Digenic analysis confirms known and uncovers novel schizophrenia risk genes

Jian Ning, B.S. ¹; Xingyu Wang, Ph.D. ²; Jurg Ott, Ph.D. ³

1.  Center of Statistical Genetics, West Orange, NJ 07052, USA
2.  National Center for Human Genetic Resources, National Research Institute for Family Planning, Beijing, China 100012, and Rockefeller University, New York, NY 10065, USA
3.  Rockefeller University, New York, NY 10065, USA

OPEN ACCESS

PUBLISHED: 30 September 2024

CITATION: Ning, J., Wang, X., et al., 2024. Digenic analysis confirms known and uncovers novel schizophrenia risk genes. Medical Research Archives, [online] 12(9). https://doi.org/10.18103/mra.v12i9.5723

COPYRIGHT: © 2024 European Society of Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

DOI https://doi.org/10.18103/mra.v12i9.5723

ISSN 2375-1924

ABSTRACT

Based on an Ashkenazy Jewish case-control cohort for schizophrenia, we carried out (1) genetic association analysis for one variant at a time (GWAS) and (2) digenic analysis by comparing frequencies of genotype pairs between cases and controls. To control for genetic heterogeneity between sexes, we analyzed males and females separately. After pruning of variants in each of males and females, single-variant allelic analysis furnished 9 and 8 statistically significant variants in males and females, respectively, with 3 of these variants being significant in both males and females. Of the 14 distinct variants in males and females, 5 (36%) reside in genes. For digenic analysis, we evaluated all pairs of variants and, for a given variant pair, all nine genotype pairs. For each genotype pair, we applied the Fisher exact test to evaluate whether the given genotype pair was more frequent in cases than controls. We found 76 significant genotype pairs, comprising 36 distinct variants, 20 (56%) of which reside in genes, with many of which being known risk genes, thus lending credence to our approach.

Keywords

schizophrenia, genetic association, digenic analysis, Ashkenazy Jewish cohort, genotype pairs

Introduction

Schizophrenia is a rather common, highly genetic trait that is also regulated epigenetically and environmentally. The Human Gene Database lists 15,235 entries for schizophrenia. Many of its disease-associated variants are preserved across populations. Because of its high genetic heterogeneity, we chose to look for disease-associated variants in a founder population and downloaded an Ashkenazy Jewish case-control dataset from dbGaP.

Most investigations of schizophrenia-associated variants have been carried out in case-control studies by assessing direct (main) effects of variants, that is, differences in allele or genotype frequencies between cases and controls, possibly combined over multiple variants in a gene or in the form of polygenic risk scores over large numbers of variants. To date, 287 loci with significant association to schizophrenia have been identified.

Mathematical models have shown that empirical segregation ratios of schizophrenia fit polygenic models better than single-locus models. Thus, as outlined below, we proceeded to search for risk variants based on pairwise interactions between genotypes in addition to direct variant effects. Such an exhaustive search for all pairs of variant genotypes has previously required enormous computing efforts, but modern workstations containing dozens of threads (central processing units, CPUs) have made this task more manageable. Thus, our Gpairs program not only evaluates all possible pairs of variants, but, for a given variant pair, it tests each of the 3 × 3 = 9 pairs of genotypes whether the pair occurs in higher frequency in cases than controls.

Methods

DATA

A dataset entitled Genetics of Schizophrenia in an Ashkenazi Jewish Case-Control Cohort was downloaded from dbGaP in the form of a binary plink-formatted fileset. Customary quality-control measures reduced the original 1,016,422 genetic variants down to 892,850 variants, each genotyped in 3,096 individuals (1,044 cases and 2,052 controls; 2,164 males and 932 females).

Even though our data represent an ethnically homogeneous set of individuals, other sources of heterogeneity are of potential concern, notably differences in heritability between males and females from a genetic and a biological perspective. For example, for Parkinson Disease, a striking effect of sex on monocyte gene expression has been shown, with a note on the “importance of studies which examine the differential effects of sex on pathophysiology” of disease. Also, there are clear morphological differences in the brains of the two sexes, gene expression differences between male and female schizophrenics have been documented, association between troponin T levels and psychosis have only been found in women, not in men, a coronary heart disease genetic risk score predicted disease risk only in men, not women, sex differences in gene regulatory networks underlying lung cancer have been documented, and a sex difference exists in the association between cannabis use disorder and schizophrenia. Allowing for sex in case-control studies may be accomplished through a logistic regression model. However, rather than imposing the constraints of such a model, we decided to analyze the data in sex-specific subgroups, which would eliminate any heterogeneity due to sex differences although at the cost of smaller numbers of individuals in each of the resulting two datasets. Analyzing a heterogeneous combined dataset would be expected to lead to false positive results. Thus, we proceeded to separately analyze the 2,164 males (660 cases, 1,504 controls) and 932 females (384 cases, 548 controls). In each of the two resulting data subsets, we imposed a minimum minor allele frequency of 0.01 and made variants relatively independent by applying the “indep 50 5 2” option in plink. These steps resulted in 179,104 variants in males and 179,898 variants in females.

