: Portuguese and Mozambican. No significant differences were observed between populations, except for Lys→Arg in codon 46. Allelic associations between polymorphisms were used to define haplotypic patterns. The three common haplotypes (H1, H2 and H3) show a combined frequency of 95% in Portugal and 87% in Mozambique. One haplotype was found only in Portugal and three are only present in Mozambique, resulting in a higher haplotype diversity. The combined information from the SNPs and the DXS1068 (CA)n repeat was used to outline OTC haplotype phylogeny, which, in conjunction with the population data, allowed us to sketch possible evolutionary pathways, although some haplotypes could have arisen by either repeated mutation or recombination.

We show that the methods for calculating the power of the TDT of Risch & Merikangas (1996), McGinnis (1998, 2000), Tu & Whittemore (1999) and Knapp (1999) consist of three distinct approaches. The differences between the methods arise either by treating parental transmissions independently or not, and either conditioning on parental heterozygosity or not. Transmissions are only truly independent when the mode of inheritance of the disease is multiplicative, making this assumption invalid and the methods that assume them (Risch & Merikangas, 1996; McGinnis, 1998, 2000) inappropriate. We demonstrate a basic model that allows analytical comparisons of the methods, and suggest a new method that calculates power at least as accurately as any of the three previously published.

The euchromatic regions of chromosomes 21 and 22 are almost completely sequenced and have similar lengths (33.7–34.6 Mb). This similarity effectively controls for the influence of length, making comparisons of recombination and interference interesting. For both chromosomes, there is less male than female recombination, and male recombination is associated with GT/CA repeats. The striking sex difference may result from greater condensation of chromosomes in paternal meiosis, possibly restricting recombination to regions with longer repeat tracts and/or higher repeat densities. Chiasma interference in both sexes for chromosome 22 and in females for chromosome 21 is close to the genome average. Chromosome 21 is significantly different in male meiosis, with near complete interference, suggesting that even when double recombinants occur they are widely spaced. We propose that this difference is related to the different distribution of GT/CA dinucleotides. These repeats are widely distributed on chromosome 22, perhaps offering greater opportunities for double recombinants to occur within smaller regions, whereas they are largely subtelomeric in distribution on chromosome 21.

Gluten sensitive enteropathy has various manifestations, of which the two major forms are classical coeliac disease (cCD) and dermatitis herpetiformis (DH). In cCD predominantly the small intestine is affected, whereas in DH also the skin is affected showing typical rash and IgA deposits. The symptoms in both forms are dependent on gluten intake. The factors diversifying these two clinical outcomes are unknown. In the present report we evaluated the role of the major genetic susceptibility locus, HLA DQ, in 25 families, in which both forms of the disease, cCD and DH, occurred in siblings. By using the family-based approach it can be assumed that within each family variation in environmental factors is substantially lower than in the standard case-control setting, and also the problems related to population stratification can be avoided. Results from the Finnish family material with 25 discordant and 85 concordant sib pairs, and from additional case-control material comprising 71 unrelated Hungarian DH and 68 cCD patients, together indicated that the HLA DQ locus did not differ between the two major outcomes of gluten sensitive enteropathy. The non-HLA DR;DQ factors are critical for the different clinical manifestations of gluten sensitivity.

Genotyping costs still preclude analysis of a comprehensive SNP map in thousands of individual subjects in the search for disease susceptibility loci. Allele frequency estimation in DNA pools from cases and controls offers a partial solution, but variance in these estimates will result in some loss of statistical power. However, there has been no systematic attempt to quantify the several sources of error in previous studies. We report an analysis of the magnitude of variance components of each experimental stage in DNA pooling studies, and find that a design based on the formation of numerous small pools of approximately 50 individuals is superior to the formation of fewer, larger pools and the replication of any of the experimental stages. We conclude that this approach may retain an effective sample size greater than 68% of the true sample size, whilst offering a 60-fold reduction in DNA usage and a greater than 30-fold saving in cost, compared to individual genotyping. The possibility of combining pooling with informed selection of haplotype tag SNPs is also considered. In this way further savings in efficiency may be possible by using pooled allele frequency estimates to infer haplotype frequencies and hence, allele frequencies at untyped markers.

