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      Common and Low Frequency Variants in MERTK Are Independently Associated with Multiple Sclerosis Susceptibility with Discordant Association Dependent upon HLA-DRB1*15: 01 Status

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          Abstract

          Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system. The risk of developing MS is strongly influenced by genetic predisposition, and over 100 loci have been established as associated with susceptibility. However, the biologically relevant variants underlying disease risk have not been defined for the vast majority of these loci, limiting the power of these genetic studies to define new avenues of research for the development of MS therapeutics. It is therefore crucial that candidate MS susceptibility loci are carefully investigated to identify the biological mechanism linking genetic polymorphism at a given gene to the increased chance of developing MS. MERTK has been established as an MS susceptibility gene and is part of a family of receptor tyrosine kinases known to be involved in the pathogenesis of demyelinating disease. In this study we have refined the association of MERTK with MS risk to independent signals from both common and low frequency variants. One of the associated variants was also found to be linked with increased expression of MERTK in monocytes and higher expression of MERTK was associated with either increased or decreased risk of developing MS, dependent upon HLA-DRB1*15: 01 status. This discordant association potentially extended beyond MS susceptibility to alterations in disease course in established MS. This study provides clear evidence that distinct polymorphisms within MERTK are associated with MS susceptibility, one of which has the potential to alter MERTK transcription, which in turn can alter both susceptibility and disease course in MS patients.

          Author Summary

          Multiple sclerosis (MS) is the most common neurological disease of young Caucasian adults. Oligodendrocytes are the key cell type damaged in MS, a process that is accompanied by loss of the myelin sheath that these cells produce, resulting in demyelination and ultimately in secondary damage to nerve cells. Susceptibility to MS is strongly influenced by genes, and over 100 genes have now been linked with the risk of developing MS. However, surprisingly little is known about the biological mechanism by which any one of these genes increases the probability of developing MS. In this study we have explored in detail the links between one known MS risk gene, MERTK, and MS susceptibility. We found that a number of different alterations in the MERTK gene are independently associated with the risk of developing MS. One these changes was also linked with changes in the level of expression of MERTK in monocytes, an immune cell type known to be involved in the etiology of MS. In an unexpected result, we found this expression-linked alteration in MERTK was either protective or risk-associated, depending on the genotype of the individual at another well known MS risk gene known as HLA-DRB1. In addition, we found that not only were alterations in MERTK associated with MS susceptibility, but potentially with ongoing disease course, indicating that MERTK may be a good target for the development of novel MS therapeutics.

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          Most cited references35

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

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          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            Risk alleles for multiple sclerosis identified by a genomewide study.

            Multiple sclerosis has a clinically significant heritable component. We conducted a genomewide association study to identify alleles associated with the risk of multiple sclerosis. We used DNA microarray technology to identify common DNA sequence variants in 931 family trios (consisting of an affected child and both parents) and tested them for association. For replication, we genotyped another 609 family trios, 2322 case subjects, and 789 control subjects and used genotyping data from two external control data sets. A joint analysis of data from 12,360 subjects was performed to estimate the overall significance and effect size of associations between alleles and the risk of multiple sclerosis. A transmission disequilibrium test of 334,923 single-nucleotide polymorphisms (SNPs) in 931 family trios revealed 49 SNPs having an association with multiple sclerosis (P<1x10(-4)); of these SNPs, 38 were selected for the second-stage analysis. A comparison between the 931 case subjects from the family trios and 2431 control subjects identified an additional nonoverlapping 32 SNPs (P<0.001). An additional 40 SNPs with less stringent P values (<0.01) were also selected, for a total of 110 SNPs for the second-stage analysis. Of these SNPs, two within the interleukin-2 receptor alpha gene (IL2RA) were strongly associated with multiple sclerosis (P=2.96x10(-8)), as were a nonsynonymous SNP in the interleukin-7 receptor alpha gene (IL7RA) (P=2.94x10(-7)) and multiple SNPs in the HLA-DRA locus (P=8.94x10(-81)). Alleles of IL2RA and IL7RA and those in the HLA locus are identified as heritable risk factors for multiple sclerosis. Copyright 2007 Massachusetts Medical Society.
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              Rare Variants Create Synthetic Genome-Wide Associations

