A framework for power and sensitivity analyses for quantitative genetic studies of natural populations, and case studies in Soay sheep (Ovis aries)

Studies of the quantitative genetics of natural populations have contributed greatly to evolutionary biology in recent years. However, while pedigree data required are often uncertain (i.e. incomplete and partly erroneous) and limited, means to evaluate the effects of such uncertainties have not been developed. We have therefore developed a general framework for power and sensitivity analyses of such studies. We propose that researchers first generate a set of pedigree data that they wish to use in a quantitative genetic study, as well as data regarding errors that occur in that pedigree. This pedigree is then permuted using the data regarding errors to generate hypothetical 'true' and 'assumed' pedigrees that differ so as to mimic pedigree errors that might occur in the study system under consideration. Phenotypic data are then simulated across the true pedigree (according to user-defined genetic and environmental covariance structures), before being analysed with standard quantitative genetic techniques in conjunction with the 'assumed' pedigree data. To illustrate this approach, we conducted power and sensitivity analyses in a well-known study of Soay sheep (Ovis aries). We found that, although the estimation of simple genetic (co)variance structures is fairly robust to pedigree errors, some potentially serious biases were detected under more complex scenarios involving maternal effects. Power analyses also showed that this study system provides high power to detect heritabilities as low as about 0.09. Given this range of results, we suggest that such power and sensitivity analyses could greatly complement empirical studies, and we provide the computer program PEDANTICS to aid in their application.     

Impacts and interactions of PDGFRB, MMP-3, TIMP-2, and RNF213 polymorphisms on the risk of Moyamoya disease in Han Chinese human subjects

Polymorphisms of PDGFRB, MMP-3, TIMP-2, RNF213, TGFB1, Raptor and eNOS genes have been associated with Moyamoya disease (MMD) separately in studies, but their interactions on MMD have never been evaluated in one study. This study enrolled 96 MMD patients and 96 controls to evaluate the contributions and interactions of these polymorphisms on MMD in Chinese Hans. After genotyping, five polymorphisms loci were deemed suitable for analysis, rs3828610 in PDGFRB, rs3025058 in MMP-3, rs8179090 in TIMP-2, rs112735431 and rs148731719 in RNF213. Interactions of different loci on MMD were evaluated by multifactor dimensionality reduction (MDR) method. Significantly higher frequencies of A allele and G/A genotype of rs112735431 in RNF213 were observed in MMD patients compared with controls (P = 0.011; P = 0.018, respectively). In the dominant model, G/A genotype of rs112735431 was associated with increased risk of MMD (P = 0.018). A higher frequency of G allele and G/G genotype of rs148731719 in RNF213 gene in patient than control group (P < 0.001; P < 0.01, respectively) was also detected. No significant association between MMD and other three loci (P > 0.05) was detected. MDR analysis failed to detect any significant interaction among these five loci in the occurrence of MMD (P > 0.05), but the combination of three loci (rs112735431 in RNF213, rs3828610 in PDGFRB, rs3025058 in MMP-3) could have the maximum testing accuracy (57.29%) and cross-validation consistency (10/10). The results indicated that RNF213 rs112735431 and rs148731719 may exert a significant influence on MMD occurrence. Compared with this overwhelming effect, the influences of PDGFRB, MMP-3, and TIMP-2 on MMD may be unremarkable in Chinese Hans. There may be no prominent interaction among these five gene polymorphisms on the occurrence of MMD.