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.Geneettisessä epidemiologiassa on tavallista kerätä perheaineistoja väestöpohjaisista tautirekistereistä. Tällaisten sairauden perusteella valikoituneiden aineistojen tilastollisessa käsittelyssä on poimintatapa otettava huomioon. Kun lisäksi analysoitavana on tauti, jonka sairastumisikä voi vaihdella, kuten tyypin 1 diabetes, on luonnollista olettaa, että osa ihmisistä ei sairastu tautiin vaikka heitä seurattaisiin hyvin pitkään. He saattavat olla taudille ei-alttiita (immuuneja). Nykyisin käytössä olevat tilastolliset menetelmät soveltuvat heikosti edellä kuvattujen aineistojen analysointiin. Tässä työssä kehitettiin uusia tilastollisia menetelmiä väestöpohjaisten rekisteriaineistojen geneettiseen analyysiin. Lisäksi analysoitiin tyypin 1 diabeteksen ja siihen liittyvän munuaissairauden (nefropatia) geneettisiä tekijöitä. Väestöpohjaisen rekisteriaineiston tilastollisissa analyyseissa syntyy valikoitumisesta johtuvaa harhaa, joka korjataan rajoittamalla analyysi esimerkiksi rekisteröintiin johtaneen yksilön tiettyihin sisaruksiin. Tässä työssä käytetään myös toista tapaa korjata otannasta johtuvaa harhaa, jossa yhdistetään sairastumisikä- ja genotyyppitiedot tautirekisteristä, ulkopuolinen otos terveiden genotyypeistä sekä väestötiedoista saatavat syntymäkohorttien tiedot ja muodostetaan koko Suomen väestön kattava tilastollisen malli geneettisten vaikutusten tutkimiseksi. Tällä tavalla vahvistettiin HLA-DRB1 geenin hyvin tunnettu assosiaatio tyypin 1 diabetekseen. Toisen tässä työssä kehitetyn tilastollisen mallin avulla osoitetaan, että tyypin 1 diabeteksen ilmaantuvuuden kasvua Suomessa vuosina 1965-1996 ei voida selittää tautialttiutta lisäävän geenin rikastumisella väestössä insuliinin keksimisen jälkeen, vaan muita tekijöitä tarvitaan näin nopeasti tapahtuvan kasvun selittämiseen. Myöskään tyypin 1 diabeteksen kromosomin 6 HLA-alueella sijaitsevien alttiusgeenien esitetylle rikastumiselle ei-mendelistisellä periytymismekanismilla ei löydetty tukea, kun perheaineiston tautirekisteriin perustuva valikoituminen oli huomioitu tilastollisessa analyysissa. Tilastollisia malleja kehitettiin myös tautialttiuden ja sairastumisiän geneettisten tekijöiden samanaikaisesti tapahtuvaan analysointiin. Näiden mallien avulla voidaan tutkia sekä tautialttiuden ja sairastumisiän kasautumista perheaineistoissa ilman mitattuja geneettisiä markkereita että mitattujen geneettisten markkereiden tautiassosiaatioita. Tyypin 1 diabeteksen kromosomin 6 HLA-DRB1, HLA-A ja HLA-B alueelta löydettiin viitteitä assosiaatiosta sekä alttiuteen että sairastumisikään. Analysoitaessa tyypin 1 diabetekseen liittyvää nefropatiaa havaittiin perheiden välillä vaihtelua nefropatia-alttiudessa. Kokonaisuutena tämä työ tarjoaa uusia tilastollisia menetelmiä sairastumisiältään vaihtelevan taudin geneettisten tekijöiden analyysiin, kun käytössä on väestöpohjainen rekisteriaineisto. Tyypin 1 diabeteksen sairastumisiän ja kromosomin 6 HLA-DRB1 lokuksen välinen, aikaisemmin tunnettu assosiaatio vahvistettiin väestöpohjaisessa tilastollisessa analyysissa

Family-based Bayesian collapsing method for rare-variant association study

In this study, we analyze the Genetic Analysis Workshop 18 data to identify the genes and underlying single-nucleotide polymorphisms on 11 chromosomes that exhibit significant association with systolic blood pressure. We propose a novel family-based method for rare-variant association detection based on the hierarchical Bayesian framework. The method controls spurious associations caused by population stratification, and improves the statistical power to detect not only individual rare variants, but also genes with either continuous or binary outcomes. Our method utilizes nuclear family information, and takes into account the effects of all single-nucleotide polymorphisms in a gene, using a hierarchical model. When we apply this method to the genome-wide Genetic Analysis Workshop 18 data, several genes and single-nucleotide polymorphisms are identified as potentially related to systolic blood pressure.Peer reviewe

