Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus.
Humans
Schizophrenia
/ genetics
Female
Male
Hippocampus
/ metabolism
Caudate Nucleus
/ metabolism
Quantitative Trait Loci
Sex Characteristics
Dorsolateral Prefrontal Cortex
/ metabolism
Adult
Transcriptome
Gene Expression Profiling
Sex Factors
Chromosomes, Human, X
/ genetics
Prefrontal Cortex
/ metabolism
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
10 May 2024
10 May 2024
Historique:
received:
21
11
2022
accepted:
15
04
2024
medline:
11
5
2024
pubmed:
11
5
2024
entrez:
10
5
2024
Statut:
epublish
Résumé
Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.
Identifiants
pubmed: 38730231
doi: 10.1038/s41467-024-48048-z
pii: 10.1038/s41467-024-48048-z
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3980Informations de copyright
© 2024. The Author(s).
Références
Goldstein, J. M. et al. Are there sex differences in neuropsychological functions among patients with schizophrenia? Am. J. Psychiatry 155, 1358–1364 (1998).
pubmed: 9766767
doi: 10.1176/ajp.155.10.1358
Eranti, S. V., MacCabe, J. H., Bundy, H. & Murray, R. M. Gender difference in age at onset of schizophrenia: a meta-analysis. Psychol. Med. 43, 155–167 (2013).
pubmed: 22564907
doi: 10.1017/S003329171200089X
Faraone, S. V., Chen, W. J., Goldstein, J. M. & Tsuang, M. T. Gender differences in age at onset of schizophrenia. Br. J. Psychiatry 164, 625–629 (1994).
pubmed: 7921712
doi: 10.1192/bjp.164.5.625
Khashan, A. S. et al. Higher risk of offspring schizophrenia following antenatal maternal exposure to severe adverse life events. Arch. Gen. Psychiatry 65, 146–152 (2008).
pubmed: 18250252
doi: 10.1001/archgenpsychiatry.2007.20
Migeon, B. R. X-linked diseases: susceptible females. Genet. Med. 22, 1156–1174 (2020).
pubmed: 32284538
pmcid: 7332419
doi: 10.1038/s41436-020-0779-4
Hoffman, G. E. et al. Sex differences in the human brain transcriptome of cases with schizophrenia. Biol. Psychiatry 91, 92–101 (2022).
pubmed: 34154796
doi: 10.1016/j.biopsych.2021.03.020
Qin, W., Liu, C., Sodhi, M. & Lu, H. Meta-analysis of sex differences in gene expression in schizophrenia. BMC Syst. Biol. 10, 9 (2016).
pubmed: 26818902
pmcid: 4895727
doi: 10.1186/s12918-015-0250-3
Oliva, M. et al. The impact of sex on gene expression across human tissues. Science 369, eaba3066 (2020).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
pmcid: 4112379
doi: 10.1038/nature13595
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
pubmed: 29483656
pmcid: 5918692
doi: 10.1038/s41588-018-0059-2
Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).
pubmed: 35396580
pmcid: 9392466
doi: 10.1038/s41586-022-04434-5
Benjamin, K. J. M. et al. Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes. Nat. Neurosci. 25, 1559–1568 (2022).
pubmed: 36319771
pmcid: 10599288
doi: 10.1038/s41593-022-01182-7
Collado-Torres, L. et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron 103, 203–216.e8 (2019).
pubmed: 31174959
pmcid: 7000204
doi: 10.1016/j.neuron.2019.05.013
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
pubmed: 27668389
pmcid: 5083142
doi: 10.1038/nn.4399
Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).
pubmed: 30050107
pmcid: 6438700
doi: 10.1038/s41593-018-0197-y
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Kukurba, K. R. et al. Impact of the X Chromosome and sex on regulatory variation. Genome Res 26, 768–777 (2016).
pubmed: 27197214
pmcid: 4889977
doi: 10.1101/gr.197897.115
Yao, C. et al. Sex- and age-interacting eQTLs in human complex diseases. Hum. Mol. Genet. 23, 1947–1956 (2014).
pubmed: 24242183
doi: 10.1093/hmg/ddt582
Shen, J. J., Wang, Y.-F. & Yang, W. Sex-interacting mRNA- and miRNA-eQTLs and their implications in gene expression regulation and disease. Front. Genet. 10, 313 (2019).
pubmed: 31024623
pmcid: 6465513
doi: 10.3389/fgene.2019.00313
GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
pmcid: 5776756
doi: 10.1038/nature24277
Sun, L., Wang, Z., Lu, T., Manolio, T. A. & Paterson, A. D. eXclusionarY: 10 years later, where are the sex chromosomes in GWASs? Am. J. Hum. Genet. 110, 903–912 (2023).
pubmed: 37267899
pmcid: 10257007
doi: 10.1016/j.ajhg.2023.04.009
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
doi: 10.1186/1471-2105-9-559
Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).
