Making Optimal Use of and Extending beyond Polygenic Additive Liability Models

Ioannidis, John P. A.

doi:10.1159/000448200

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…As pointed out recently [1], this search is conducted within a paradigm assuming “genetic variance in a population [is] due to a large number of Mendelian factors, each making a small additive contribution to a particular phenotype, the socalled ‘infinitesimal model’” [2]. This “genes of small effect” paradigm is now usually assumed for all complex traits, including both quantitative and binary traits [1,3]. The dominant viewpoint further assumes environmental inputs modify (or “contribute to”) the structural relationships and effects (“risk”) of many (dozens to hundreds) of these genes of small effect.…”

Section: Introductionmentioning

confidence: 99%

A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes

Alper

Larsen

Trautwein

et al. 2019

Journal of Autoimmunity

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The incidence of type 1 diabetes (T1D) and some other complex diseases is increasing. The cause has been attributed to an undefined changing environment. We examine the role of the environment (or any changing non-genetic mechanism) in causing the rising incidence, and find much evidence against it: 1) Dizygotic twin T1D concordance is the same as siblings of patients in general; 2) If the environment is responsible for both the discordance among identical twins of patients with T1D and its rising incidence, the twin concordance rate should be rising, but it is not; 3) Migrants from high- to low-incidence countries continue to have high-incidence children; 4) TID incidence among the offspring of two T1D parents is identical to the monozygotic twin rate. On the other hand, genetic association studies of T1D have revealed strong susceptibility in the major histocompatibility complex and many optional additive genes of small effect throughout the human genome increasing T1D risk. We have, from an analysis of previously published family studies, developed a stochastic epigenetic Mendelian oligogenic (SEMO) model consistent with published observations. The model posits a few required recessive causal genes with incomplete penetrance explaining virtually all of the puzzling features of T1D, including its rising incidence and the specific low T1D incidence rates among first-degree relatives of patients. Since historic selection against any causal gene could prevent T1D, we postulate that the rising incidence is because of increasing population mixing of parents from some previously isolated populations that had selected against different causal genes.

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Section: Introductionmentioning

confidence: 99%

A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes

Alper

Larsen

Trautwein

et al. 2019

Journal of Autoimmunity

View full text Add to dashboard Cite

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How Can We Explain Very Low Odds Ratios in GWAS? I. Polygenic Models

Hodge

Greenberg²

2016

Hum Hered

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Genome-wide association studies of common diseases often identify a number of disease-related SNPs that reach highly significant p values but at the same time show very low disease odds ratios (ORs), most <1.5 and many <1.2. Despite their statistical significance, associations involving very low ORs explain little about the genetic contribution to the disease and nothing about disease inheritance. A commonly accepted explanation for very low ORs involves a model of polygenic inheritance, i.e., where the disease being studied is caused by a large number of interacting genes, each gene contributing only a small increment to disease risk. Here we demonstrate the perhaps counterintuitive result that, within a reasonable range of disease population prevalences (≤10%), a pure polygenic model is incompatible with very low ORs, unless very large numbers (hundreds or even thousands) of polygenic loci are involved.

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Making Optimal Use of and Extending beyond Polygenic Additive Liability Models

Cited by 2 publications

References 22 publications

A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes

A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes

How Can We Explain Very Low Odds Ratios in GWAS? I. Polygenic Models

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