Increasing the power of genome wide association studies in natural populations using repeated measures : evaluation and implementation

1. Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful. 2. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden. 3. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response. 4. The method is available in the R package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.

Women's experiences of two different self-assessment methods for monitoring fetal movements in full-term pregnancy : a crossover trial

Background: Low maternal awareness of fetal movements is associated with negative birth outcomes. Knowledge regarding pregnant women's compliance with programs of systematic self-assessment of fetal movements is needed. The aim of this study was to investigate women's experiences using two different self-assessment methods for monitoring fetal movements and to determine if the women had a preference for one or the other method. Methods: Data were collected by a crossover trial; 40 healthy women with an uncomplicated full-term pregnancy counted the fetal movements according to a Count-to-ten method and assessed the character of the movements according to the Mindfetalness method. Each self-assessment was observed by a midwife and followed by a questionnaire. A total of 80 self-assessments was performed; 40 with each method. Results: Of the 40 women, only one did not find at least one method suitable. Twenty of the total of 39 reported a preference, 15 for the Mindfetalness method and five for the Count-to-ten method. All 39 said they felt calm, relaxed, mentally present and focused during the observations. Furthermore, the women described the observation of the movements as safe and reassuring and a moment for communication with their unborn baby. Conclusions: In the 80 assessments all but one of the women found one or both methods suitable for self-assessment of fetal movements and they felt comfortable during the assessments. More women preferred the Mindfetalness method compared to the count-to-ten method, than vice versa.