              Introduction Efforts to fine map the causal variants responsible for genome-wide association studies (GWAS) signals have been largely predicated on the common disease common variant theory, postulating a common variant as the culprit for observed associations. This has led to extensive resequencing efforts that have been largely unsuccessful [1]–[5]. Here, we explore the possibility that part of the reason for this may be that the disease class causing an observed association may consist of multiple low-frequency variants across large regions of the genome—a phenomenon we call synthetic association. For convenience, these less common variants will be referred to here as “rare,” but we emphasize that we use this term loosely, only to refer to variants less common than those routinely studied in GWAS. The basic idea of how synthetic associations emerge in this model is illustrated in Figure 1, which shows how rare variants, by chance, can occur disproportionately in some parts of a gene genealogy. Any variant “higher up in the genealogy” that partitions those parts of the genealogy containing more disease variants than average will be identified as disease-associated. It is well appreciated that a noncausal variant will show association with a causal variant if the two are in strong linkage disequilibrium (LD). We use the previously introduced term synthetic association [6], however, to describe how such indirect association can occur between a common variant and at least one and possibly many rarer causal variants. Using the term synthetic as opposed to indirect emphasizes that the properties of the association signal are very different when the responsible variant or variants are much less frequent than the marker that carries the signal, as we detail below. 10.1371/journal.pbio.1000294.g001 Figure 1 Example genealogies showing causal variants and the strongest association for a common variant. (A) A genealogy with 10,000 original haplotypes was generated with 3,000 cases and 3,000 controls, genotype relative risk (γ) = 4, and nine causal variants. The branches containing the strongest synthetic association are indicated in blue. The branches containing the rare causal variants are in red. (B) A second genealogy was generated using the same parameters. These genealogies demonstrate two scenarios with genome-wide significant synthetic associations: the first (upper genealogy) had a high risk allele frequency (RAF = 0.49), and the second (lower genealogy) had a low RAF (0.08). To assess the tendency of rare disease-causing variants to create synthetic signals of association that are credited to single polymorphisms that are much more common in the population than the causal variants, we have simulated 10,000 haplotypes based on a coalescent model in a region either with or without recombination (Materials and Methods). We assumed that gene variants that influence disease have an allele frequency between 0.005 and 0.02, which is generally below the range of reliable detection (either by inclusion or indirect representation) using the genome-wide association platforms currently in use. We assumed a baseline probability of disease of φ for individuals with none of the rare genetic risk factors. The presence of at least one rare risk allele at the locus increased the probability of disease from φ to γ. We considered two values of φ (0.01, 0.1) and chose values of the penetrance γ such that the genotypic relative risk (GRR) of the rare causal variants varied incrementally between 2 and 6, where GRR is the ratio γ/φ. These values were chosen to explore the space around a GRR of 4, a threshold above which consistent linkage signals would be expected [7]. We simulated scenarios with one, three, five, seven, and nine rare causal variants. Results Across the conditions we have studied, not only is it possible to achieve genome-wide significance for common variants when one or more rare variants are the only contributors to disease, it is often the likely outcome (Figure 2). Overall, 30% of the simulations were able to detect an association with a common SNP at genome-wide significance (p 5%, Hardy-Weinberg equilibrium p-value >1×10−6, SNP call rate >95%), using the PLINK software [40]. For the sickle cell anemia GWAS, we compared 194 cases and 7,407 controls of inferred African ancestry via multidimensional scaling, with a genomic control inflation factor of 1.01. For hearing loss, we performed a GWAS on 418 cases and 6,892 control subjects, all of whom were of genetically inferred European ancestry via multidimensional scaling, with a genomic control inflation factor of 1.02.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                18 March 2016
                March 2016
                : 12
                : 3
                : e1005853
                Affiliations
                [1 ]Multiple Sclerosis Division, The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
                [2 ]Department of Anatomy and Neuroscience, University of Melbourne, Parkville, Victoria, Australia
                [3 ]Bioinformatics Core, The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
                [4 ]Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
                [5 ]Western Australian Neuroscience Research Institute, Nedlands, Western Australia, Australia
                [6 ]Comparative Genomics Centre, James Cook University, Townsville, Queensland, Australia
                [7 ]John Curtin School of Medical Research, Australian National University, Acton, Australian Capital Territory, Australia
                Georgia Institute of Technology, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MDB SF JF TJK AGB HB. Performed the experiments: MDB DM LJJ LG SEC RA GZMM AAP MMG LL MJFP MAJ JF. Analyzed the data: MDB ADF DM SEC GF TS MAJ JF. Contributed reagents/materials/analysis tools: MJFP HB JF MAJ AGB SF. Wrote the paper: MDB ADF TJK JF.

                [¤a]

                Current address: Cancer Immunology Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia

                [¤b]

                Current address: MRC Centre for Regenerative Medicine, The University of Edinburgh, Edinburgh, United Kingdom

                ¶ Membership of ANZgene is provided in the Acknowledgments.

                Article
                PGENETICS-D-15-02507
                10.1371/journal.pgen.1005853
                4798184
                26990204
                786ea2f0-4673-4859-8a69-461b9b912b5c
                © 2016 Binder et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 October 2015
                : 18 January 2016
                Page count
                Figures: 8, Tables: 7, Pages: 25
                Funding
                This work was supported by the National Multiple Sclerosis Society (US) Grant Number 6007629 to MDB, TJK, JF, HB and ADF; by the Multiple Sclerosis Research Australia Grant Number 13-013 to MDB, JF and TJK; by the National Health and Medical Research Council Grant Number APP1032486 to HB, JF and AGB, and by the Australian Research Council Linkage Grant Number LP110100473 to HB, JF abd AGB. The Florey Institute of Neuroscience and Mental Health acknowledges the strong support from the Victorian Government and in particular the funding from the Operational Infrastructure Support Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                The GEO accession number for the RNAseq data from which all samples can be accessed is GSE77598. The NCBI dbVar accession number for the structural variants identified is nstd124. All other relevant data are in the paper and its Supporting Information files.

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