The polygenic risk score (PRS) is derived by estimating the effect size of large numbers of genetic variants from a case–control discovery sample and combining these across the genotypes observed in other subjects (Visscher et al., 2017). Typically, many thousands of variants are used to contribute to the PRS with the expectation that only a proportion of them will in fact be truly associated with the trait in question. The first major application of this approach was to a genome-wide association study (GWAS) of schizophrenia and was used to justify the claim that thousands of alleles contribute to the risk of schizophrenia and that there is a shared genetic risk between schizophrenia and bipolar disorder (Purcell et al., 2009). Since this landmark publication, the observation that the PRS derived from one sample is associated with schizophrenia in another sample has been replicated numerous times and is probably one of the most robust observations in psychiatric genetics. The effect size is substantial and in a larger GWAS the odds ratio for schizophrenia risk between subjects with PRS in the highest or the lowest decile was around 10 (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). This study made summary statistics available for each marker and this would allow other researchers to use it as a discovery sample to calculate the PRS in other samples. However, it should be noted that to carry out a GWAS cases and controls need to be matched for ancestry because marker allele frequencies vary between populations. Thus, the summary statistics were produced from a meta-analysis of a number of matched case–control samples that were overwhelmingly of European ancestry. Of a total of 38 131 schizophrenia cases and 114 674 controls, all were of European ancestry except 1866 cases and 3418 controls who were of East Asian ancestry. Using the summary statistics and applying them to other traits allows one to test whether the variants associated with increased risk of schizophrenia are also associated with other disorders and a recent review identified 31 articles examining the association of the schizophrenia PRS with other psychiatric and nonpsychiatric phenotypes (Mistry et al., 2017).
Given the way that it is derived and how it is used, there is a natural tendency to assume that the PRS produced for a trait is a measure of polygenic susceptibility to that trait. However, a recent study of autism showed in a supplementary figure that the PRS for schizophrenia stratified by ancestry (Weiner et al., 2017) and in a separate study investigating whether the PRS predicted gene expression, it was noted that the PRS for schizophrenia was strongly correlated with the first principal component of marker genotypes (a proxy for ancestry) in both cases and controls of the CommonMind Consortium (CMC) dataset (Curtis, 2017). Other studies have also shown that the PRS for schizophrenia varies between populations (Martin et al., 2017). A recent study showed that the PRS for both type 2 diabetes and coronary heart disease varies between populations and pointed out that this would need to be taken into account if attempting to estimate an individual’s risk (Reisberg et al., 2017).
To investigate this further, the present study set out to examine the distribution of the PRS in cohorts of different ancestry genotyped for the HapMap project and to compare this with the distribution between schizophrenia cases and controls in the CMC dataset that had initially revealed the principal component correlation.
The merged post-QC phase I+II and III HapMap (International HapMap 3 Consortium, 2010) genotype files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010–08_phaseII+III/forward/. The subjects included in this study are from 11 different ancestral groups and are assumed not to be affected with schizophrenia. As the prevalence of schizophrenia is only 1%, it seems reasonable to assume, even in the absence of a formal psychiatric assessment, that at least the vast majority are unaffected. To obtain a PRS for schizophrenia, the file called scz2.prs.txt.gz, containing odds ratios (ORs) and P values for 102 636 linkage disequilibrium (LD)-independent single nucleotide polymorphism markers (SNPs), was downloaded from the Psychiatric Genetics Consortium (PGC) website (www.med.unc.edu/pgc/results-and-downloads). The ORs had been obtained by carrying out meta-analysis of 49 cohorts of European ancestry along with three cohorts of East Asian ancestry and using principal components to control for population stratification. This training set was produced as part of the previously reported PGC2 schizophrenia GWAS (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). SNPs from this dataset were then selected only if they had also been genotyped in all 11 of the HapMap cohorts, yielding a reduced set of 32 588 SNPs. HapMap subjects with genotyping call rate less than 0.90 were removed, leaving a sample of 1397.
The dataset used in the previous gene expression studies was downloaded from the CMC Knowledge Portal (https://www.synapse.org/#!Synapse:syn2759792/wiki/69613) consisting of SNP genotypes and RNAseq results from frontal cortex samples originating from tissue collections at Mount Sinai NIH Brain Bank and Tissue Repository, University of Pennsylvania Brain Bank of Psychiatric illnesses and Alzheimer’s Disease Core Center (Pennsylvania) and The University of Pittsburgh NIH NeuroBioBank Brain and Tissue Repository (Pittsburgh), collectively referred to as the CMC Mount Sinai NIH Brain Bank and Tissue Repository-Pitt-Penn dataset (Fromer et al., 2016). Genotypes and expression levels were available for 258 subjects with schizophrenia and 279 controls, though in the current analysis only the genotypes were used. The distributions of ethnicities were reported to be similar between subjects with schizophrenia and controls (Caucasian 80.7%, African–American 14.7%, Hispanic 7.7%, East Asian 0.6%). The methods for obtaining the genotypes and expression data have been described by the authors of the original study (Fromer et al., 2016). Genotyping was performed on the Illumina Infinium HumanOmniExpressExome 8v 1.1b chip (Catalogue no.: WG-351-2301; Illumina, San Diego, California, USA) using the manufacturer’s protocol. QC was performed using PLINK to remove markers with: zero alternate alleles, genotyping call rate less than 0.98, Hardy–Weinberg P value less than 5×10−5 and individuals with genotyping call rate less than 0.90. Marker alleles were phased to the forward strand, and ambiguously stranded markers were removed.
