Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci

Abstract

Background:

A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability.

Objectives:

We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence.

Methods:

We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy. We tested each SNP for both association and linkage with MS, the linkage being explored in terms of identity-by-descent (IBD) sharing excess and using gene dropping to compute a corresponding empirical p-value. By targeting regions that are both associated and in linkage with MS, we increase chances of identifying interesting genomic regions.

Results:

We identified 486 MS-associated (p < 1 × 10^–4) and 18,426 MS-linked (p < 0.05) SNPs. A total of 111 loci were both linked and associated with MS, 18 of them pointing to 14 non-major histocompatibility complex (MHC) genes, and 93 of them located in the MHC region.

Conclusion:

We discovered new suggestive signals and confirmed some previously identified ones. We believe this to represent a significant step toward an understanding of the genetic basis of MS.

Keywords

Multiple sclerosis identity-by-descent sharing association pedigree founder population

Get full access to this article

View all access options for this article.

References

Gourraud

Harbo

Hauser

et al . The genetics of multiple sclerosis: an up-to-date review. Immunol Rev 2012; 248: 87–103.

Compston

McDonald

Noseworthy

et al . McAlpine’s multiple sclerosis. 4th ed. Amsterdam: Elsevier, 2005.

Mackenzie

Morant

Bloomfield

et al . Incidence and prevalence of multiple sclerosis in the UK 1990-2010: A descriptive study in the General Practice Research Database. J Neurol Neurosurg Psychiatry 2014; 85: 76–84.

Montomoli

Prokopenko

Caria

et al . Multiple sclerosis recurrence risk for siblings in an isolated population of Central Sardinia, Italy. Genet Epidemiol 2002; 22: 265–271.

Hemminki

Sundquist

et al . Risk for multiple sclerosis in relatives and spouses of patients diagnosed with autoimmune and related conditions. Neurogenetics 2009; 10: 5–11.

The International Multiple Sclerosis Genetics Consortium. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007; 357: 851–862.

The International Multiple Sclerosis Genetics Consortium and The Wellcome Trust Case Control Consortium 2. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011; 476: 214–219.

De Jager

Jia

Wang

et al . Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 2009; 41: 776–782.

Patsopoulos

Esposito

Reischl

et al . Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 2011; 70: 897–912.

10.

Consortium

IMSG

Beecham

Patsopoulos

et al . Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 2013; 45: 1353–1360.

11.

Bush

Sawcer

De Jager

et al . Evidence for polygenic susceptibility to multiple sclerosis—The shape of things to come. Am J Hum Genet 2010; 86: 621–625.

12.

Poser

Paty

Scheinberg

et al . New diagnostic criteria for multiple sclerosis: Guidelines for research protocols. Ann Neurol 1983; 13: 227–231.

13.

Shah

Liu

Floyd

et al . optiCall: A robust genotype-calling algorithm for rare, low-frequency and common variants. Bioinformatics 2012; 28: 1598–1603.

14.

Purcell

Neale

Todd-Brown

et al . PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

15.

Chen

Yang

GWAF: An R package for genome-wide association analyses with family data. Bioinformatics 2010; 26: 580–581.

16.

Han

Abney

Using identity by descent estimation with dense genotype data to detect positive selection. Eur J Hum Genet 2013; 21: 205–211.

17.

Han

Abney

Identity by descent estimation with dense genome-wide genotype data. Genet Epidemiol 2011; 35: 557–567.

18.

MacCluer

Vandeberg

Read

et al . Pedigree analysis by computer simulation. Zoo Biol 1986; 5: 147–160.

19.

Knijnenburg

Wessels

LFA

Reinders

MJT

et al . Fewer permutations, more accurate P-values. Bioinformatics. Epub ahead of print 27 May 2009. DOI: 10.1093/bioinformatics/btp211.

20.

Wang

Tucker

Rizki

et al . Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. Elife. Epub ahead of print 10 May 2016. DOI: 10.7554/eLife.10557.

21.

Yang

Manolio

Pasquale

et al . Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 2011; 43: 519–525.

22.

Kristiansson

Naukkarinen

Peltonen

Isolated populations and complex disease gene identification. Genome Biol 2008; 9: 109.

23.

Varilo

Peltonen

Isolates and their potential use in complex gene mapping efforts. Curr Opin Genet Dev 2004; 14: 316–323.

24.

Arcos-Burgos

Muenke

Genetics of population isolates. Clin Genet 2002; 61: 233–247.

25.

Browning

Thompson

EA.

Detecting rare variant associations by identity-by-descent mapping in case-control studies. Genetics 2012; 190: 1521–1531.

26.

Shao

Niu

Zhang

et al . Opposite associations between individual KIAA0319 polymorphisms and developmental dyslexia risk across populations: A stratified meta-analysis by the study population. Sci Rep 2016; 6: 30454.

27.

Ramagopalan

Knight

Ebers

GC.

Multiple sclerosis and the major histocompatibility complex. Curr Opin Neurol 2009; 22: 219–225.

28.

Marrosu

Murru

et al . Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia. Hum Mol Genet 2001; 10: 2907–2916.

29.

Moutsianas

Jostins

Beecham

et al . Class II HLA interactions modulate genetic risk for multiple sclerosis. Nat Genet 2015; 47: 1107–1113.

30.

Cocco

Murru

Costa

et al . Interaction between HLA-DRB1-DQB1 haplotypes in Sardinian multiple sclerosis population. PLoS One 2013; 8: e59790.

31.

Barizzone

Zara

Sorosina

et al . The burden of multiple sclerosis variants in continental Italians and Sardinians. Mult Scler 2015; 21: 1385–1395.

32.

Pastorino

Menni

Barca

et al . Association between protective and deleterious HLA alleles with multiple sclerosis in Central East Sardinia. PLoS One 2009; 4: e6526.

33.

Hoppenbrouwers

Aulchenko

Ebers

et al . EVI5 is a risk gene for multiple sclerosis. Genes Immun 2008; 9: 334–337.

34.

D’Netto

Ward

Morrison

et al . Risk alleles for multiple sclerosis in multiplex families. Neurology 2009; 72: 1984–1988.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.08 MB

2.36 MB

1.01 MB

0.45 MB

0.48 MB