REVIEW ARTICLE Neuropsychiatric Genetics

HLA Associations in Schizophrenia: Are We Re-Discovering the Wheel? Chowdari V. Kodavali,1 Annie M. Watson,1 Konasale M. Prasad,1 Cemil Celik,2 Hader Mansour,1 Robert H. Yolken,3 and Vishwajit L. Nimgaonkar4* 1

Department of Psychiatry, University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, Pittsburgh, Pennsylvania

2

Department of Psychiatry, Gulhane School of Medicine, Etlik-Ankara, Turkey Department of Pediatrics, Stanley Division of Developmental Neurovirology, Johns Hopkins University School of Medicine, Baltimore, Maryland 3

4

Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania

Manuscript Received: 15 February 2013; Manuscript Accepted: 2 August 2013

Associations between human leukocyte antigen (HLA) polymorphisms on chromosome 6p and schizophrenia (SZ) risk have been evaluated for over five decades. Numerous case–control studies from the candidate gene era analyzed moderately sized samples and reported nominally significant associations with several loci in the HLA region (sample sizes, n ¼ 100–400). The risk conferred by individual alleles was modest (odds ratios < 2.0). The basis for the associations could not be determined, though connections with known immune and auto-immune abnormalities in SZ were postulated. Interest in the HLA associations has re-emerged following several recent genome-wide association studies (GWAS); which utilized 10- to 100-fold larger samples and also identified associations on the short arm of chromosome 6. Unlike the earlier candidate gene studies, the associations are statistically significant following correction for multiple comparisons. Like the earlier studies; they have modest effect sizes, raising questions about their utility in risk prediction or pathogenesis research. In this review, we summarize the GWAS and reflect on possible bases for the associations. Suggestions for future research are discussed. We favor, in particular; efforts to evaluate local population sub-structure as well as further evaluation of immune-related variables in future studies. Ó 2013 Wiley Periodicals, Inc.

Key words: human leukocyte antigen (HLA); schizophrenia; genetics; GWAS; immune-related

How to Cite this Article: Chowdari KV, Watson AM, Prasad KM, Celik C, Mansour H, Yolken RH, Nimgaonkar VL. 2014. HLA associations in schizophrenia: Are we re-discovering the wheel? Am J Med Genet Part A 165B:19–27.

2012]. Three questions were raised: (i) which allele/s are the key risk variants; (ii) could confounding factor/s explain the associations; (iii) what is the function of the associated SNPs; in other words can they explain SZ pathogenesis [Lehner, 2012]? In the following sections, we review the published associations, reflect on Lehner’s admonitions and propose avenues for further research.

REVIEW OF SZ ASSOCIATIONS IN THE HLA REGION The HLA or the Major Histocompatibility Complex (MHC) super-locus is a gene-dense genomic region spanning 3.6 Mb on chromosome 6p [Shiina et al., 2009]. Polymorphisms in this region have been associated with more than 100 different diseases, including common diseases such as diabetes mellitus, rheumatoid arthritis, systemic lupus erythematosus, psoriasis and asthma (http://gwas.nih.gov/).

INTRODUCTION Associations between Human Leukocyte Antigen (HLA) polymorphisms and risk for SZ have been reported over the past five decades [Wright et al., 2001; Lehner, 2012]. They have attracted renewed interest because of recent genome-wide association studies that repeatedly indicated significant associations in the region encoding the HLA genes on the short arm of chromosome 6 (Table I). Despite the encouraging genome-wide significant results, a recent commentary espoused Kirkegaard’s dictum, “de omnibus dubitandum” (be suspicious of everything, doubt everything) [Lehner,

Ó 2013 Wiley Periodicals, Inc.

