Inheritance of human longevity in Iceland

The idea that human longevity is influenced by genetic factors has recently received strong support from work on other species. On the basis of partial population studies and selected kinships, significant correlations between the ages of parents and offspring have been reported, and some but not all twin studies have confirmed that human longevity is moderately inherited. However, studies based upon a relatively small proportion of a population are susceptible to sampling error and selection bias. Here we report the use of a comprehensive population-based computerised genealogy database to examine multigenerational relationships among those who live to the 95th percentile in Iceland. We have developed a clustering tool which can generate large extended pedigrees connecting individuals from any list using the genealogy database. First degree relatives of those living to the 95th percentile are almost twice as likely to live to the 95th percentile compared with controls. Furthermore, we have developed an algorithm which we have named the Minimum Founder Test (MFT) to examine the degree of relatedness of any population-based list of individuals to estimate whether a trait has a familial component. The data indicate that there is a significant genetic component to longevity. In addition, age-specific death rates are significantly lower in the offspring of long-lived parents compared with controls, especially after age 70.

Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans

Complex disorders such as diabetes, cardiovascular disease, asthma, hypertension and psychiatric illnesses account for a large and disproportionate share of health care costs, but remain poorly characterized with respect to aetiology. The transmission of such disorders is complex, reflecting the actions and interactions of multiple genetic and environmental factors. Genetic analyses that allow for the simultaneous consideration of susceptibility from multiple regions may improve the ability to map genes for complex disorders, but such analyses are currently computationally intensive and narrowly focused. We describe here an approach to assessing the evidence for statistical interactions between unlinked regions that allows multipoint allele-sharing analysis to take the evidence for linkage at one region into account in assessing the evidence for linkage over the rest of the genome. Using this method, we show that the interaction of genes on chromosomes 2 (NIDDM1) and 15 (near CYP19) makes a contribution to susceptibility to type 2 diabetes in Mexican Americans from Starr County, Texas.

Sequential imputation and multipoint linkage analysis

A novel Monte Carlo method for linkage analyses involving large pedigrees and many polymorphic loci is introduced. Issues related to the efficiency of the method are discussed.

Importance sampling. I. Computing multimodel p values in linkage analysis

In linkage analysis, when the lod score is maximized over multiple genetic models, standard asymptotic approximation of the significance level does not apply. Monte Carlo methods can be used to estimate the p value, but procedures currently used are extremely inefficient. We propose a Monte Carlo procedure based on the concept of importance sampling, which can be thousands of times more efficient than current procedures. With a reasonable amount of computing time, extremely accurate estimates of the p values can be obtained. Both theoretical results and an example of maturity-onset diabetes of the young (MODY) are presented to illustrate the efficiency performance of our method. Relations between single-model and multimodel p values are explored. The new procedure is also used to investigate the performance of asymptotic approximations in a single model situation.