Correction: Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment.

Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer's disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer's Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups (p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.

Examining diabetic heel ulcers through an ecological lens: microbial community dynamics associated with healing and infection.

PURPOSE: While some micro-organisms, such as Staphylococcus aureus, are clearly implicated in causing tissue damage in diabetic foot ulcers (DFUs), our knowledge of the contribution of the entire microbiome to clinical outcomes is limited. We profiled the microbiome of a longitudinal sample series of 28 people with diabetes and DFUs of the heel in an attempt to better characterize the relationship between healing, infection and the microbiome. METHODOLOGY: In total, 237 samples were analysed from 28 DFUs, collected at fortnightly intervals for 6 months or until healing. Microbiome profiles were generated by 16S rRNA gene sequence analysis, supplemented by targeted nanopore sequencing.Result/Key findings. DFUs which failed to heal during the study period (20/28, 71.4 %) were more likely to be persistently colonized with a heterogeneous community of micro-organisms including anaerobes and Enterobacteriaceae (log-likelihood ratio 9.56, P=0.008). During clinically apparent infection, a reduction in the diversity of micro-organisms in a DFU was often observed due to expansion of one or two taxa, with recovery in diversity at resolution. Modelling of the predicted species interactions in a single DFU with high diversity indicated that networks of metabolic interactions may exist that contribute to the formation of stable communities. CONCLUSION: Longitudinal profiling is an essential tool for improving our understanding of the microbiology of chronic wounds, as community dynamics associated with clinical events can only be identified by examining changes over multiple time points. The development of complex communities, particularly involving Enterobacteriaceae and strict anaerobes, may be contributing to poor outcomes in DFUs and requires further investigation.

Discovery by the Epistasis Project of an epistatic interaction between the GSTM3 gene and the HHEX/IDE/KIF11 locus in the risk of Alzheimer's disease.

Despite recent discoveries in the genetics of sporadic Alzheimer's disease, there remains substantial "hidden heritability." It is thought that some of this missing heritability may be because of gene-gene, i.e., epistatic, interactions. We examined potential epistasis between 110 candidate polymorphisms in 1757 cases of Alzheimer's disease and 6294 control subjects of the Epistasis Project, divided between a discovery and a replication dataset. We found an epistatic interaction, between rs7483 in GSTM3 and rs1111875 in the HHEX/IDE/KIF11 gene cluster, with a closely similar, significant result in both datasets. The synergy factor (SF) in the combined dataset was 1.79, 95% confidence interval [CI], 1.35-2.36; p = 0.00004. Consistent interaction was also found in 7 out of the 8 additional subsets that we examined post hoc: i.e., it was shown in both North Europe and North Spain, in both men and women, in both those with and without the ε4 allele of apolipoprotein E, and in people older than 75 years (SF, 2.27; 95% CI, 1.60-3.20; p < 0.00001), but not in those younger than 75 years (SF, 1.06; 95% CI, 0.59-1.91; p = 0.84). The association with Alzheimer's disease was purely epistatic with neither polymorphism showing an independent effect: odds ratio, 1.0; p ≥ 0.7. Indeed, each factor was associated with protection in the absence of the other factor, but with risk in its presence. In conclusion, this epistatic interaction showed a high degree of consistency when stratifying by sex, the ε4 allele of apolipoprotein E genotype, and geographic region.

Investigating statistical epistasis in complex disorders.

The missing heritability exhibited by late-onset Alzheimer's disease is unexplained and has been partly attributed to epistatic interaction. Methods available to explore this are often based on logistic regression and allow for determination of deviation from an expected outcome as a result of statistical epistasis. Three such methodologies including Synergy Factor and the PLINK modules, -epistasis and -fast-epistasis, were applied to study an epistatic interaction between interleukin-6 and interleukin-10. The models analyzed consisted of two synergistic interactions (SF ≈ 4.2 and 1.6) and two antagonistic interactions (SF ≈ 0.9 and 0.6). As with any statistical test, power to detect association is paramount; and most studies will be underpowered for the task. However, the availability of large sample sizes through genome-wide association studies make it feasible to examine approaches for determining epistatic interactions. This study documents the sample sizes needed to achieve a statistically significant outcome from each of the methods examined and discusses the limitations/advantages of the chosen approaches.