Potts model for haplotype associations

Bayesian spatial modeling has become important in disease mapping and has also been suggested as a useful tool in genetic fine mapping. We have implemented the Potts model and applied it to the Genetic Analysis Workshop 14 (GAW14) simulated data. Because the "answers" were known we have analyzed latent phenotype P1-related observed phenotypes affection status (genetically determined) and i (random) in the Danacaa population replicate 2. Analysis of the microsatellite/single-nucleotide polymorphism-based haplotypes at chromosomes 1 and 3 failed to identify multiple clusters of haplotype effects. However, the analysis of separately simulated data with postulated differences in the effects of the two clusters has yielded clear estimated division into the two clusters, demonstrating the correctness of the algorithm. Although we could not clearly identify the disease-related and the non-associated groups of haplotypes, results of both GAW14 and our own simulation encourage us to improve the efficiency and sensitivity of the estimation algorithm and to further compare the proposed method with more traditional methods

On Exploring Hidden Structures Behind Cervical Cancer Incidence

Finding new etiological components is of great interest in disease epidemiology. We consider time series version of invariant coordinate selection (tICS) as an exploratory tool in the search of hidden structures in the analysis of population-based registry data. Increasing cancer burden inspired us to consider a case study of age-stratified cervical cancer incidence in Finland between the years 1953 and 2014. The latent components, which we uncover using tICS, show that the etiology of cervical cancer is age dependent. This is in line with recent findings related to the epidemiology of cervical cancer. Furthermore, we are able to explain most of the variation of cervical cancer incidence in different age groups by using only two latent tICS components. The second tICS component, in particular, is interesting since it separates the age groups into three distinct clusters. The factor that separates the three clusters is the median age of menopause occurrence.Peer reviewe

Development of Body Mass Index of Japanese Triplets From Birth Until the Onset of Puberty

Peer reviewe

Weight growth of triplet infants from birth to twelve years of age

WOS:000309095700009We analyzed the characteristics associated with the growth in weight of Japanese triplets from birth to 12 years of age. The study included 376 mothers and their 1,128 triplet children, who were born between 1978 and 2006. Data were collected through a mailed questionnaire sent to the mothers asking for information recorded in medical records. For these births, data on triplets' weight growth, gestational age, sex, parity, maternal age at delivery, maternal height, and maternal body mass index were obtained from records in the Maternal and Child Health Handbooks and records in the school where children receive health check-ups. The weight deficit of the triplets compared to the general population of Japan remained between 10% and 17% until 12 years of age. Moreover, at 12 years of age, the differences of weight between the general population and triplets were approximately -4.75 kg for boys and -6.00 kg for girls. Very low birth weight had the strongest contribution to body weight until 8 years of age. After 8 years of age, maternal body mass index was a significant factor affecting the weight of triplets until 12 years of age.Peer reviewe

Layperson-Oriented versus Clinical-Based Models for Assessing 10-Year Incidence of Coronary Heart Disease: National FINRISK Study

One laboratory-based and two non-laboratory-based models with and without blood pressure measures are developed based on data of 14815 men and 16617 women aged 25–64 years. During the followup 1134 men and 566 women developed coronary heart disease (CHD). The area under the receiver operating characteristic curve (95% CI) for prediction of CHD incidence was 0.823 (0.807–0.839) for the laboratory-based model, 0.808 (0.791–0.824) and 0.803 (0.787–0.820) for the non-laboratory-based models with and without systolic blood pressure in men (P < 0.01 for overall comparison), and 0.878 (0.856–0.901), 0.871 (0.848–0.894), and 0.864 (0.840–0.887), respectively, in women (P < 0.01). The predicted rates matched well with the observed ones (P > 0.10). Compared with the model without blood pressure, the non-laboratory-based model with blood pressure tended to reclassify individuals into the higher risk categories for both event and nonevent groups in both genders. The study concludes the predictive property of the non-laboratory-based models are good

Hematologisten syöpien ilmaantuvuus lisääntyy mutta ennuste paranee

Teema : Hematologiset syövä