Grant sponsor: Stanley Medical Research Institute; Grant number: 07R1712; Grant sponsor: National Institutes of Health; Grant numbers: MH63480, MH085269, MH083756, D43, TW008302.  Correspondence to: Nimgaonkar L. Vishwajit, 3811 O’Hara Street, WPIC Room 441, Pittsburgh, PA 15213. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 19 October 2013 DOI 10.1002/ajmg.b.32195

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AMERICAN JOURNAL OF MEDICAL GENETICS PART B

TABLE I. SNPs in the Extended HLA Region Significantly Associated With Schizophrenia in GWAS SNP number 1

SNPs rs13194053

Chromosomal position 27143883

2

rs6932590

27248931

3 4

rs1635 rs2523722

28227604 30165273

Nearest gene Transfer RNA Ile (HIST1H2AH) Transfer RNA Val (PRSS16) NKAPL TRIM26

5 6 7 8

rs2021722 rs886424 rs3131296 rs9272219

30174131 30782002 32172993 32602269

TRIM26 AK098012 Notch4 DQA1

Risk allele T

Frequency range 0.82–1.00

P-Value 9.54  109

Odds ratio (95% CI) NR

T

0.63–0.99

1.4  1012

1.16 (1.11–1.21)

C G

0.52–0.95 0.54–0.94

7  1012 2.88  1016

0.78 (0.73–0.82) NR

C Ca G G

0.48–0.93 0.87–1.00 0.88–0.97 0.53–0.87

4.3  1011 4.54  108 2.3  1010 6.88  108

1.18 (1.13–1.23) 0.68 (NR) 1.19 (1.13–1.25) NR

Refs. Shi et al. [2009], Purcell et al. [2009] Stefansson et al. [2009] Yue et al. [2011] ISGC and the WTCCC 2 [2012] Ripke et al. [2011] Bergen et al. [2012] Stefansson et al. [2009] Shi et al. [2009]

Only the most significant GWAS SNPs that are in or near the HLA/MHC region are included, as of publications till December 2012. Chromosomal position and nearest gene are based on the UCSC Genome Browser on Human Feb. 2009 (GRCh37/hg19) Assembly. NR, not reported; CI, confidence interval Frequency ranges for the associated alleles are reported from the populations listed in the HapMap database (www.hapmap.org; populations: ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI and YRI). a Nucleotide is not available from the reference report.

Early HLA Candidate Gene Studies A review of HLA association studies in schizophrenia published prior to 2000 indicated several suggestive associations; indeed, two or more publications reported associations with the following HLA antigens (serotypes) or DNA polymorphisms: HLA A9 serotype or its A24 sub-specificity, HLA A28, HLA A10, HLA DRw6 and HLA DRB1 01 [see review by Wright et al., 2001]. Consistent associations with alleles of the HLA DQB1 gene were also reported later [Nimgaonkar et al., 1992, 1995; Chowdari et al., 2001]. The effect sizes were generally modest, with estimated odds ratios (ORs) less than 2.0. Though Type I errors due to inadequately powered samples could arguably explain non-significant associations in a large number of other studies, the lack of consistent associations did not bode well for such analyses. Moreover, it is difficult to synthesize the studies through meta-analysis, because of wide variation in ascertainment schemes, diagnostic criteria and control selection [Wright et al., 2001].

HLA Associations From GWAS A multi-site case–control GWAS of Caucasian ancestry participants indicated that rs13194053, a single nucleotide polymorphism (SNP) localized to the TRNA_Ile (transfer RNA Isoleucine) gene on chromosome 6p21 was significantly associated with schizophrenia (N ¼ 8,008 cases, N ¼ 19,077 controls; see Table I and Fig. 1) [Purcell et al., 2009]. A significant association with rs9272219 in HLA-DQA1 was also observed [Shi et al., 2009]. Remarkably, there were similar trends for associations in the same direction in the samples from individual sites that contributed to the GWAS. A third GWAS involving European/US participants reported a significant association with rs6932590 TRNA_val gene (transfer RNA Valine). Other genome-wide significant associations using conditional analysis included rs3131296 in the NOTCH4 gene (all associations: P < 1.1  109; N ¼ 12,945 SZ cases, N ¼ 34,591 controls) [Stefansson et al., 2009]. In a smaller independent Swedish case–control sample (N ¼ 1,507 cases, N ¼ 2,093 controls), Bergen et al. [2012]

