Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Human Genetics
Bent MüllerHolger Kirsten

Abstract

Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or mor...Continue Reading

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Citations

Oct 17, 2016·Diabetes Research and Clinical Practice·Tinashe ChikoworeKarin R Conradie
Dec 5, 2017·Brain and Behavior·Bent MüllerArndt Wilcke
Feb 20, 2019·Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova·G V RukavishnikovN G Neznanov
May 3, 2019·Human Genetics·Anne-Laure BoulesteixInke R König
Jun 18, 2017·The EPMA Journal·Tingting Cheng, Xianquan Zhan
Jun 28, 2018·Molecular Neurobiology·Wu ChenXin Wang
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Dec 8, 2020·Frontiers in Genetics·Hongxiang LuAnqiang Zhang
Dec 20, 2020·Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova·A O KibitovN G Neznanov
Aug 19, 2021·Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova·E D KasyanovG E Mazo

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Methods Mentioned

BETA
genotyping
biopsy

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