The large literature on family-based tests of association and/or linkage is reviewed, concentrating on the underlying principles and on recent methodological developments. We explain the distinction between testing for association and testing for linkage, and give our views on the circumstances in which each is the appropriate null hypothesis.

Rapid prenatal diagnoses of major chromosome aneuploidies have been achieved successfully using quantitative fluoresent PCR (QF–PCR) assays and small tandem repeat (STR) markers. Here we report the results of evaluating the use of previously untested X-linked STRs, (DXS6803) and (DXS6809), together with modified amelogenin (AMXY) sequences and the X22 marker that maps in the pseudoautosomal region PAR2 on the long arm of the X and Y chromosomes. These markers will allow prenatal diagnoses of sex chromosome aneuploidies such as 45,X (pure Turner Syndrome), 47,XXY and 47,XYY, while assessing the sex of the fetuses. Data are also presented concerning the difficulties associated with the evaluation of the frequencies of the various types of sub-populations of cells in amniotic fluid samples collected from fetuses with sex chromosome mosaicism. The results of evaluating the use of new markers for the rapid diagnosis of aneuploidies affecting chromosomes 21,18 and 13 are also presented. Three chromosome 21 specific STRs have been found to produce trisomic triallelic or diallelic patterns from all amniotic samples retrieved from fetuses with Down Syndrome. Since all samples tested were amplified and no false positive or negative results were observed, the present results confirm the diagnostic value of QF–PCR for the prenatal detection of major numerical chromosome disorders.

The polymorphic alleles of the human ABO-Secretor locus (FUT2 or Se) show high heterogeneity and overt ethnic specificity. To provide additional data for analysis to elucidate the origins of populations, we have investigated the allelic polymorphism of FUT2 in 40 unrelated Tibetan and 53 Tamang individuals from Nepal, 42 Indonesian individuals from Surabaya, and 55 Uygur individuals from Urumqi, using DNA sequencing. In Tibetan, Tamang and Indonesian populations, the frequency of a nonfunctional allele, se357,385, which is found only in Asian populations, was 0·638, 0·509 and 0·631, respectively. In Uygur, the se428, which is common in Caucasian populations, and the se357,385 consisting of two common nonfunctional FUT2 alleles, had frequencies of 0·3 and 0·145, respectively. The fixation index (FST) based on genetic differentiation was obtained pairwise among the four populations in this study and six populations in our previous studies. The results suggested that genetic differentiation among Tibetan, Tamang, Indonesian and East Asian populations is very low, while the distribution feature of the FUT2 alleles in the Uygur population implied an admixture of European with Asian. The distribution of nonfunctional alleles at the FUT2 locus provided further evidence of human migration among the Asian populations.

A sample of mitochondrial DNA (mtDNA) from the southeastern African population of Mozambique has been shown to have affinities with populations both to its north and south. From the north came sequences that may have been involved in the Bantu expansion (from western, through eastern, to southern Africa), such as members of haplogroups L3b, L3e1a and a subset of L1a. The dating of the major component of Mozambican mtDNAs, the subset L2a of haplogroup L2, displayed an age range compatible with the Bantu expansion. The southern influence was traced by the presence of sequence types from haplogroup L1d, a probable relict of Khoisan-speaking populations that inhabited the region prior to their displacement by the Bantu-speaking incomers. Within historical times, the forced displacement of Mozambicans as part of the slave trade, mainly documented as being to the Americas, generated a differential input of eastern African sequences into the mtDNA pools of the Americas and of Europe, as testified to by the greater number of sequence matches between Mozambique and the Americas, compared to those between Mozambique and Europe.

The frequency of Wilson's disease in many populations is thought to be about one in 40000 persons, based on case and autopsy studies. Although the Wilson's disease gene has been identified, there is such a large number of mutations already known that it is not currently feasible to determine disease gene frequency by mutation analysis of a population. We have used a novel approach to obtain an estimate of the number of cases of Wilson's disease expected at birth in the US Caucasian population. We used data from four studies to determine that approximately one-third of Wilson's disease mutations in US Caucasian Wilson's disease patients are due to His→Gln at the 1069 position. We then determined the frequency of this mutation in random DNA samples from 2601 US Caucasian newborns to be 0·285%. Multiplying by three gives an expected Wilson's disease heterozygote frequency of 0·855% and an allele frequency of 0·428%, or 0·00428. These data translate into a Wilson's disease frequency of about one in 55000 births. The 95% confidence interval is rather broad, ranging from about one in 18000 to one in 700000 births, but will be reduced as more data are added.