did not find genome-wide significant associations. Re-analysis of these data in conjunction with previously reported samples (N ¼ 2,111 cases, N ¼ 2,535 controls), however, revealed a genome-wide significant association with another SNP in the HLA region (rs886424, P ¼ 4.54  108). The MHC-specific imputation in the enlarged sample revealed an additional SNP with genomewide significance (rs1264353, P ¼ 3.89  108) [Bergen et al., 2012]. Separately, a sample from Ireland that was originally analyzed as part of the International Schizophrenia Genomics Consortium and the Wellcome Trust Case Control Consortium 2 [Irish Schizophrenia Genomics Consortium and the Wellcome Trust Case Control Consortium 2, 2012] study (N ¼ 1,606 cases, N ¼ 1,794 controls) was re-analyzed; it indicated significant associations at two SNPs in the HLA region (rs204999, P-combined ¼ 1.34  109 and rs2523722, combined P ¼ 2.88  1016) that were replicable in an independent sample of 13,195 cases and 31,021 controls. In this study, conventional HLA alleles were also imputed and the strongest association was detected with HLA-C 01:02. The authors also replicated “protective” effects of HLA-B 08:01 and DRB1 03:01 [ISGC and the WTCCC 22012]. A SZ GWAS of Han Chinese samples (N ¼ 4,773 cases, N ¼ 7,202 controls) also reported two susceptibility loci (rs1233710, P ¼ 4.76  1011; rs1635 (NKAPL), P ¼ 6.91  1012) at 6p21–22 [Yue et al., 2011]. The initial GWAS have been updated through “mega-analyses” that include the prior reports as well as additional samples, though the overlaps with previous reports requires scrutiny before independent replications can be declared. Ripke et al. [2011] reported on a discovery sample of 21,856 participants and a replication sample of 29,839; multiple statistically significant associations in the HLA region were noted, consistent with the earlier GWAS. The most significant association was detected with rs2021722 (stage 1, P ¼ 4.3  1011; OR ¼ 1.18, 95% CI: 1.13–1.23). A number of SNPs within the HLA region were also significant at genome-wide levels of significance, but some of the SNPs were in substantial linkage disequilibrium (LD). Following conditional analyses using rs2021722 as the primary predictor, only the association with

KODAVALI ET AL.

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FIG. 1. Simplified map of the HLA region to illustrate SZ associated SNPs. The genomic distrances in kilobases are based on the UCSC Genome Browser February 2009 (GRCh37/hg19). Assembly. The coordinate positions are not scaled for clarity. Selected genes that are reported in schizophrenia candidate gene studies or GWAS are shown. GWAS associated SNPs that are listed in the Table I are displayed in relation to HLA genes.

rs9272105 was significant, albeit not at genome-wide significant levels (P ¼ 1.8  106, inter-SNP distance ¼ 2.4 Mb, r2 ¼ 0.01 based on HapMap data) [Ripke et al., 2011]. An ongoing mega analysis conducted by the psychiatric genomics consortium (PGC) comprises 26,000 cases and 28,000 control individuals. Through preliminary analyses, genome-wide significant associations were detected at over 60 loci; the highest level of statistical significance was noted in the HLA region (Dr. S. Ripke, personal communication). In summary, the mega analyses based on GWAS studies indicate highly significant associations in the HLA region. There is some variation between independent samples constituting the mega analyses; the differences may be due to variations in sample size and stochastic variation, although multiple independent risk SNPs may be present. The associations have relatively small effect sizes (ORs ¼ 1.1–1.2). There is no clear biological function for any of the associated SNPs.

WHICH ALLELE/S ARE THE PRIMARY RISK VARIANTS? The initial GWAs studies, as well as the subsequent mega analyses have suggested multiple SNPs conferring risk for SZ (see Table I for a list of SNPs that attained statistical significance following corrections for genome-wide analyses) [Shi et al., 2009; Stefansson et al., 2009; Ripke et al., 2011]. Some of these associations may be explained by the substantial LD in the HLA region. It is difficult to detect which SNPs affect risk due to the substantial LD in the HLA region, compounded by the modest risk due to individual SNPs [Undlien et al., 2001].