Potential heterogeneity is often addressed by the use of principal components as covariates in a logistic regression analysis. However, the steps outlined above did not seem to necessitate the use of principal components, and there are also concerns regarding their use.

GENETIC ANALYSIS

Initially, we carried out a standard GWAS in each of the two sexes and applied the Fisher exact test as implemented in plink (–assoc function). Empirical significance levels (p-values), corrected for multiple testing, were obtained in 100,000 permutations of phenotypes. Results were declared statistically significant for p < 0.05. For digenic analysis, we applied the Gpairs program as previously described to evaluate, separately for males and females, all pairs of genotypes. For each genotype pair, we applied the Fisher exact test to see whether the given genotype pair was more frequent in cases than controls. Correction for multiple testing was carried out by the Bonferroni method.

Results

SINGLE-VARIANT GWAS

In males, 9 variants were significant (p < 0.05), while 8 variants were significant in females, as shown in Tables 1 and 2. Of the 17 significant variants, 3 were shared between males and females, that is, single-variant analysis furnished a total of 14 unique significant variants, of which 5 (36%) reside in genes.

In males, 9 variants were significant (p < 0.05), while 8 variants were significant in females, as shown in Tables 1 and 2. Of the 17 significant variants, 3 were shared between males and females, that is, single-variant analysis furnished a total of 14 unique significant variants, of which 5 (36%) reside in genes.

DIGENIC ANALYSIS

In males, our analysis of genotype pairs (patterns) resulted in 69 patterns with significantly higher frequencies in cases than controls (p < 0.05) while in females, 7 genotype pairs were significant. None of the significant genotype pairs were the same in males and females although a few of the individual variants making up the pairs were shared (see below). Thus, we combined genotype pairs from males and females, resulting in 76 genotype pairs or, equivalently, 76 variant pairs leading to these genotype pairs. The combined 76 variant pairs are listed in Supplementary Table S1, along with their genic locations, if known.

For a given variant pair, the two component variants were either in two genes, only in one gene, or not in any gene, which occurred for 14 (18%), 44 (58%), and 18 (24%) variant pairs, respectively. The total of 76 variant pairs comprised 36 distinct variants, 20 (56%) of which reside in genes. Table 3 lists these variants, along with gene names where a variant is located, and the number n of other variants connected with the given variant.

For a given variant pair, the two component variants were either in two genes, only in one gene, or not in any gene, which occurred for 14 (18%), 44 (58%), and 18 (24%) variant pairs, respectively. The total of 76 variant pairs comprised 36 distinct variants, 20 (56%) of which reside in genes. Table 3 lists these variants, along with gene names where a variant is located, and the number n of other variants connected with the given variant.

Here we briefly discuss the 20 genes we have significantly identified as forming gene pairs associated with schizophrenia.

ATG2A, “autophagy-related 2A”, also known as BLTP4A, has recently been mentioned as one of 11 autophagy-related differentially expressed mRNA genes potentially involved in schizophrenia. Various autophagy-related genes have previously been implicated as risk factors for schizophrenia and other psychiatric disorders.

CDKAL1 is a protein-coding gene without known function. Various reports have demonstrated an association between CDKAL1 and gestational diabetes, and an older study showed association of CDKAL1 with type 2 diabetes. These associations seem to be related to the known increased prevalence in schizophrenics of type 2 diabetes, which is associated with CDKAL1.

CEP295, also known as KIAA1731 and SCKL11, is a protein coding gene that has just recently been shown to be associated with a Seckel-like syndrome involving intellectual disability and short stature, but we have been unable to find any references that associate the CEP295 gene with schizophrenia. Based on Supplementary Table S1, CEP295 is indirectly connected with several genes listed here via associations to variants outside of genes.

GRM5, also known as GPRC1E, MGLUR5, PPP1R86, and mGlu5, encodes a protein functioning as a metabotropic glutamate receptor, with a restricted expression toward the brain. It is an emerging target for the treatment of schizophrenia. Recently, GRM2 and GRM3 have been implicated for their differential expressions in brains of schizophrenics versus controls. Our results suggest that GRM5 may function in a similar manner.

