Polygenic risk score: use in migraine research

DOI: https://doi.org/10.1186/s10194- 018- 0856- 0

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Abstract-Summary Polygenic Risk Score has been widely used with success in genetics studies within neuropsychiatric disorders.

The use of polygenic scores is highly relevant as data from a large migraine Genome-Wide Association Study are now available, which will form an excellent basis for Polygenic Risk Score in migraine studies.

Polygenic Risk Score has been used in studies of neuropsychiatric disorders to assess prediction of disease status in case-control studies, shared genetic correlation between co-morbid diseases, and shared genetic correlation between a disease and specific endophenotypes.

Polygenic Risk Score provides an opportunity to investigate the shared genetic risk between known and previously unestablished co-morbidities in migraine research, and may lead to better and personalized treatment of migraine if used as a clinical assistant when identifying responders to specific drugs.

Polygenic Risk Score can be used to analyze the genetic relationship between

different headache types and migraine endophenotypes.

Polygenic Risk Score can be used to assess pharmacogenetic effects, and per- haps help to predict efficacy of the Calcitonin Gene-Related Peptide monoclonal antibodies that soon become available as migraine treatment.

Migraine genetics; Genome-Wide Association Studies; Polygenic Risk Score;

pleiotropy; endophenotype.

Review The hereditary component of migraine, i.e. the proportion of individual differences explained by genetic variation in migraine, is estimated to be between 38 and 53% and is likely to arise from the combined effect of many common risk variants each with small effect sizes, thus characterizing migraine as a common complex, poly- genic disease [2–4].

SNPs have been valuable as genomic “markers” in the search for causal variants that influence susceptibility to common diseases, or as causal variants with mar- ginal effect.

38 genetic loci with common SNPs associated with migraine have been discov- ered [5], where the individual SNP only explains a marginal proportion of the genetic variance.

Using a PRS calculated from sufficiently powered studies is a better way to esti- mate the genetic variance of the disease assessed than the individual genome-wide significant SNPs [6].

Methods The articles were categorized into two groups: Group one included articles where the main focus in the papers was PRS, and/or papers where the PRS methods were used, and thus relevant for the review; group two included articles that did not describe or use a polygenic risk scoring method, and thus not relevant for the review. Abstracts that were relevant to migraine research included genetic risk scoring of

complexly inherited neuropsychiatric traits.

2.1 Genetics

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The search yielded 146 articles; out of which 38 fulfilled the inclusion criteria

and were included in the review.

Understanding the Polygenic Risk Score PRS analysis allows for more genetic information to be assessed from genomic data than a simple threshold approach, such as the GWAS threshold, which convention- ally uses a p-value threshold of 5 × 10− 8 to avoid issues of false positive findings due to multiple testing.

To construct a PRS, an initial GWAS is done which is considered the discov-

ery sample.

In an independent sample with GWAS data, denoted the target sample, the PRS is calculated for each individual by adding up the risk alleles weighted by their odds ratios from the discovery sample.

This can be done using different significance thresholds (PT) of the data from the discovery sample, thereby testing whether including more information, i.e. SNPs, increases the power of prediction.

For successful construction of the PRS four prerequisites has been suggested: The target and discovery samples must be large (n > 2000 [6]); the discovery sample must be at least as large as the target sample; the phenotype investigated must be relatively homogeneous; and the level of genetic variation explained by common variants must be high [6].

Lessons from Genetic Studies of Neuropsychiatric Disorders There have been several studies on neuropsychiatric disorders using the PRS to assess: Prediction of disease status in case-control studies, shared genetic correla- tion between co-morbid diseases, and shared genetic correlation between a disease and specific endophenotypes.

The studies show consistent patterns across different phenotypes with significant disease prediction capacity but low ability to explain variance in genetic liability (between 0.2 and 5%).

The schizophrenia PRS predicted bipolar disorder status but had no correlation

with non-neuropsychiatric traits.

The variance of genetic liability to bipolar disorder explained by the polygenic score was small (R2 = 0.019), but still a significant portion of the total SNP heritabil- ity could be explained by the schizophrenia PRS [7].

This approach gained further support from studies conducted by the cross disor- der group of the Neuropsychiatric Genomics Consortium and it found overlapping genetic loci, i.e. pleiotropy, for childhood-onset diseases (ADHD and autism) and adolescent/adult on-set diseases (bipolar disorder, major depressive disorder, and schizophrenia) [8].

Factors Influencing Polygenic Risk Score Performance The PRS performance relies on the sample size; by increasing the discovery sample, the variance explained increases, which further increases the accuracy of the PRS for each individual.

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Patients of the same ethnicity as the target sample are often excluded from the

discovery sample to avoid an overestimation of the effects of the PRS.

Suggested Application of Polygenic Risk Score Analysis to Migraine Research As migraine and other headache disorders resemble neuropsychiatric disorders on the complexity, the polygenetic nature, and both being common brain disorders, we have introduced PRS analysis by summarizing experiences from studies of neuro- psychiatric disorders.

Ligthart and others [9] found genetic components shared between migraine and

major depressive disorder (MDD).

One can even imagine using PRS as a clinical assistant when choosing prophy- lactic drugs, e.g. patients who have a shared genetic component between migraine and depression may profit better from antidepressants than others. PRS analysis may identify more endophenotypes in migraine. It would be interesting to investigate whether migraine without aura and migraine

with aura are endophenotypes or genetically distinct disorders.

PRS analysis may help to assess whether patients with organic cerebral disorders

have a lower threshold than others for developing migraine attacks.

Conclusion PRS analyses have shown successful progress in the research of neuropsychiatric disorders and may inspire migraine research to understand more about the genetic underpinnings of migraine.

PRS may be useful in the investigation of shared genetic risk with comorbidities, in studying the relation between primary headache disorders and their sub-forms, and to personalize migraine treatment.

Acknowledgement A machine generated summary based on the work of Chalmer, Mona Ameri; Esserlind, Ann-Louise; Olesen, Jes; Hansen, Thomas Folkmann. 2018  in The Journal of Headache and Pain.

Genetics of migraine aura: an update