COULD CONFOUNDING FACTOR/S EXPLAIN THE ASSOCIATIONS? Despite their correlational nature, genetic associations in case– control samples can convincingly indicate causality provided “spu-

rious” associations due to confounding factors can be discounted. Many of the GWAS have pooled samples collected at different sites, with variation across sites in sample sizes. The variable “site” was routinely included as a covariate in the GWAS analyses. While such an inclusion may be satisfactory with regard to inter-site variation (such as sample size differences), it assumes homogeneity within each site—an assumption that may not be valid. For example, population sub-structure due to historical admixture may differ between cases and controls within site/s. Principal components analyses (PCA) based on genome-wide SNP lists has been utilized to account for sub-structure [Price et al., 2006], but genome wide PCA-based correction may not satisfactorily correct for local genomic sub-structure due to factors such natural selection, migration, or random genetic drift [Qin et al., 2010]. Indeed, geographical variations in HLA allele frequencies are well documented across European nations [Heath et al., 2008; McEvoy et al., 2009]. Heterogeneity has been noted even in relatively small geographical locations such as Switzerland [Buhler et al., 2012]. Thus, it is challenging to account for population sub-structure in the HLA region and tailored analysis may be required. In summary, most obvious confounding factors have been accounted for in the GWAS. Also, subtle variation in population sub-structure in the chr 6p21–p22 region is an important variable that needs to be considered as a possible confounding factor.

WHAT IS THE FUNCTION OF THE ASSOCIATED SNPS? None of the significantly associated SNPs from the GWAS analyses cause obvious functional changes, so it is difficult to postulate a plausible model for SZ pathogenesis based on the current associations. Yet, other SZ research indicates three pathways to pathogenesis in which the associated SNPs could plausibly have functional roles: (i) inflammatory/infectious pathways, (ii) autoimmune abnormalities, (iii) non-immune related functions.

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Role for SZ Associated SNPs in Inflammatory/ Infectious Pathways Reciprocal regulation of the immune and the central nervous systems is well documented [Aloisi et al., 2000a,b; Tracey, 2009]. For example, neurotransmitters secreted from nerve terminals modulate immune cell activity [Levite, 2008]. Conversely, functional receptors for several immune mediators (e.g., chemokines) are located on neurons [Besedovsky et al., 1983; Bajetto et al., 2001; Adler and Rogers, 2005]. Cytokine and chemokine receptors are also expressed on neurons and glia [Coughlan et al., 2000; Gardoni et al., 2011], and can thus regulate neurodevelopment [Bajetto et al., 2001; Smith et al., 2007], apoptosis [Bajetto et al., 2002], signal transduction [Adler and Rogers, 2005], neuroplasticity [Ben Menachem-Zidon et al., 2008; Goshen et al., 2008; Koo and Duman, 2008], and neurotransmission [Kitagami et al., 2003; Moro´n et al., 2003a,b; Volterra and Meldolesi, 2005]. Elevated immune markers (e.g., IL-6, CRP) are associated independently with cognitive deficits [Marsland et al., 2006; Dickerson et al., 2007], and hippocampal volume reduction [Marsland et al., 2008], and through interaction with exposure to certain infectious agents [Dickerson et al., 2012; Prasad et al., 2012]. Thus, increased pro-inflammatory tone could affect diverse neurobiological processes that have been implicated in the pathophysiology of schizophrenia and related psychotic disorders. Mounting evidence suggests altered immune functions as well as “neuro-inflammation” in SZ [Heath and Krupp, 1967; Heath et al., 1967a,b; Bayer et al., 1999; Radewicz et al., 2000; Rothermundt et al., 2001; Brown et al., 2004; Adler and Rogers, 2005; Saetre et al., 2007; Smith et al., 2007; Meyer and Feldon, 2009; Bechter et al., 2010; Ellman et al., 2010; Mu¨ller and Schwarz, 2010; Meyer et al., 2011]. Elevated levels of inflammatory cytokines in the peripheral blood have been noted in SZ patients compared with healthy controls [Potvin et al., 2008]. Some post-mortem brain studies indicate neuro-inflammation in the form of activated microglia/macrophages [Bayer et al., 1999; Radewicz et al., 2000; Wierzba-Bobrowicz et al., 2005], increased expression of inflammatory markers in the dorsolateral prefrontal cortex neurons [Fillman et al., 2013] as well as altered cerebral microvasculature [Kim et al., 2008a]. Whether the absence of obvious gliosis argues against the occurrence of neuroinflammation in SZ has been debated [Roberts et al., 1986; Stevens et al., 1988; Casanova et al., 1990; Arnold et al., 1996; Harrison, 1999], but non-invasive whole brain scanning techniques also document evidence of neuroinflammation [van Berckel et al., 2008; Doorduin et al., 2009]. Indirect evidence supporting the concept of neuroinflammation comes from a drug trial in which non-specific anti-inflammatory drugs reduced psychotic symptoms severity [Mu¨ller et al., 2002; Muller et al., 2010; Sommer et al., 2012]. Together, these studies suggest a role for neuroinflammation in SZ pathogenesis and a framework for additional tests of the hypothesis [Mu¨ller and Schwarz, 2010]. How might these findings bear on the HLA associations with SZ? In view of the critical role for HLA molecules in antigen presentation during the immune response process, it is not surprising that numerous infections are associated with HLA variation [Hill, 2006]. Analyses of HLA variation, particularly the extensive genetic diversity has been explained by pathogen-driven-balancing selec-