H3C6, also known as H3.1, H3/d, H3C1, H3C2, H3C3, H3C4, H3C7, H3C8, H3FD, H3C10, H3C11, H3C12, and HIST1H3E, is a protein coding gene lacking introns. It encodes one of the histones responsible for the nucleosome structure. While we have not found any reports on a direct association of H3C6 with schizophrenia, post-translational modifications of histones have been suggested to play a role in the etiology of schizophrenia.

IRS2 has been listed as a schizophrenia-associated gene with a strong fold change in expression level compared with that in databases.

LARGE1, also known as LARGE, MDC1D, MDDGA6, and MDDGB6, is mostly expressed in brain and heart and, to a lesser degree, in various other tissues. We have not found much information about a direct association between this gene and schizophrenia, but a very recent report in medRxiv lists LARGE1 as one of several genes involved in a pathway of neuroinflammatory response of the nervous system to various forms of damage, which may be connected to an inherent genetic predisposition to neurodegenerative aspects of schizophrenia.

LIFR (chr 5: 38,474,668..38,608,403 bp, complement), Leukemia Inhibitory Factor Receptor, is a protein coding gene, and LIFR-AS1 (chr 5: 38,556,786..38,671,216 bp) is a noncoding RNA gene. LIFR is located at 22q12.1-q12.2, a hot spot for schizophrenia, and was associated more than ten years ago with schizophrenia. Also, a large-scale transcriptomic meta-analysis of patient brain tissues with single-cell sequencing data of CNS neurons involving LIFR and other genes, was able to shed light on the well-known sexual dimorphism of schizophrenia.

MGAT4C is a protein coding gene with biased expression in thyroid, brain, and three other tissues. For several of the MGAT genes, but not for MGAT4C, post-translational protein modifications in schizophrenics have been demonstrated. On the other hand, in a very recent schizophrenia case-control study of individuals of Chinese descent, recurrent somatic copy number variations were observed at several chromosomal regions including MGAT4C.

OR2L13, also known as OR2L14, is a protein coding gene on chr 1 (247,937,177..248,101,163 bp) and LOC105373275 is an uncharacterized non-coding RNA gene on chr 1 (248,047,705..248,095,542 bp, complement). A decrease in taste receptor expression in the brain has been reported for several genes but results for OR2L13 were not statistically significant. Our results strengthen that earlier report.

PCDH9 is a protein coding gene with biased expression in brain, fat, and two other tissues. In a large GWAS for Major Depressive Disorder (MDD), PCDH9 was identified as a novel risk factor. In that study, individuals with schizophrenia had been excluded, but our data strongly suggest that PCDH9 also plays a role in schizophrenia, if only through its connections with other genes. In other publications, however, PCDH9 has clearly been implicated in playing a role in familial schizophrenia.

RP1L1, also known as DCDC4B, OCMD, and RP88, is a protein coding gene and has in many publications been associated with photoreceptor diseases including macular dystrophy and retinitis pigmentosa. Recently, genetic association analyses between cognitive impairment in schizophrenia showed results for large numbers of variants, including RP1L1, although the statistical significance for the involvement of RP1L1 was unclear.

TMEM161B, also known as FLB3342 and PRO1313, acts as a regulator of sonic hedgehog signaling and, in mouse models, plays a CNS-specific role; it is also associated with defective formation of folds of the early brain development (polymicrogyria). In a case-control study of Chinese freshmen, major depressive disorder was associated with TMEM161B. In that study, individuals with schizophrenia and bipolar disorder were excluded, but our analysis strongly suggests an involvement of TMEM161B in schizophrenia. Another transmembrane protein, TMEM204, has been listed as being differentially expressed in schizophrenics.

The following additional 7 schizophrenia-associated genes are not directly connected with other genes but with variants outside of genes:

CD226, also known as PTA1, DNAM1, DNAM-1, TLiSA1, encodes a glycoprotein on the surface of several cell types. We have not found evidence for direct association between CD226 and schizophrenia. In a mendelian randomization study, CD226 was one of five proteins with a causal relationship to psychiatric disorders.

E2F3 is a protein coding gene and encodes a transcription factor. In our analysis, E2F3 is significantly connected with three other genes. It is one of a large number of genes interacting with other genes in their relation to abnormal psychomotor behavior characteristics in schizophrenia and other severe mental disorders.

GPR88. The protein encoded by GPR88 is a G protein-coupled receptor with particularly robust expression in the brain. It is emerging as a potential drug target for CNS-related diseases including schizophrenia. GPR88 has been shown long ago to be a risk factor for psychiatric traits in three different populations. Another G protein receptor gene, GPR56, has been published as being differentially expressed in schizophrenics.

