De Mol et al.
recently published an analysis of the correlation between genetic risk for multiple
sclerosis (MS) and magnetic resonance imaging (MRI) metrics of white matter tract integrity in
healthy children. They used an MS polygenic risk score (PRS) derived from the International
Multiple Sclerosis Genetics Consortium (IMSGC) genome-wide association study (GWAS)
summary statistics. In a cohort of 1087 healthy, unrelated children of European
ancestry, the PRS was associated with a number of MRI ‘molehills’ – localised increases in
fractional anisotropy (FA) indicative of focal white matter tract alterations. This extends
the previously reported correlation between higher MS genetic risk and global FA in the same cohort.It is unclear whether these observations extend to healthy adults. Ikram et al.
reported nominal associations between a 110-variant MS PRS and various volumetric and
tractographic MRI outcomes in 4710 healthy adults; however, none of these associations
survived multiple testing. An analysis of an early release of UK Biobank (UKB) MRI data
(n = 8353) found no association between an MS PRS and white matter
hyperintensity volume, global FA, or mean diffusivity.We sought to replicate the results of De Mol et al. in the latest release of UKB data, which
contain MRI data for ~50,000 individuals. For MRI outcomes, we used imaging-derived phenotypes
(IDP) produced by a standardised pipeline on behalf of UKB.
People with cerebral small vessel disease, Alzheimer’s disease, and Parkinson’s disease
(defined by the ‘source of report’ fields in UKB, which combine self-report, Hospital Episode
Statistics, primary care codes, and other sources) were excluded to avoid confounding. A
variety of MS polygenic risk scores were generated using the clumping-and-thresholding
approach (full details and code available at https://github.com/benjacobs123456/PRS_UKB_MRI, methods similar to those
previously described
). Scores were calculated by both including the major histocompatibility (MHC) region
(using the HLA-DRB1*15:01 risk allele to capture the risk conferred by variation at this locus
) and excluding this region. In all, 130 distinct PRSs (65 non-MHC, 65 with MHC) were
generated by varying the clumping p-value threshold (using 13 thresholds from
5 × 10−8 to 1) and the clumping R2 threshold (using
five thresholds from 0.1 to 0.8). We divided the cohort into a training set comprising all
unrelated individuals of European ancestry with no imaging data available
(ncontrol = 346,547; nms = 1978) and
a test set with imaging data available (ncontrol = 30,040;
nms = 124). We identified the optimal PRS (explaining the
maximal liability to MS as measured by Nagelkerke’s pseudo-R2
metric) with and without the MHC region included using the training set. We then applied these
MHC-containing PRS and non-MHC PRS to the test set in order to investigate their association
with MRI findings.Within the testing set, both the MHC PRS and non-MHC PRS were strongly associated with MS
susceptibility (non-MHC: p = 5.70 × 10−6, odds ratio
(OR)top-vs-bottom-decile = 3.64, 95% confidence interval (CI) = 1.57–8.44; MHC:
p = 1.43 × 10−7, ORtop-vs-bottom-decile = 3.68, 95%
CI = 1.68–8.07; logistic regression models adjusted for age, sex, and genetic principal
components 1–4). These PRS explained 1.5% (MHC) and 1.3% (non-MHC) of MS susceptibility
(Nagelkerke’s pseudo-R2 on the observed scale). As would be
expected, individuals with MS had a higher total volume of T2 hyperintensities and
demonstrated nearly global reductions in regional FA compared to healthy controls.Neither the MHC-PRS (p = 0.34) nor the non-MHC-PRS
(p = 0.49) was associated with T2 hyperintensity volume in healthy controls
in linear regression models (n = 29,988, models adjusted as above plus total
intracranial volume). Similarly, there was no association exceeding the multiple testing
threshold (Bonferroni correction, alpha = 0.05, ntests = 48)
between either MHC-PRS or non-MHC PRS and regional FA in models adjusting for the same
confounding covariates. We observed similar results over a range of p-value
and clumping parameters.Our results support earlier findings
suggesting that, in healthy adults, MS polygenic risk does not correlate with white
matter hyperintensity volume or regional FA. Although the PRS explains a small proportion of
liability towards MS, the large sample size available here would enable us to detect a small
effect of the PRS on MRI phenotypes (power > 99% for an R2 of
0.1%). The large sample size of UKB and a rigorous approach to selecting an optimal PRS score
maximise the chance of observing such an effect. In summary, our results argue against the
concept that healthy adult individuals at high genetic risk of MS have subclinical MRI
evidence of the disease, in contrast to previous observations in children.
Authors: Mohammad Arfan Ikram; Meike W Vernooij; Gennady V Roshchupkin; Albert Hofman; Cornelia M van Duijn; André G Uitterlinden; Wiro J Niessen; Rogier Q Hintzen; Hieab Hh Adams Journal: Mult Scler Date: 2016-12-21 Impact factor: 6.312
Authors: C Louk de Mol; Philip R Jansen; Ryan L Muetzel; Maria J Knol; Hieab H Adams; Vincent W Jaddoe; Meike W Vernooij; Rogier Q Hintzen; Tonya J White; Rinze F Neuteboom Journal: Ann Neurol Date: 2020-03-27 Impact factor: 10.422
Authors: Loukas Moutsianas; Luke Jostins; Ashley H Beecham; Alexander T Dilthey; Dionysia K Xifara; Maria Ban; Tejas S Shah; Nikolaos A Patsopoulos; Lars Alfredsson; Carl A Anderson; Katherine E Attfield; Sergio E Baranzini; Jeffrey Barrett; Thomas M C Binder; David Booth; Dorothea Buck; Elisabeth G Celius; Chris Cotsapas; Sandra D'Alfonso; Calliope A Dendrou; Peter Donnelly; Bénédicte Dubois; Bertrand Fontaine; Lars Fugger; An Goris; Pierre-Antoine Gourraud; Christiane Graetz; Bernhard Hemmer; Jan Hillert; Ingrid Kockum; Stephen Leslie; Christina M Lill; Filippo Martinelli-Boneschi; Jorge R Oksenberg; Tomas Olsson; Annette Oturai; Janna Saarela; Helle Bach Søndergaard; Anne Spurkland; Bruce Taylor; Juliane Winkelmann; Frauke Zipp; Jonathan L Haines; Margaret A Pericak-Vance; Chris C A Spencer; Graeme Stewart; David A Hafler; Adrian J Ivinson; Hanne F Harbo; Stephen L Hauser; Philip L De Jager; Alastair Compston; Jacob L McCauley; Stephen Sawcer; Gil McVean Journal: Nat Genet Date: 2015-09-07 Impact factor: 38.330
Authors: Fidel Alfaro-Almagro; Mark Jenkinson; Neal K Bangerter; Jesper L R Andersson; Ludovica Griffanti; Gwenaëlle Douaud; Stamatios N Sotiropoulos; Saad Jbabdi; Moises Hernandez-Fernandez; Emmanuel Vallee; Diego Vidaurre; Matthew Webster; Paul McCarthy; Christopher Rorden; Alessandro Daducci; Daniel C Alexander; Hui Zhang; Iulius Dragonu; Paul M Matthews; Karla L Miller; Stephen M Smith Journal: Neuroimage Date: 2017-10-24 Impact factor: 6.556