AMERICAN JOURNAL OF MEDICAL GENETICS PART B tion, which can predict HLA genetic differentiation worldwide [Sanchez-Mazas et al., 2012]. Functional variations in HLA molecules can alter immune functions that may modify the susceptibility to exposure to certain infectious agents; they may also regulate the balance between pro-inflammatory and anti-inflammatory factors. Such an altered balance could in turn affect other biological processes that are not classically immunological in nature. We are not aware of any studies that investigated links between these processes and variation due to the GWAS associated SNPs in the HLA region. Admittedly, it is difficult to establish such links in view of the complex, inter-linked nature of inflammatory processes. Indeed, the reported alterations in biological processes may be compensatory mechanism for a primary immunological change. In summary, convergent lines of evidence suggest chronic low grade neuro-inflammation in SZ, as well as an array of immunological abnormalities. There are no obvious associations between the SZ associated HLA SNPs and any of these variables.

Role for SZ Associated SNPs in Auto-Immune Abnormalities National hospital registry based studies from Denmark indicate that individuals with auto-immune diseases such as type 1 diabetes mellitus, psoriasis, Sjogren’s syndrome, autoimmune hepatitis and dermatopolymyositis have an increased risk for SZ, with an estimated 29% increased risk across all autoimmune diseases [Eaton et al., 2010; Benros et al., 2011]. The elevated SZ risk is observed even if the diagnoses of autoimmune dysfunction predate the SZ diagnosis and is also observed among individuals with a family history of autoimmune diseases [Benros et al., 2011, 2012]. Conversely, a national case-registry based study of SZ patients from Taiwan indicated increased prevalence of auto-immune diseases including Graves’ disease, psoriasis, celiac disease, pernicious anemia, and hypersensitivity vasculitis [Chen et al., 2012]. An increased prevalence of auto-immune disorders has also been observed among non-psychotic relatives of patients with SZ [Eaton et al., 2010; Benros et al., 2012]. On the other hand, patients with SZ have reduced prevalence of rheumatoid arthritis, another auto-immune disease [Chen et al., 2012]. Others have also reported a reduced prevalence of type 1 diabetes mellitus among patients with SZ in a Finnish national registry [Juvonen et al., 2007] in contrast to the Danish studies [Benros et al., 2011]. Several mechanisms may explain the epidemiologic data [Strous and Shoenfeld, 2006]. Individuals with auto-immune diseases have elevated prevalence of antibodies directed against brain proteins or may produce antibodies that cross-react with brain proteins [Irani and Lang, 2008]; individuals with SZ can also produce antibodies against proteins in the frontal cortex [Henneberg et al., 1994], cingulate gyrus [Ganguli et al., 1987; Kelly et al., 1987; Henneberg et al., 1994], hippocampus [Ganguli et al., 1987] and against glutamate receptors [Tsutsui et al., 2012]. Individuals with autoimmune disorders also develop further CNS dysfunction in conjunction with microbial infections [Benros et al., 2011]. The inflammatory reactions to these insults could conceivably compromise the blood–brain barrier, permitting the transport of noxious agents [Eaton et al., 2006; Dalman et al., 2008; Dantzer et al., 2008]; similar mechanisms are plausible in SZ. It has even been proposed

