Stargardt Disease: towards developing a model to predict phenotype

Abstract

Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

The intronic ABCA4 c.5461‐10T>C variant, frequently seen in patients with Stargardt disease, causes splice defects and reduced ABCA4 protein level

“…The phenotype of patient 1, with most reduced level of ABCA4 protein, seems to be more severe than the phenotype of patient 2, fitting with previous reports (Heathfield et al. ). Thus, we have revealed the molecular mechanism explaining the pathogenicity of this variant and this explains why this variant is so common in patients with STGD.…”

Conservation of the coding regions of the glycine N-acyltransferase gene further suggests that glycine conjugation is an essential detoxification pathway

“…The data from the 1000 Genomes Project, the HapMap Project, and the Khoi-San/Bantu sequencing project was used. Since very few studies have focused on the analyses of genetic variation of a particular gene within the Afrikaner Caucasian population (Xu et al, 2009(Xu et al, , 2011(Xu et al, , 2012Kruse et al, 2009;Heathfield et al, 2013;RodriguezMurillo et al, 2014), we also started to investigate the genetic variation in the GLYAT gene of a small group of Afrikaner Caucasian individuals by sequencing the coding regions of their GLYAT genes. There is no data available for the GLYAT gene in the Afrikaner Caucasian population and this group is of particular interest as several founder mutations have been identified in the Afrikaner Caucasian population due to the dramatic population growth of the European settlers at the Cape since 1672 (Abecasis et al, 2004;Reeves et al, 2004;Roos et al, 2009;Tipping et al, 2001).…”

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

“…At present, quantitative prediction is widely used in various fields, and the model methods used are various, such as: neural network models were used to predict crash frequency [5,6], and Bayesian spatial generalized ordered logit model was developed for predicting the crash severity [7]; the integrated approach of belief rule base and deep learning was used to predict air pollution [8]; machine-learning techniques was used to accurately predict battery life [9]; regression model was used to energy performance of a net-zero energy building [10], etc, the results of these studies can help early analysis for the future situation of these research field, so as to do the planning work well in advance and reduce loss. In recent years, many mathematical model methods were used to predict the incidence of infectious diseases, such as linear model [11,12], dynamics model [13,14], grey model [15], time series ARIMA model, neural network model, and so on. Since the time series of infectious diseases often have the characteristics of trend and randomness, ARIMA model and neural network model can capture the regularity of such data well, so they were most widely used and obtained good prediction performance and high prediction accuracy [16][17][18][19][20][21][22][23][24].…”