pubmed: 21283776
pmcid: 3024255
doi: 10.1371/journal.pcbi.1001057
Benjamin, K. J. M., Katipalli, T. & Paquola, A. C. M. dRFEtools: Dynamic recursive feature elimination for omics. Bioinformatics https://doi.org/10.1093/bioinformatics/btad513 (2023).
doi: 10.1093/bioinformatics/btad513
pubmed: 37632789
pmcid: 10471895
Vernet, R. et al. Identification of novel genes influencing eosinophil-specific protein levels in asthma families. J. Allergy Clin. Immunol. 150, 1168–1177 (2022).
pubmed: 35671886
doi: 10.1016/j.jaci.2022.05.017
Yengo, L. et al. A saturated map of common genetic variants associated with human height. Nature 610, 704–712 (2022).
pubmed: 36224396
pmcid: 9605867
doi: 10.1038/s41586-022-05275-y
A Gadd, D. et al. The genetic and epigenetic profile of serum S100β in the Lothian Birth Cohort 1936 and its relationship to Alzheimer’s disease. Wellcome Open Res. 6, 306 (2021).
pubmed: 35028426
doi: 10.12688/wellcomeopenres.17322.1
Alliey-Rodriguez, N. et al. NRXN1 is associated with enlargement of the temporal horns of the lateral ventricles in psychosis. Transl. Psychiatry 9, 230 (2019).
pubmed: 31530798
pmcid: 6748921
doi: 10.1038/s41398-019-0564-9
Carlson, J. C. et al. Genome-wide interaction studies identify sex-specific risk alleles for nonsyndromic orofacial clefts. Genet. Epidemiol. 42, 664–672 (2018).
pubmed: 30277614
pmcid: 6185762
doi: 10.1002/gepi.22158
Trabzuni, D. et al. Widespread sex differences in gene expression and splicing in the adult human brain. Nat. Commun. 4, 2771 (2013).
pubmed: 24264146
doi: 10.1038/ncomms3771
Mayne, B. T. et al. Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans. Front. Genet. 7, 183 (2016).
pubmed: 27790248
pmcid: 5062749
doi: 10.3389/fgene.2016.00183
Gershoni, M. & Pietrokovski, S. The landscape of sex-differential transcriptome and its consequent selection in human adults. BMC Biol. 15, 7 (2017).
pubmed: 28173793
pmcid: 5297171
doi: 10.1186/s12915-017-0352-z
Tukiainen, T. et al. Landscape of X chromosome inactivation across human tissues. Nature 550, 244–248 (2017).
pubmed: 29022598
pmcid: 5685192
doi: 10.1038/nature24265
Lopes-Ramos, C. M. et al. Sex differences in gene expression and regulatory networks across 29 human tissues. Cell Rep. 31, 107795 (2020).
pubmed: 32579922
pmcid: 7898458
doi: 10.1016/j.celrep.2020.107795
Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
pubmed: 25954002
pmcid: 4547472
doi: 10.1126/science.aaa0355
Balaton, B. P., Cotton, A. M. & Brown, C. J. Derivation of consensus inactivation status for X-linked genes from genome-wide studies. Biol. Sex. Differ. 6, 35 (2015).
pubmed: 26719789
pmcid: 4696107
doi: 10.1186/s13293-015-0053-7
Brand, B. A., de Boer, J. N. & Sommer, I. E. C. Estrogens in schizophrenia: progress, current challenges and opportunities. Curr. Opin. Psychiatry 34, 228–237 (2021).
pubmed: 33560022
pmcid: 8048738
doi: 10.1097/YCO.0000000000000699
Wang, J. Z., Du, Z., Payattakool, R., Yu, P. S. & Chen, C.-F. A new method to measure the semantic similarity of GO terms. Bioinformatics 23, 1274–1281 (2007).
pubmed: 17344234
doi: 10.1093/bioinformatics/btm087
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
pubmed: 30545856
pmcid: 6443102
doi: 10.1126/science.aat8127
Marques-Coelho, D. et al. Differential transcript usage unravels gene expression alterations in Alzheimer’s disease human brains. npj Aging Mech. Dis. 7, 2 (2021).
pubmed: 33398016
pmcid: 7782705
doi: 10.1038/s41514-020-00052-5
Leon, A. C. & Heo, M. Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model. Comput. Stat. Data Anal. 53, 603–608 (2009).
pubmed: 20084090
pmcid: 2678722
doi: 10.1016/j.csda.2008.06.010
Goldman-Rakic, P. S. Psychopathology and the Brain (eds Carroll, B. J. & Barrett, J. E.) (Raven Press, New York, 1991).