KODAVALI ET AL. that maternal infection provokes immunological reactions and producing autoantibodies that disrupt neural development, thus elevating the risk for SZ [Kirch, 1993]. Finally, there may be shared genetic or environmental risk factors (e.g., stress) for specific autoimmune disease and SZ. HLA variants could serve as shared genetic risk factors because there are well known associations for several autoimmune diseases [Todd et al., 2007; van Heel et al., 2007; Genetic Analysis of Psoriasis Consortium et al., 2010; Zhernakova et al., 2011]. Particular HLA molecular configurations have been identified as prominent risk factors for individual auto-immune diseases, such as ulcerative colitis and type I diabetes mellitus [Trucco, 1992; Achkar et al., 2012]. To see if HLA variants from GWAS of auto-immune disease also alter risk for SZ, we conducted a simplified survey of published data (http://gwas.nih.gov/). Of the most significantly associated HLA region SNPs for auto-immune disease, none overlap with the significantly associated SNP list in the current SZ mega analysis [Ripke et al., 2011]. However, rs3131296 an HLA region SNP reported in an earlier SZ GWAS [Stefansson et al., 2009] is in significant LD (r2 > 0.73) with SNPs that are significantly associated with celiac disease [van Heel et al., 2007], type-1 diabetes mellitus [Todd et al., 2007] and systemic lupus erythematosus [Harley et al., 2008]. More sophisticated and precise analysis based on HLA variants may help understand whether shared HLA risk variants explain the increased (or decreased) prevalence of individual autoimmune diseases among persons with SZ. In summary, the co-occurrence of auto-immune disease among persons with SZ and their relatives could be explained by several mechanisms. Shared etiology is an appealing possibility that could be explored precisely through HLA associations.

Role for SZ Associated SNPs in Non-Immune Related Functions A substantial proportion of genes on chromosome 6p do not have immune-related functions, so it is plausible that the GWAS associations in this region also indicate abnormalities unrelated to immune dysfunction. Candidate gene studies have indicated nominal associations with genes with no obvious role in immune function, for example, NOTCH4 [Wei and Hemmings, 2000], HSPA1B [Pae et al., 2005], and HSPA1L [Kim et al., 2008b]. Indeed, a GWAS from Iceland indicates genome-wide significant results at this locus [Stefansson et al., 2009]. However, NOTCH4 SNPs are not significantly associated with SZ following corrections for multiple comparisons in the GWAS mega analyses. In summary, the SZ associations on chromosome 6p could be plausibly related to non-immune related functions, as illustrated by the reported associations at NOTCH4 [Shayevitz et al., 2012].

SUGGESTIONS FOR FUTURE RESEARCH In planning further research, it is helpful to retrace Lehner’s admonitions. Consistent with wariness about artifacts in all case–control studies, we advocate caution while interpreting the HLA associations. The greatest concern in our view relates to population structure in the HLA region as a potential confounding factor. Employing local principal components (PC) to represent

23 local ancestries and adjusting for local PCs when testing associations could address these questions [Qin et al., 2010]. Assuming that HLA associations are detectable even after such corrections, we believe that future research should proceed along two lines of enquiry that should proceed simultaneously, each informing the other: (i) refine the search for potential causal variant/s; (ii) understand the functional correlates of the associated variants.

