Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13, a gene coding for a protein of an unknown function (p < 10–4). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts (p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC (p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene (p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases.

[1]  T. B. Hansen,et al.  RNA-Seq profiling of leukocytes reveals a sex-dependent global circular RNA upregulation in multiple sclerosis and 6 candidate biomarkers. , 2020, Human molecular genetics.

[2]  V. Martinelli,et al.  Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning , 2020, Cell reports. Medicine.

[3]  U. Muthane,et al.  Novel and reported variants in Parkinson's disease genes confer high disease burden among Indians. , 2020, Parkinsonism & related disorders.

[4]  Gerta Rücker,et al.  How to perform a meta-analysis with R: a practical tutorial , 2019, Evidence-Based Mental Health.

[5]  Simon C. Potter,et al.  Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility , 2019, Science.

[6]  H. Ullum,et al.  Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk , 2018, Cell.

[7]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[8]  Timothy A. Miller,et al.  Genome-wide Analyses Identify KIF5A as a Novel ALS Gene in and for Therapeutic , 2018 .

[9]  Hyojin Kim,et al.  COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH) , 2016, Nucleic Acids Res..

[10]  Allison Hainline,et al.  A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures , 2015, European Journal of Human Genetics.

[11]  F. Alkuraya,et al.  Exome-based case–control association study using extreme phenotype design reveals novel candidates with protective effect in diabetic retinopathy , 2015, Human Genetics.

[12]  Tom R. Gaunt,et al.  Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel , 2015, Nature Communications.

[13]  M. Daly,et al.  Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants , 2014, Nature.

[14]  C. Schlötterer,et al.  Sequencing pools of individuals — mining genome-wide polymorphism data without big funding , 2014, Nature Reviews Genetics.

[15]  G. Abecasis,et al.  Rare-variant association analysis: study designs and statistical tests. , 2014, American journal of human genetics.

[16]  G. Zamora-López,et al.  Transcriptomic Profile Reveals Gender-Specific Molecular Mechanisms Driving Multiple Sclerosis Progression , 2014, PloS one.

[17]  J. Nielsen,et al.  Analysis of the Human Tissue-specific Expression by Genome-wide Integration of Transcriptomics and Antibody-based Proteomics. , 2014, Molecular & cellular proteomics : MCP.

[18]  M. Pirinen,et al.  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis , 2013, Nature Genetics.

[19]  D. MacArthur,et al.  Negligible impact of rare autoimmune-locus coding-region variants on missing heritability , 2013, Nature.

[20]  Lei Sun,et al.  Robust and Powerful Tests for Rare Variants Using Fisher's Method to Combine Evidence of Association From Two or More Complementary Tests , 2013, Genetic epidemiology.

[21]  G. Abecasis,et al.  Exome sequencing and complex disease: practical aspects of rare variant association studies , 2012, Human molecular genetics.

[22]  Shane J. Neph,et al.  Systematic Localization of Common Disease-Associated Variation in Regulatory DNA , 2012, Science.

[23]  Iuliana Ionita-Laza,et al.  Scan-statistic approach identifies clusters of rare disease variants in LRP2, a gene linked and associated with autism spectrum disorders, in three datasets. , 2012, American journal of human genetics.

[24]  Adrian Liston,et al.  The immunogenetic architecture of autoimmune disease. , 2012, Cold Spring Harbor perspectives in biology.

[25]  Dan-Yu Lin,et al.  A general framework for detecting disease associations with rare variants in sequencing studies. , 2011, American journal of human genetics.

[26]  Kathryn Roeder,et al.  Testing for an Unusual Distribution of Rare Variants , 2011, PLoS genetics.

[27]  Vikas Bansal,et al.  A statistical method for the detection of variants from next-generation resequencing of DNA pools , 2010, Bioinform..

[28]  M. Brown,et al.  The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis. , 2010, Human molecular genetics.

[29]  Jing Cui,et al.  Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score , 2009, The Lancet Neurology.

[30]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[31]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[32]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[33]  S. Browning,et al.  A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic , 2009, PLoS genetics.

[34]  M. Spitz,et al.  Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. , 2008, American journal of human genetics.

[35]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[36]  S. Hauser,et al.  The Neurobiology of Multiple Sclerosis: Genes, Inflammation, and Neurodegeneration , 2006, Neuron.

[37]  Jonathan C. Cohen,et al.  Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. , 2006, The New England journal of medicine.