Using genomic data to make indirect (and unauthorized) estimates of disease risk

The number of genetic factors associated with common human traits and disease is increasing rapidly, and the general public is utilizing affordable, direct-to-consumer genetic tests. The results of these tests are often in the public domain. A combination of factors has increased the potential for the indirect estimation of an individual's risk for a particular trait. Here we explain the basic principals underlying risk estimation which allowed us to test the ability to make an indirect risk estimation from genetic data by imputing Dr. James Watson's redacted apolipoprotein E gene (APOE) information. The principles underlying risk prediction from genetic data have been well known and applied for many decades, however, the recent increase in genomic knowledge, and advances in mathematical and statistical techniques and computational power, make it relatively easy to make an accurate but indirect estimation of risk. There is a current hazard for indirect risk estimation that is relevant not only to the subject but also to individuals related to the subject; this risk will likely increase as more detailed genomic data and better computational tools become available.

Epidemiological data of seasonal variation in mood and behaviour

The aim of this study was to measure seasonal variation in mood and behaviour. The dual vulnerability and latitude effect hypothesis, the risk of increased appetite, weight and other seasonal symptoms to develop metabolic syndrome, and perception of low illumination in quality of life and mental well-being were assessed. These variations are prevalent in persons who live in high latitudes and need balancing of metabolic processes to adapt to environmental changes due to seasons. A randomized sample of 8028 adults aged 30 and over (55% women) participated in an epidemiological health examination study, The Health 2000, applying the probability proportional to population size method for a range of socio-demographic characteristics. They were present in a face-to-face interview at home and health status examination. The questionnaires included the modified versions of the Seasonal Pattern Assessment Questionnaire (SPAQ) and Beck Depression Inventory (BDI), the Health Related Quality of Life (HRQoL) instrument 15D, and the General Health Questionnaire (GHQ). The structured and computerized Munich Composite International Diagnostic Interview (M-CIDI) as part of the interview was used to assess diagnoses of mental disorders, and, the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) criteria were assessed using all the available information to detect metabolic syndrome. A key finding was that 85% of this nationwide representative sample had seasonal variation in mood and behaviour. Approximately 9% of the study population presented combined seasonal and depressive symptoms with a significant association between their scores, and 2.6% had symptoms that corresponded to Seasonal Affective Disorder (SAD) in severity. Seasonal variations in weight and appetite are two important components that increase the risk of metabolic syndrome. Other factors such as waist circumference and major depressive disorder contributed to the metabolic syndrome as well. Persons reported of having seasonal symptoms were associated with a poorer quality of life and compromised mental well-being, especially if indoors illumination at home and/or at work was experienced as being low. Seasonal and circadian misalignments are suggested to associate with metabolic disorders, and could be remarked if individuals perceive low illumination levels at home and/or at work that affect the health-related quality of life and mental well-being. Keywords: depression, health-related quality of life, illumination, latitude, mental well-being, metabolic syndrome, seasonal variation, winter.

Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

Strategies for collecting screen-less oscillation data

Screen-less oscillation photography is the method of choice for recording three-dimensional X-ray diffraction data for crystals of biological macromolecules. The geometry of an oscillation camera is extremely simple. However, the manner in which the reciprocal lattice is recorded in any experiment is fairly complex. This depends on the Laue symmetry of the reciprocal lattice, the lattice type, the orientation of the crystal on the camera and to a lesser extent on the unit-cell dimensions. Exploring the relative efficiency of collecting X-ray diffraction data for different crystal orientations prior to data collection might reduce the number of films required to record most of the unique data and the consequent amount of time required for processing these films. Here algorithms are presented suitable for this purpose and results are reported for the 11 Laue groups, different lattice types and crystal orientations often employed in data collection.

Spatially based data delivery and recording sys-Aust Sugar Cane industry

Establish an internet platform where spatially referenced data can be viewed, entered and stored.