Genome-wide screening for localization of disease genes necessitates the efficient reconstruction of haplotypes of members of a pedigree from genotype data at multiple loci. We propose a genetic algorithmic approach to haplotyping and show that it works fast, efficiently and reliably. This algorithm uses certain principles of biological evolution to find optimal solutions to complex problems. The optimality criterion used in the present problem is the minimum number of recombinations over possible haplotype configurations of members of a pedigree. The proposed algorithm is much less demanding in terms of data and assumption requirements compared to the currently used likelihood-based methods of haplotype reconstruction. It also provides multiple optimal haplotype configurations of a pedigree, if such multiple optima exist.

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This article considers the amount of economic capital that defined benefit pension schemes potentially need to cover the risks they are running. A real open scheme, the Universities Superannuation Scheme, is modelled and used to illustrate our results and, as expected, economic capital requirements are large. We discuss the appropriateness of these results and what they mean for the defined benefit pension scheme industry and their sponsors. The article is particularly pertinent following the recent European Commission Green Paper on the future of European pensions systems, its call for advice on reviewing the Institutions for Occupational Retirement Provision Directive and the introduction of the Basel 2 and Solvency 2 risk-based regulatory regimes for banking and insurance respectively. keywordsDefined benefit pension scheme, Economic capital, Solvency, Stochastic modelling, Asset-liability management.

The impact that capital structure and capital asset allocation have on financial services firm economic capital and risk adjusted performance is considered. A stochastic modelling approach is used in conjunction with banking and insurance examples. It is demonstrated that gearing up Tier 1 capital with Tier 2 capital can be in the interests of bank Tier 1 capital providers, but may not always be so for insurance Tier 1 capital providers. It is also shown that, by allocating a bank or insurance firm's Tier 1 and Tier 2 capital to higher yielding, more risky assets, risk adjusted performance can be enhanced. These results are particularly pertinent with the advent of the new Basel 2 and Solvency 2 risk based capital initiatives, for banks and insurers respectively.

Rapid advances in genetic epidemiology and the setting up of large-scale cohort studies have shifted the focus from severe, but rare, single gene disorders to less severe, but common, multifactorial disorders. This will lead to the discovery of genetic risk factors for common diseases of major importance in insurance underwriting. If genetic information continues to be treated as private, adverse selection becomes possible, but it should occur only if the individuals at lowest risk obtain lower expected utility by purchasing insurance at the average price than by not insuring. We explore where this boundary may lie, using a simple 2 × 2 gene-environment interaction model of epidemiological risk, in a simplified 2-state insurance model and in a more realistic model of heart-attack risk and critical illness insurance. Adverse selection does not appear unless purchasers are not very risk-averse, and insure a small proportion of their wealth; or unless the elevated risks implied by genetic information are implausibly high. In many cases adverse selection is impossible if the low-risk stratum of the population is large enough. These observations are strongly accentuated in the critical illness model by the presence of risks other than heart attack, and the constraint that differential heart-attack risks must agree with the overall population risk. We find no convincing evidence that adverse selection is a serious insurance risk, even if information about multifactorial genetic disorders remains private.