Heritability of young- and old-onset ischaemic stroke.

BACKGROUND AND PURPOSE: Although the genetic contribution to stroke risk is well known, it remains unclear if young-onset stroke has a stronger genetic contribution than old-onset stroke. This study aims to compare the heritability of ischaemic stroke risk between young and old, using common genetic variants from whole-genome array data in population-based samples. METHODS: This analysis included 4050 ischaemic stroke cases and 5765 controls from six study populations of European ancestry; 47% of cases were young-onset stroke (age < 55 years). To quantify the heritability for stroke risk in these unrelated individuals, the pairwise genetic relatedness was estimated between individuals based on their whole-genome array data using a mixed linear model. Heritability was estimated separately for young-onset stroke and old-onset stroke (age ≥ 55 years). RESULTS: Heritabilities for young-onset stroke and old-onset stroke were estimated at 42% (±8%, P < 0.001) and 34% (±10%, P < 0.001), respectively. CONCLUSIONS: Our data suggest that the genetic contribution to the risk of stroke may be higher in young-onset ischaemic stroke, although the difference was not statistically significant.

Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors.

Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor interactions of carcinogens, non-carcinogens, and mammary carcinogens to a set of 5494 target proteins. Leave-one-out validations of the ligand mammary carcinogen-non-carcinogen model had a concordance between experimental and predicted results of 71%, and the mammary carcinogen-non-mammary carcinogen model was 72% concordant. The development of a hybrid fragment-ligand model improved the concordances to 85 and 83%, respectively. In a separate external validation exercise, hybrid fragment-ligand models had concordances of 81 and 76%. Analyses of example rat mammary carcinogens including the food mutagen and oestrogenic compound PhIP, the herbicide atrazine, and the drug indomethacin; the ligand model identified a number of proteins associated with each compound that had previously been referenced in Medline in conjunction with the test chemical and separately with association to breast cancer. This new modelling approach can enhance model predictivity and help bridge the gap between chemical structure and carcinogenic activity by descriptors that are related to biological targets.