Refining Risk Variants Fine mapping typically involves deep sequencing of the region of interest, followed by comprehensive genotyping in all available samples. The HLA region is difficult to sequence due to numerous duplicated segments and other complexities; moreover, the high levels of LD in some sub-regions may hamper the identification of the primary risk variants. Therefore, we advocate a complementary trans-ethnic mapping approach [Zaitlen et al., 2010; Morris, 2011]. This approach relies on variations in recombination patterns in different ethnic groups to help identify “true” risk alleles; it takes account of the expected similarity in allelic effects between the most closely related populations, while allowing for heterogeneity between more diverse ethnic groups. The vast majority of SZ GWAS have been conducted in Caucasian ancestry samples. Because the LD patterns in the HLA region vary across ethnic groups, transethnic mapping may confirm risk detected at primary risk loci, while diluting the OR at other loci. The trans-ethnic approach is not without pitfalls. Because the frequency of a causal variant and exposure to putative interacting environmental risk factors is likely to be different across populations, the marginal effect may vary and may even be undetectable in some samples. Further complexity may result from allelic heterogeneity at some loci. As conventional GWAS assume similar allelic effects at different loci, Morris and colleagues have introduced Meta-ANalysis of Transethnic Association (MANTRA) studies software that takes into account expected similarity in allelic effects between closely related populations using a Bayesian partition model [Morris, 2011]. There have been prior efforts to conduct GWAS in non-Caucasian ancestry samples, including Japanese [Ikeda et al., 2013] and African-American samples [Shi et al., 2009]. Associations corrected for multiple comparisons have not been detected in the HLA region in these samples (possibly due to insufficient sample size), but suggestive associations have been reported [Bamne et al., 2012; Ikeda et al., 2013]. MANTRA analysis may be worthwhile in these samples.

Test Functional Correlates of Associated SNPs None of the current HLA associated SNPs in SZ have known biological functions. The Encyclopedia of DNA Elements (ENCODE) project could help clarify cellular functional correlates of the associated SNPs (http://www.ncbi.nlm.nih.gov/geo/info/ENCODE.html). Since ENCODE datasets do not focus extensively on neuronal cellular functions, additional analyses using post-mortem tissue and neuronal cultures are necessary. In conjunction with the in vitro analyses, we advocate additional clinically focused investigations. In view of the intriguing epidemiologic links between auto-immune disease and SZ risk, in silico analysis of shared HLA risk variants for individual auto-immune diseases and SZ is easy to motivate, as described above. If informative links are found, it

24 would be important to investigate pathogenic mechanisms through further clinical investigations. Such analyses entail the inclusion of auto-antibody status in conjunction with diagnostic status for SZ/ auto-immune disease as part of further genetic association analyses. Therefore we recommend screening for autoimmune disorders among SZ patients and if feasible, among their unaffected relatives in future studies. In view of the evidence for neuro-inflammation in SZ, it will be fruitful to investigate associations between HLA risk SNPs and suitable immune related markers in the plasma, as well as exposure to selected infectious agents. We recognize that the complexity of the immune system and the multiple comparisons dilemma require careful selection of variables for such analyses. Finally, we recommend analysis of the SZ risk SNPs in relation to suitable indices of non-immune functions as they relate to genes on chromosome 6p, for example, indices of NOTCH4 function. In conclusion, mega analyses based on GWAS studies indicate highly significant associations with SZ in the HLA region. Together with the similar patterns of associations noted in smaller individual samples, they provide strong support for risk allele/s in this region. Additional research is needed to refine the search for causal variant/ s and to understand the functional correlates of the associated variants.

ACKNOWLEDGEMENTS We thank the Stanley Medical Research Institute (Grant 07R-1712) and National Institutes of Health (MH63480, MH085269, MH083756, D43 TW008302) for support. We thank Dr. Bernie Devlin for helpful discussions regarding population sub-structure and Dr. Thomas Lehner for constructive comments on an earlier version of this manuscript.

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