SNPs with P values less than 0.05 in the PGC2 training set were selected and their log(OR) summed over sample genotypes using the --score function of plink 1.09beta to produce a PRS for each patient in both the HapMap and CMC datasets (www.cog-genomics.org/plink/1.9/) (Purcell et al., 2007, 2009; Chang et al., 2015). The first 20 principal components for both datasets were produced using the --pca and --make-rel functions of plink.
As described previously (Curtis, 2017) ancestral outliers were removed from the CMC dataset by removing subjects with values for the first or second principal component exceeding −0.01. This left a fairly homogeneous sample of 264 subjects in which the PRS was not correlated at P less than 0.05 with 19 of the first 20 principal components, although it was correlated with the 11th at P of 0.0019.
Statistical tests and data manipulation were carried out using R, version 3.3.2 (R Core Team, 2014). The distributions of the PRS were compared between HapMap cohorts using analysis of variance and in addition a correlation analysis of PRS with the principal components was performed. For the CMC dataset, a t-test was used to compare the PRS between subjects with schizophrenia and controls. In both samples, the residuals of the PRS after correction for the first 20 principal components were also compared.
This study was approved by the UCL Research Ethics Committee.
The distribution of schizophrenia PRS between HapMap cohorts is shown in Table 1 and Fig. 1. The SD of the PRS is fairly similar in all cohorts, varying from 1.71 to 2.59. However, the means are very different. In CEU and TSI, the cohorts of European ancestry, the average PRS is −2.90 and −2.64, respectively. In ASW, LWK, MKK and YRI, the average PRS is 7.75, 9.48, 7.08 and 10.27. As can be seen from Fig. 1, the differences between the cohorts are so marked that the scores for the European and African cohorts scarcely overlap. The analysis of variance testing for a difference between cohort means was formally statistically significant at P less than 2×10−16. In the correlation analysis with the first 20 principal components, the PRS was highly significantly correlated with each of the first six principle components, at values of P less than 2×10−12 or lower. The residual PRS from this analysis did not significantly differ between cohorts.
In the CMC dataset, the PRS was significantly higher in subjects with schizophrenia (mean: −5.14) than controls (mean: −6.08) (difference=0.94, P=2.5×10−7). The residual PRS after correcting for the first 20 principal components in this dataset remained significantly higher in subjects with schizophrenia, P=7.4×10−7, though with a somewhat smaller difference between the means (0.41 vs. −0.47, difference=0.88).
There are striking differences in the schizophrenia PRS between cohorts with different ancestries. The differences between subjects of European and African ancestry are much larger, by a factor of around 10, than the differences between subjects with schizophrenia and controls of European ancestry.
The underlying mechanisms responsible for producing these results are not immediately obvious. Although differences in the frequencies of individual variants between groups of different ancestries would be not unexpected, it is harder to see why there should be a systematic effect such that alleles found more commonly in European schizophrenic subjects in the PGC should generally tend to be commoner in subjects with African ancestry. Two kinds of explanation for this effect suggest themselves.
The more benign explanation, from the point of view of the usefulness of the PRS, is that the PRS does indeed indicate genetic susceptibility to schizophrenia and that the contributing alleles are under weaker negative selection in African than non-African environments. This could arise if SNPs used to generate the PRS did not themselves confer risk but were in LD with variants which did, and if these LD relationships were stronger in Europeans than in Africans, which might be expected given the bottleneck produced by the out of Africa migration. Then negative selection could operate on the causative variants and also pull down the frequencies of marker SNPs in LD with them. This would result in SNPs associated with schizophrenia being at lower frequency in Europeans than Africans, leading to a higher PRS in Africans.
The less benign explanation would be to say that the PRS is basically an indicator of African ancestry and that for some reason, perhaps through mechanisms such as social adversity, subjects in the PGC with schizophrenia have a higher African ancestry component than controls. However, this does not seem a likely explanation because the PRS is associated with schizophrenia risk in a homogeneous sample after correction for principal components. Although there is some evidence that rates of schizophrenia are higher among subjects with African ancestry resident, for example, in the UK (Pinto et al., 2008), the prevalence appears to be fairly similar across the world and if anything lower in developing countries (Bhugra, 2005). It is certainly not plausible that the genetic risk associated in subjects with African ancestry could be as high as a naive interpretation of the PRS would imply.
