Genetic risk score predicting accelerated progression from mild cognitive impairment to Alzheimer's disease.

Aside from APOE, the genetic factors that influence the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) remain largely unknown. We assessed whether a genetic risk score (GRS), based on eight non-APOE genetic variants previously associated with AD risk in genome-wide association studies, is associated with either risk of conversion or with rapid progression from MCI to AD. Among 288 subjects with MCI, follow-up (mean 26.3 months) identified 118 MCI-converters to AD and 170 MCI-nonconverters. We genotyped ABCA7 rs3764650, BIN1 rs744373, CD2AP rs9296559, CLU rs1113600, CR1 rs1408077, MS4A4E rs670139, MS4A6A rs610932, and PICALM rs3851179. For each subject we calculated a cumulative GRS, defined as the number of risk alleles (range 0-16) with each allele weighted by the AD risk odds ratio. GRS was not associated with risk of conversion from MCI to AD. However, MCI-converters to AD harboring six or more risk alleles (second and third GRS tertiles) progressed twofold more rapidly to AD when compared with those with less than six risk alleles (first GRS tertile). Our GRS is a first step toward development of prediction models for conversion from MCI to AD that incorporate aggregate genetic factors.

Adding up autism risks

The causes of autism and autism spectrum disorder (ASD) are complex, and contain elements of both nature (genes) and the environment. New research published in BioMed Central's open access journal Molecular Autism shows that common genetic polymorphisms (genetic variation) can add up to an increased risk of ASD.

Read more at: http://medicalxpress.com/news/2012-10-adding-autism.html#jCp

High-throughput analysis of epistasis in genome-wide association studies with BiForce

Gene–gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS.

Results: We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case–control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits.

Availability and implementation: The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/.

Gene-gene and gene-environmental interactions of childhood asthma: a multifactor dimension reduction approach.

The importance of gene-gene and gene-environment interactions on asthma is well documented in literature, but a systematic analysis on the interaction between various genetic and environmental factors is still lacking.

METHODOLOGY/PRINCIPAL FINDINGS:

We conducted a population-based, case-control study comprised of seventh-grade children from 14 Taiwanese communities. A total of 235 asthmatic cases and 1,310 non-asthmatic controls were selected for DNA collection and genotyping. We examined the gene-gene and gene-environment interactions between 17 single-nucleotide polymorphisms in antioxidative, inflammatory and obesity-related genes, and childhood asthma. Environmental exposures and disease status were obtained from parental questionnaires. The model-free and non-parametrical multifactor dimensionality reduction (MDR) method was used for the analysis. A three-way gene-gene interaction was elucidated between the gene coding glutathione S-transferase P (GSTP1), the gene coding interleukin-4 receptor alpha chain (IL4Ra) and the gene coding insulin induced gene 2 (INSIG2) on the risk of lifetime asthma. The testing-balanced accuracy on asthma was 57.83% with a cross-validation consistency of 10 out of 10. The interaction of preterm birth and indoor dampness had the highest training-balanced accuracy at 59.09%. Indoor dampness also interacted with many genes, including IL13, beta-2 adrenergic receptor (ADRB2), signal transducer and activator of transcription 6 (STAT6). We also used likelihood ratio tests for interaction and chi-square tests to validate our results and all tests showed statistical significance.

CONCLUSIONS/SIGNIFICANCE:

The results of this study suggest that GSTP1, INSIG2 and IL4Ra may influence the lifetime asthma susceptibility through gene-gene interactions in schoolchildren. Home dampness combined with each one of the genes STAT6, IL13 and ADRB2 could raise the asthma risk.

The Fox and the Rabbits—Environmental Variables and Population Genetics (1) Replication Problems in Association Studies and the Untapped Power of GWAS (2) Vitamin A Deficiency, Herpes Simplex Reactivation and Other Causes of Alzheimer's Disease

Classical population genetics shows that varying permutations of genes and risk factors permit or disallow the effects of causative agents, depending on circumstance. For example, genes and environment determine whether a fox kills black or white rabbits on snow or black ash covered islands. Risk promoting effects are different on each island, but obscured by meta-analysis or GWAS data from both islands, unless partitioned by different contributory factors. In Alzheimer's disease, the foxes appear to be herpes, borrelia or chlamydial infection, hypercholesterolemia, hyperhomocysteinaemia, diabetes, cerebral hypoperfusion, oestrogen depletion, or vitamin A deficiency, all of which promote beta-amyloid deposition in animal models—without the aid of gene variants. All relate to risk factors and subsets of susceptibility genes, which condition their effects. All are less prevalent in convents, where nuns appear less susceptible to the ravages of ageing. Antagonism of the antimicrobial properties of beta-amyloid by Abeta autoantibodies in the ageing population, likely generated by antibodies raised to beta-amyloid/pathogen protein homologues, may play a role in this scenario. These agents are treatable by diet and drugs, vitamin supplementation, pathogen detection and elimination, and autoantibody removal, although again, the beneficial effects of individual treatments may be tempered by genes and environment.

Genetic testing in epilepsy -- it takes more than 1 gene

Voltage sensitive and ligand gated ion channels control neuronal excitability and play a key role in the abnormal discharges in epilepsy . Certain channel mutations can cause epilepsy, but individuals with the same mutations can also be epilepsy free. When looking at sequence differences in many channels at once, some functional differences can act together or cancel each other out. This is an important pointer and relevant to many other complex diseases.

Klassen et al, Cell Paper

Scientists Map Changes In Genetic Networks Caused By DNA Damage - Science News - redOrbit

If you think that genes are static, and isolated , think again: Hundreds are wired together in signalling networks that change in many different epistatic ways in response to stress. I suppose this means that one SNP in one gene could affect the expression of hundreds of others. These could of course be more relevant that the seed gene. Analysis of signalling networks may be the way forward in disease gene analysis.

RAPID detection of gene–gene interactions in genome-wide association studies

Interactions between genes, or between genes and risk factors (epistasis ) can modify the risk promoting effects of either gene or risk factor. Because so many genes are involved in polygenic diseases, there are probably millions of possible permutations of hundreds of polymorphisms, that are beyond even today's computing power. This paper goes some way towards resolving the problem.

Histone Deacetylases and Mood Disorders: Epigenetic Programming in Gene-Environment Interactions.

Sodium valproate

Biological Validation of Increased Schizophrenia Risk With NRG1, ERBB4, and AKT1 Epistasis via Functional Neuroimaging in Healthy Controls.

Several hundred genes have been implicated in schizophrenia and many more epistatasis interactions are likely. Each gene's effect  may be conditioned by those of many others, accounting in part for the disparity in gene association studies.