Linkage analysis and mouse model knockout studies indicate that loci/genes mapping in different chromosome 1 regions are good candidates for nonsyndromic orofacial cleft (OFC) malformation. On this basis, three different regions of the chromosome 1 have been analysed, by linkage analysis, in 38 families with nonsyndromic OFC. Positive scores were obtained by pairwise analysis and a non-parametric linkage approach for the 1p36 region, with markers close to the MTHFR locus. Additional results allowed us to exclude the presence of an OFC susceptibility gene in the 1q21 and 1q32-42·3 regions.

A number of methods have previously been described which carry out linkage analysis considering information for two or more loci simultaneously. Apart from some ad hoc methods such as analysing subsamples, these methods use information regarding linkage at all loci under consideration. However, if the actual genotype-specific effects are known for some loci then it would be preferable to consider the genotypes of these loci directly, rather than the amount of allele-sharing they demonstrate. Here we present an extension to our likelihood-based method of model-free linkage analysis as implemented in the MFLINK program. This allows the incorporation of liability classes. The genotypes of a locus known to affect risk can be used to assign subjects to liability classes prior to carrying out linkage tests at other loci. An example application is presented for genome scan data on Alzheimer's disease with analysis conditional on Apoliprotein E (APOE) genotypes. The results provide support for the existence of additional susceptibility loci linked to D10S1211 and to D12S358.

The gamete competition model is a likelihood version of the transmission disequilibrium test (TDT) that is inspired by conditional logistic regression and the Bradley–Terry ranking procedure. In family-based association studies, both the TDT and the gamete competition model apply directly to data on a single nucleotide polymorphism (SNP). Because any given SNP has limited polymorphism, it is tempting to collect several SNPs within a gene into a single super marker whose alleles are haplotypes. Unfortunately, this tactic wreaks havoc with the traditional TDT, which requires codominant markers (Spielman et al. 1993; Terwilliger & Ott, 1992). Eliminating phase ambiguities by assigning haplotypes to individuals before conducting the TDT may give misleading results because only the most probable haplotypes are then considered. Because pedigree implementations of the gamete competition model can accommodate dominant as well as codominant markers, they circumvent the phase problem by including all possible phases weighted by their estimated frequencies.

Typically, genome scans for complex disease have produced linkage peaks which have proved difficult to replicate in additional independent studies. Here we confirm that this may be due to the large variance in magnitude and position of the linkage statistics when maximized across a region. Simulations suggest that for genes of moderate effect (locus-specific sibling relative risks λs in the range 1.23–1.39), sample sizes of less than 500 affected sib pairs will give unacceptably large standard errors in the magnitudes and locations of significant linkage results. For genes of small effect (λs [les ] 1.13), sample sizes in the region of 1000–2000 pairs may be required to achieve consistency of results between different studies. These figures have important implications for our confidence in location estimates for disease genes obtained from linkage studies of modest size. In particular, collection of larger data sets and/or analysis strategies such as conditioning or narrowing the phenotype definition, in order to increase the relative effect size, may be required before embarking on positional cloning.

A year ago there was hope that a finished sequence of the human genome would soon be publicly available and would give a more reliable locus order than an unconstrained radiation hybrid or genetic map. Alas, there are now different draft orders for each region, none of which may be correct because of gaps, uncertain polarity of contigs, and errors in assembly. Shortly before these drafts became available, we analysed allelic association (also called linkage disequilibrium, LD) in the FRAX region in a large sample of haplotypes (Ennis et al. 2000). We demonstrate here that this material discriminates among alternative draft orders. To express support for discrimination between two values of χ2 = −2 ln L we use the Akaike criterion AIC = df[χ2/min χ2 −1]. Excluding premutations and full mutations at FMR1, all maps have 715 degrees of freedom (df) among 717 pairs of alleles after accepting L = 0 and estimating M, ∈ in the Malecot equation E(ρ) = Me −∈d, where ρ is the association between a pair of alleles at distance d. An AIC in excess of 2 provides evidence against a map with the larger χ2 .