A number of other issues might be relevant. There might be different patterns of LD between different populations that impacted on the way variants were tagged and the extent to which marker SNPs tended to co-occur with each other. The SNPs contributing to the PRS are pruned so as not to be in LD with each other; therefore, if there were different LD relationships in a target population then this might be problematic. The P value threshold chosen to select SNPs might be relevant in affecting how the PRS performs in different populations. More complex effects may be relevant and simulations in a coalescent-based framework have shown that genetic drift can produce unpredictable biases in any direction even in the absence of selection effects, so that in any particular scenario differences between populations may be essentially random (Martin et al., 2017). Indeed, that study reported that the PRS for schizophrenia was actually lower in African than in European and Asian populations, although with smaller differences than found in the present study. These discrepancies may be related to differences in the methods used to select SNPs and the use of a P value threshold of 0.01 rather than 0.05. They serve to illustrate that ancestry impacts on PRS in a complex way that will need to be systematically studied.
Whatever the explanation, these results have important implications for the interpretation of the PRS. As has been pointed out in the case of type 2 diabetes and coronary heart disease, the fact that the PRS varies according to ancestry would raise problems when one attempts to use it to estimate an individual’s risk in a clinical situation (Reisberg et al., 2017). A recent study using the PRS for schizophrenia to distinguish cases with first episode psychosis from controls showed that it performed better in subjects with European rather than African ancestry (Vassos et al., 2017). Any proposal to use the PRS to predict risk in individual subjects would need to explain very clearly how ancestry issues were to be dealt with. For certain applications, the PRS might be adjusted using principal components and other measures but given the very strong ancestry effects it would be reasonable to be concerned about whether such adjustments could ever be fully effective at the level of the individual. It may be worth noting that the PRS used for this study was obtained from analyses of mostly European samples in which principal components had already been incorporated, but nevertheless the resulting measure still correlated very strongly with the principal components of an ancestrally heterogeneous sample. It might be that the PRS is a measure of relative polygenic risk in sufficiently homogeneous populations. However, it would seem wise to exercise caution and to ensure that ancestry effects are appropriately dealt with whenever the PRS or similar methods are employed. If the PRS has some applicability, this would seem to be restricted to subjects having similar ancestry to that of the samples used to generate it. Given the results reported here, one could not attempt to apply the PRS derived from European subjects to individual subjects with African ancestry. This highlights difficult questions about the fact that people of non-European ancestry may be disadvantaged if they cannot benefit from research that has been carried out largely or exclusively in European subjects. It is uncertain how much credence should be given to previous studies that may have used the PRS without appropriately accounting for ancestry effects. Although the current investigation has only dealt with the PRS for schizophrenia, it seems that similar considerations apply to the PRS for type 2 diabetes and coronary heart disease and this is likely to be the case for many other phenotypes (Reisberg et al., 2017).
The author thanks Michael O’Donovan for helpful discussion and pointing out a mistake in an earlier version. He also thanks the CommonMind, HapMap and PGC Consortia for making these datasets available. The CommonMind Consortium data generation was supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimers Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: Pamela Sklar, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), Enrico Domenici, Laurent Essioux (F. Hoffman-La Roche Ltd), Lara Mangravite, Mette Peters (Sage Bionetworks), Thomas Lehner, Barbara Lipska (NIMH).
Conflicts of interest
There are no conflicts of interest.
Bhugra D (2005). The global prevalence of schizophrenia
. PLoS Med 2:e151.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015). ‘Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7.
Curtis D (2017). Polygenic risk score
is not strongly associated with the expression
of specific genes or gene sets. Psychiatr Genet 28:59–65.
Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al (2016). Gene expression
elucidates functional impact of polygenic risk for schizophrenia
. Nat Neurosci 19:1442–1453.
International HapMap 3 Consortium, Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs, R A, Peltonen L, et al, International HapMap 3 Consortium (2010). Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58.
Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al (2017). Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet 100:635–649.
Mistry S, Harrison JR, Smith DJ, Escott-Price V, Zammit S (2018). The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia
: Systematic review. Schizophr Res [Epub ahead of print].
Pinto R, Ashworth M, Jones R (2008). Schizophrenia
in black Caribbeans living in the UK: an exploration of underlying causes of the high incidence rate. Br J Gen Pract 58:429–434.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575.
Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, et al (2009). Common polygenic variation contributes to risk of schizophrenia
and bipolar disorder. Nature 460:748–752.
R Core Team (2014). R: a language and environment for statistical computing. Vienna, Austria, Austria: R Foundation for Statistical Computing.
Reisberg S, Iljasenko T, Läll K, Fischer K, Vilo J (2017). Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations. PLoS ONE 12:e0179238.
Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia
-associated genetic loci. Nature 511:421–427.
Vassos E, Di Forti M, Coleman J, Iyegbe C, Prata D, Euesden J, et al (2017). An examination of polygenic score risk prediction in individuals with first-episode psychosis. Biol Psychiatry 81:470–477.
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017). 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22.
Weiner DJ, Wigdor EM, Ripke S, Walters RK, Kosmicki JA, Grove J, et al (2017). Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 49:978–985.