IGF2BP2, also known as IMP-2, IMP2, and VICKZ2, is a protein coding gene. Significant associations between IGF2BP2 and type 2 diabetes as well as with schizophrenia have been found in Iran.

LINC02789 is a non-coding RNA gene. A recent study pointed out that plant-derived miRNAs can be found in the human body through eating and can then affect post-transcriptional gene regulation by binding to human mRNAs. In that study, miRNAs were shown to bind to 33 human mRNAs associated with schizophrenia and other human traits. LINC02789 is one of many potential target genes of 84 wheat miRNAs identified in humans.

LOC107984378 is an uncharacterized non-coding RNA gene, located at chr 11p14.3 at bp 24,118,969..24,158,536. It contains an enhancer sequence, bp 24,155,823..24,156,117.

UBE2W, also known as UBC16 and UBC-16, is a protein coding gene with broad expression in brain, thyroid, and 25 other tissues. It encodes an enzyme, E2, in the ubiquitin proteasome system (UPS). In recent years, several publications reported association of schizophrenia with disruption of the UPS although UBE2W is not generally mentioned specifically. Another ubiquitin conjugating enzyme, UBE2G1, has been implicated in schizophrenia.

Combining variants from our GWAS and digenic analysis, and eliminating duplicates, we wound up with 38 unique significant variants located outside of genes, shown in Supplementary Table S2.

Discussion

It has long been postulated that for common human traits, interactions among genes (and environmental effects) may be the norm rather than the exception. Indeed, in our single-variant GWAS, only a relatively small number of variants were detected as being significantly associated with schizophrenia, but many more were significant based on pairs of genotypes involving different variants. Given that thousands of variants contribute to schizophrenia risk, it is gratifying to see that with powerful statistical methods, we can find 165 risk variants on the basis of only slightly more than 1,000 cases and 2,000 controls. Quite a few of these variants have previously been identified as being disease associated, which lends credence to our approach.

The statistical significance of our results appears highly reliable, particularly for our digenic analysis, where we had to rely on Bonferroni correction, which is known to be conservative. It is also immune to dependency among test items – genotype pairs in our situation, which are somewhat dependent as a given variant tends to occur in multiple genotype pairs. While many of our variants detected in digenic analysis are located in genes, many others were found outside of genes. The functions of these variants are unknown at this time but there can be no doubt that they are associated with schizophrenia.

A standard GWAS evaluates disease association for one variant at a time, which is most appropriate for monogenic traits. Polygenic traits like schizophrenia, however, should be addressed with methods allowing for the combined disease association of multiple variants. Early approaches in this direction considered family pedigree lod scores over multiple variants and their correlations, combination of p-values over multiple contiguous markers in the form of scan statistics, and sums of test statistics over large numbers of markers anywhere in the genome. The current version of similar approaches for capturing the genetic liability to disease are polygenic risk scores (PRSs), several of which have recently been published for schizophrenia. All these methods, including PRSs, represent aggregations of main effects while digenic analysis captures main and interaction effects although only over two variants at a time. A combination of multiple genotype pairs, perhaps over thousands of them, in the form of a polygenic risk score would presumably capture both main and interaction effects for large numbers of variants. We plan to develop such an approach.

Conclusion

Our digenic analysis has uncovered or confirmed 36 significant variants, quite a few more than the 14 significant variants found in standard GWAS. The value of our contribution is that it confirms previous tentative associations and points to new assignments not previously known, which are worth being followed up.

Conflicts of Interest Statement

The authors have no conflicts of interest to declare.

Acknowledgments

We are greatly indebted to Dr. Manoj Kandpal, Rockefeller University, for invaluable expertise in downloading and decrypting the data from dbGaP, supported in part by grant # UL1TR001866 from the NIH National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Clinical and Translational Science Award (CTSA) program.

The datasets used for the analysis described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000448.v1.p1. Submission of the data, phs000448.v1.p1, to dbGaP was provided by Dr. Todd Lencz and on behalf of himself and his collaborator, Ariel Darvasi, Ph.D. Support for the collection and analysis of the datasets was provided by RC2MH089964, R01MH084098, the North Shore – LIJ Health System Foundation, and the Hebrew University Genetic Resource.

Internet links

Gpairs program: https://lab.rockefeller.edu/ott/programs/GPM, https://github.com/jurgott/

Human Gene Database for schizophrenia: https://www.genecards.org/Search/Keyword?queryString=Schizophrenia&sort=Score&sortdir=Descending&startPage=0&pageSize=-1

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