Linkage tests to localize oligogenes have been extended during the past year. Using simulated data and multiplex selection we find that several tests on affected sib pairs have comparable power and type I error. Three variants of SIBPAL2 are favoured when substantial numbers of normal sibs are included, but performance relative to the BETA benchmark degrades rapidly as normal sibs are depleted by selective sampling or typing. Neglect of this fact may explain recent failure of retrospective collaboration to confirm asthma candidates in the 5q cytokine region that are supported by other studies. A fully quantitative trait favours variance components under complete ascertainment and two options in SIBPAL2 under multiplex selection, with substantial gain in power from covariance analysis if the covariate is independent of the candidate locus. A dichotomy and liability threshold give virtually identical results in the SOLAR variance components program. Comparison with single-marker parametric analysis suggests that extension to multiple markers would be competitive with nonparametric methods in power, and superior in depth of genetic analysis. The simulated examples illustrate common problems encountered with linkage scans for oligogenes. They show that nonparametric methods provide no panacea for analytical problems posed by different phenotypes and methods of ascertainment.

In this paper we compare the power of the multivariate Haseman–Elston (MHE) test proposed earlier by Amos et al. (1990) and a computationally rapid new version of the multivariate Haseman–Elston test (NMHE) (Elston et al. 2000). We show that the power of NMHE was, for different simulation setups, identical or higher than that of MHE. In the bivariate case, the power of the NMHE method was somewhat less than that of the computationally intensive maximum likelihood variance components method (Amos et al. 2001). We present comparisons of the empirical distributions of the NMHE test to its limiting distributions for a range of numbers of traits. The distribution of the NMHE test appeared to conform satisfactorily to its limiting asymptotic distribution in large samples. Otherwise, empirical critical values for NMHE are somewhat higher than predicted, i.e. the test proposed by Elston et al. (2000) is non-conservative. The use of empirical critical values is therefore recommended for limited sample sizes (less than several hundred families). We also present the results of a linkage analysis performed by the NMHE method on a set of 4 body size-related traits. The method identified meaningful combinations of traits that showed significant linkage on chromosome 2 and suggestive linkage to regions on chromosomes 16 and 17.

Given the enormous progress in the knowledge of the human genome, genetic markers are now available throughout the genome. Haplotype analysis, allowing the simultaneous use of information from several markers, has thus become increasingly popular. However, we often face the problem of missing data and of haplotype identification. We have proposed a haplotype based method for the genetic study of multifactorial diseases in founder populations, the MILC method (Bourgain et al. 2000). MILC is based on the contrast of identity length between haplotypes transmitted to affected offspring and haplotypes non-transmitted. In this study, the impact of different strategies, regarding missing data, on the MILC method are evaluated. A real situation is considered where data are derived from a genome screen for asthma susceptibility alleles in the Hutterites. Results are illustrated on this asthma data set.

Previously, we have presented a data mining-based algorithmic approach to genetic association analysis, Haplotype Pattern Mining. We have now extended the approach with the possibility of analysing quantitative traits and utilising covariates. This is accomplished by using a linear model for measuring association. We present results with the extended version, QHPM, with simulated quantitative trait data. One data set was simulated with the population simulator package Populus, and another was obtained from GAW12. In the former, there were 2–3 underlying susceptibility genes for a trait, each with several ancestral disease mutations, and 1 or 2 environmental components. We show that QHPM is capable of finding the susceptibility loci, even when there is strong allelic heterogeneity and environmental effects in the disease models. The power of finding quantitative trait loci is dependent on the ascertainment scheme of the data: collecting the study subjects from both ends of the quantitative trait distribution is more effective than using unselected individuals or individuals ascertained based on disease status, but QHPM has good power to localize the genes even with unselected individuals. Comparison with quantitative trait TDT (QTDT) showed that QHPM has better localization accuracy when the gene effect is weak.

In this article, we provide an overview of the different statistical procedures that have been developed for linkage mapping of quantitative trait loci. We outline the model assumptions, the data requirements and the underlying tests for linkage for the different methods.