Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips.

BACKGROUND: Illumina's Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensities from each SNP into genotype calls (AA, AB, BB). Various algorithms which make use of different statistical models are available for this task. We compare four methods (GenCall, Illuminus, GenoSNP and CRLMM) on data where the true genotypes are known in advance and data from a recently published genome-wide association study. RESULTS: In general, differences in accuracy are relatively small between the methods evaluated, although CRLMM and GenoSNP were found to consistently outperform GenCall. The performance of Illuminus is heavily dependent on sample size, with lower no call rates and improved accuracy as the number of samples available increases. For X chromosome SNPs, methods with sex-dependent models (Illuminus, CRLMM) perform better than methods which ignore gender information (GenCall, GenoSNP). We observe that CRLMM and GenoSNP are more accurate at calling SNPs with low minor allele frequency than GenCall or Illuminus. The sample quality metrics from each of the four methods were found to have a high level of agreement at flagging samples with unusual signal characteristics. CONCLUSIONS: CRLMM, GenoSNP and GenCall can be applied with confidence in studies of any size, as their performance was shown to be invariant to the number of samples available. Illuminus on the other hand requires a larger number of samples to achieve comparable levels of accuracy and its use in smaller studies (50 or fewer individuals) is not recommended.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis

Common diseases such as endometriosis (ED), Alzheimer's disease (AD) and multiple sclerosis (MS) account for a significant proportion of the health care burden in many countries. Genome-wide association studies (GWASs) for these diseases have identified a number of individual genetic variants contributing to the risk of those diseases. However, the effect size for most variants is small and collectively the known variants explain only a small proportion of the estimated heritability. We used a linear mixed model to fit all single nucleotide polymorphisms (SNPs) simultaneously, and estimated genetic variances on the liability scale using SNPs from GWASs in unrelated individuals for these three diseases. For each of the three diseases, case and control samples were not all genotyped in the same laboratory. We demonstrate that a careful analysis can obtain robust estimates, but also that insufficient quality control (QC) of SNPs can lead to spurious results and that too stringent QC is likely to remove real genetic signals. Our estimates show that common SNPs on commercially available genotyping chips capture significant variation contributing to liability for all three diseases. The estimated proportion of total variation tagged by all SNPs was 0.26 (SE 0.04) for ED, 0.24 (SE 0.03) for AD and 0.30 (SE 0.03) for MS. Further, we partitioned the genetic variance explained into five categories by a minor allele frequency (MAF), by chromosomes and gene annotation. We provide strong evidence that a substantial proportion of variation in liability is explained by common SNPs, and thereby give insights into the genetic architecture of the diseases

What role for genetics in the prediction of multiple sclerosis?

For most of us, the foundations of our understanding of genetics were laid by considering Mendelian diseases in which familial recurrence risks are high, and mutant alleles are both necessary and sufficient. One consequence of this deterministic teaching is that our conceptualization of genetics tends to be dominated by the notion that the genetic aspects of disease are caused by rare alleles exerting large effects. Unfortunately, the preconceptions that flow from this training are frequently erroneous and misleading in the context of common traits, where familial recurrence risks are modest, and for the most part the relevant alleles are neither rare, necessary, nor sufficient. For these common traits, the genetic architecture is far more complex, with susceptibility rather than causality resulting from the combined effects of many alleles, each exerting only a modest effect on risk. None of these alleles is sufficient to cause disease on its own, and none is essential for the development of disease. Furthermore, most are carried by large sections of the population, the vast majority of which does not develop the disease. One consequence of our innate belief in the Mendelian paradigm is that we have an inherent expectation that knowledge about the genetic basis for a disease should allow genetic testing and thereby accurate risk prediction. There is an inevitable feeling that the same should be true in complex disease, but is it

MicroRNAs miR-17 and miR-20a Inhibit T Cell Activation Genes and Are Under-Expressed in MS Whole Blood

It is well established that Multiple Sclerosis (MS) is an immune mediated disease. Little is known about what drives the differential control of the immune system in MS patients compared to unaffected individuals. MicroRNAs (miRNAs) are small non-coding nucleic acids that are involved in the control of gene expression. Their potential role in T cell activation and neurodegenerative disease has recently been recognised and they are therefore excellent candidates for further studies in MS. We investigated the transcriptome of currently known miRNAs using miRNA microarray analysis in peripheral blood samples of 59 treatment naïve MS patients and 37 controls. Of these 59, 18 had a primary progressive, 17 a secondary progressive and 24 a relapsing remitting disease course. In all MS subtypes miR-17 and miR-20a were significantly under-expressed in MS, confirmed by RT-PCR. We demonstrate that these miRNAs modulate T cell activation genes in a knock-in and knock-down T cell model. The same T cell activation genes are also up-regulated in MS whole blood mRNA, suggesting these miRNAs or their analogues may provide useful targets for new therapeutic approaches

Low and moderate frequency variants in MERTK are independently associated with Multiple Sclerosis susceptibility with discordant association dependent upon HLA-DRB1*1501 status.

Background: Susceptibility to Multiple Sclerosis (MS) is mediated by both genetic and environmental factors. Studies have shown that the family of tyrosine receptor kinases known as the TAMs (Tyro3, Axl, Mertk), play important roles in myelination and immune responses, and genetic variations within MERTK are associated with susceptibility to MS. Objectives: In order to identify functionally relevant variants associated with MS risk, we performed fine mapping of MERTK, as well as expression analyses in immune cell types isolated from people with MS and healthy controls. Methods: We employed a next generation sequencing strategy of individuals selected on the basis of MERTK haplotype to identify potential functional variants, followed by association testing of identified variants, with a particular focus on low frequency and novel variants within MERTK. We performed expression analysis (RNA) and protein analysis (flow cytometry) in immune cell subtypes in conjunction with MERTK genetics to determine the functional consequences of MS risk associated variants. Results: Low and moderate frequency variants within MERTK were independently associated with MS susceptibility. Importantly, our results demonstrated that alternate alleles at the same position within MERTK were associated with MS risk, dependent on HLA-DRB1*1501 haplotype status. Furthermore, MS susceptibility variants in MERTK were associated with altered expression of Mertk in human monocytes. Conclusions: Genetic variants within MERTK are associated with increased risk of MS, and these variants correlate with altered Mertk expression in human monocytes. Altered expression of Mertk, dependent on the allele present and in the context of HLA-DRB1*1501 status, may have consequences for monocyte function and protection against development of MS