Regressive logistic and proportional hazards disease models for within-family analyses of measured genotypes, with application to a CYP17 polymorphism and breast cancer

Various statistical methods have been proposed to evaluate associations between measured genetic variants and disease, including some using family designs. For breast cancer and rare variants, we applied a modified segregation analysis method that uses the population cancer incidence and population-based case families in which a mutation is known to be segregating. Here we extend the method to a common polymorphism, and use a regressive logistic approach to model familial aggregation by conditioning each individual on their mother's breast cancer history. We considered three models: 1) class A regressive logistic model; 2) age-of-onset regressive logistic model; and 3) proportional hazards familial model. Maximum likelihood estimates were calculated using the software MENDEL. We applied these methods to data from the Australian Breast Cancer Family Study on the CYP17 5UTR TC MspA1 polymorphism measured for 1,447 case probands, 787 controls, and 213 relatives of case probands found to have the CC genotype. Breast cancer data for first- and second-degree relatives of case probands were used. The three methods gave consistent estimates. The best-fitting model involved a recessive inheritance, with homozygotes being at an increased risk of 47% (95% CI, 28-68%). The cumulative risk of the disease up to age 70 years was estimated to be 10% or 22% for a CYP17 homozygote whose mother was unaffected or affected, respectively. This analytical approach is well-suited to the data that arise from population-based case-control-family studies, in which cases, controls and relatives are studied, and genotype is measured for some but not all subjects.

Molecular genetics and comparative genomics reveal RNAi is not functional in malaria parasites

Techniques for targeted genetic disruption in Plasmodium, the causative agent of malaria, are currently intractable for those genes that are essential for blood stage development. The ability to use RNA interference (RNAi) to silence gene expression
would provide a powerful means to gain valuable insight into the pathogenic blood stages but its functionality in Plasmodium remains controversial. Here we have used various RNA-based gene silencing approaches to test the utility of RNAi in malaria
parasites and have undertaken an extensive comparative genomics search using profile hidden Markov models to clarify whether RNAi machinery
exists in malaria. These investigative approaches revealed that Plasmodium lacks the enzymology required for RNAi-based ablation of gene expression
and indeed no experimental evidence for RNAi was observed. In its absence, the most likely explanations for previously reported RNAi-mediated knockdown are either the general toxicity of introduced RNA (with global down-regulation of gene expression) or a specific antisense effect mechanistically distinct from RNAi, which will need systematic
analysis if it is to be of use as a molecular genetic tool for malaria parasites.