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At least 685 records · Page 38Linked to original sources
PubMed2001

Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes.

In general, common diseases do not follow a Mendelian inheritance pattern. To identify disease mechanisms and etiology, their genetic dissection may be assisted by evaluation of linkage in mouse models of human disease. Statistical modeling of multiple-locus linkage data from the nonobese diabetic (NOD) mouse model of type 1 diabetes has previously provided evidence for epistasis between alleles of several Idd (insulin-dependent diabetes) loci. The construction of NOD congenic strains containing selected segments of the diabetes-resistant strain genome allows analysis of the joint effects of alleles of different loci in isolation, without the complication of other segregating Idd loci. In this article, we analyze data from congenic strains carrying two chromosome intervals (a double congenic strain) for two pairs of loci: Idd3 and Idd10 and Idd3 and Idd5. The joint action of both pairs is consistent with models of additivity on either the log odds of the penetrance, or the liability scale, rather than with the previously proposed multiplicative model of epistasis. For Idd3 and Idd5 we would also not reject a model of additivity on the penetrance scale, which might indicate a disease model mediated by more than one pathway leading to beta-cell destruction and development of diabetes. However, there has been confusion between different definitions of interaction or epistasis as used in the biological, statistical, epidemiological, and quantitative and human genetics fields. The degree to which statistical analyses can elucidate underlying biologic mechanisms may be limited and may require prior knowledge of the underlying etiology.

Animals↗
PubMed2000

The inter-regional distribution of HLA class II haplotypes indicates the suitability of the Sardinian population for case-control association studies in complex diseases.

We have analysed HLA class II gene-based substructure of the Sardinian population in order to evaluate the possible influence of this parameter in the mapping of common disease loci using association methods. We first examined the distribution of the HLA-DRB1-DQA1-DQB1 haplotypes in 631 newborns from seven different regions of the island, and found that the most frequent haplotypes were uniformly distributed in all regions, but at frequencies unique to Sardinia. Other haplotypes, common in other white European populations, are consistently rare or absent across the whole island. Analysis of molecular variance (AMOVA) showed a very low degree of genetic differentiation between the coastal regions, which have suffered repeated invasions over many years, and the most internal and isolated part of the island. This suggests that there has been little genetic flow from the various populations that have invaded the island during the last 3000 years and that Sardinia is a relatively homogeneous population. The validity of these unrelated control HLA haplotype frequencies and our claim of homogeneity were established by demonstrating the near identity of the affected family-based control (AFBAC) HLA haplotype frequencies in 243 type 1 diabetes and 495 multiple sclerosis families from Sardinia and those of the unrelated controls. These results indicate that robust case-control studies can be carried out in Sardinia offering cost efficiency over certain family-based designs.

Alleles↗
PubMed2003

The Newfoundland population: a unique resource for genetic investigation of complex diseases.

The population of the province of Newfoundland and Labrador is genetically isolated. This isolation is evidenced by an overabundance of several monogenic disorders. The Newfoundland population, like that of other isolates, is now the focus of interest for identification of genes implicated in common diseases. However, the utility of such populations for this purpose remains unproven. In this paper, we review the current genetic architecture of the province, with respect to geographic isolation, homogeneity, founder effect, genetic drift and extended linkage disequilibrium. Based on these factors, we propose that the population of Newfoundland offers many advantages for genetic mapping of common diseases, compared with admixed populations, and even compared with other isolates.

Founder Effect↗
PubMed2006

Predictive testing for complex diseases using multiple genes: fact or fiction?

PURPOSE: There is ongoing debate about whether testing low-risk genes at multiple loci will be useful in clinical care and public health. We investigated the usefulness of multiple genetic testing using simulated data. METHODS: Usefulness was evaluated by the area under the receiver-operating characteristic curve (AUC), which indicates the accuracy of genetic profiling in discriminating between future patients and nonpatients. The AUC was investigated in relation to the number of genes assumed to be involved, the risk allele frequency, the odds ratio of the risk genotypes, and to the proportion of variance explained by genetic factors as an approximation of the heritability of the disease. RESULTS: We demonstrated that a high (AUC > 0.80) to excellent discriminative accuracy (AUC > 0.95) can be obtained by simultaneously testing multiple susceptibility genes. A higher discriminative accuracy is obtained when genetic factors play a larger role in the disease, as indicated by the proportion of explained variance. The maximum discriminative accuracy of future genetic profiling can be estimated at present from the heritability and prevalence of disease. CONCLUSIONS: Genetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease.

Area Under Curve↗
PubMed2025

The impact of polygenic score and socioeconomic status in predicting risk for 19 complex diseases.

Both socioeconomic circumstances and genetic predisposition shape disease risk, yet their joint contribution across diseases has not been systematically examined. We studied 19 high-burden diseases in 743,194 participants (729,928 European; 13,266 non-European ancestry) from FinnGen, the UK Biobank, and Generation Scotland. Higher educational attainment was associated with lower risk of most conditions, but with higher risk of most common cancers. These associations were largely independent of disease-specific polygenic scores (PGSs). For seven out of 19 diseases, PGSs showed stronger effects among individuals with high education. Joint inclusion of education and PGSs modestly improved prediction for 14 and 10 out of 19 diseases in FinnGen and the UK Biobank, respectively. PGS associations were consistent across ancestries, whereas education effects were less stable; results using an alternative socioeconomic measure were directionally similar but smaller. Our findings highlight the distinct and partly interacting contributions of socioeconomic and genetic factors to disease risk.

Journal Article↗