Selemon, L. D. Regionally diverse cortical pathology in schizophrenia: clues to the etiology of the disease. Schizophr. Bull. 27, 349–377 (2001).
pubmed: 11596841
doi: 10.1093/oxfordjournals.schbul.a006881
Perzel Mandell, K. A. et al. Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus. Mol. Psychiatry 27, 2061–2067 (2022).
pubmed: 35236959
doi: 10.1038/s41380-022-01453-6
Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).
pubmed: 27756721
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).
pubmed: 31375807
pmcid: 7605509
doi: 10.1038/s41587-019-0201-4
Graubert, A., Aguet, F., Ravi, A., Ardlie, K. G. & Getz, G. RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts. Bioinformatics 37, 3048–3050 (2021).
pubmed: 33677499
pmcid: 8479667
doi: 10.1093/bioinformatics/btab135
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Cotto, K. C. et al. Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat. Commun. 14, 1589 (2023).
pubmed: 36949070
pmcid: 10033906
doi: 10.1038/s41467-023-37266-6
Morgan, M., Obenchain, V., Hester, J. & Pagès, H. SummarizedExperiment: SummarizedExperiment container. (2022).
Eagles, N. J. et al. SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses. BMC Bioinforma. 22, 224 (2021).
doi: 10.1186/s12859-021-04142-3
Kent, W. J. et al. The human genome browser at UCSC. Genome Res 12, 996–1006 (2002).
pubmed: 12045153
pmcid: 186604
doi: 10.1101/gr.229102
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
pubmed: 33568819
pmcid: 7875770
doi: 10.1038/s41586-021-03205-y
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).
pubmed: 25338720
doi: 10.1093/bioinformatics/btu704
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
pubmed: 27694958
pmcid: 5096458
doi: 10.1038/ng.3679
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
pubmed: 25722852
pmcid: 4342193
doi: 10.1186/s13742-015-0047-8
Purcell, S. & Chang, C. PLINK., (2021).
Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).
pubmed: 17907809
doi: 10.1371/journal.pgen.0030161
Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).
pubmed: 25294822
pmcid: 4245966
doi: 10.1093/nar/gku864
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
pubmed: 22287627
pmcid: 3378882
doi: 10.1093/nar/gks042
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249
pmcid: 4053721
doi: 10.1186/gb-2014-15-2-r29
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Hoffman, G. E. & Roussos, P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 37, 192–201 (2021).
pubmed: 32730587
doi: 10.1093/bioinformatics/btaa687
Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).
pubmed: 16646809
doi: 10.2202/1544-6115.1027
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
pubmed: 25885710
pmcid: 4401657
doi: 10.1371/journal.pcbi.1004219
Mölder, F. et al. Sustainable data analysis with Snakemake. F1000Res. 10, 33 (2021).
pubmed: 34035898
pmcid: 8114187
doi: 10.12688/f1000research.29032.2
Breiman, L. Random Forests. Springer Science and Business. Media LLC https://doi.org/10.1023/a:1010933404324 (2001).
doi: 10.1023/a:1010933404324
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Jue, N. K. et al. Determination of dosage compensation of the mammalian X chromosome by RNA-seq is dependent on analytical approach. BMC Genom. 14, 150 (2013).
doi: 10.1186/1471-2164-14-150
Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
pubmed: 12808457
doi: 10.1038/ng1180
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).
pubmed: 25877637
pmcid: 4397996
doi: 10.1093/database/bav028
Yu, G., Wang, L.-G., Yan, G.-R. & He, Q.-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015).
pubmed: 25677125
doi: 10.1093/bioinformatics/btu684
Klopfenstein, D. V. et al. GOATOOLS: a Python library for Gene Ontology analyses. Sci. Rep. 8, 10872 (2018).
pubmed: 30022098
pmcid: 6052049
doi: 10.1038/s41598-018-28948-z
Yu, G. et al. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26, 976–978 (2010).
pubmed: 20179076
doi: 10.1093/bioinformatics/btq064
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).
pubmed: 26708335
doi: 10.1093/bioinformatics/btv722
Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).
pubmed: 31675989
pmcid: 6823959
doi: 10.1186/s13059-019-1836-7
Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).
pubmed: 30478440
doi: 10.1038/s41588-018-0268-8
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
pubmed: 33499903
pmcid: 7836161
doi: 10.1186/s13059-020-02252-4
Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).
doi: 10.1214/16-AOAS952
Wen, X., Lee, Y., Luca, F. & Pique-Regi, R. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet. 98, 1114–1129 (2016).
pubmed: 27236919
pmcid: 4908152
doi: 10.1016/j.ajhg.2016.03.029
Lee, Y., Francesca, L., Pique-Regi, R. & Wen, X. Bayesian multi-SNP genetic association analysis: control of FDR and use of summary statistics. BioRxiv (2018) https://doi.org/10.1101/316471 .
Wen, X., Pique-Regi, R. & Luca, F. Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLoS Genet 13, e1006646 (2017).
pubmed: 28278150
pmcid: 5363995
doi: 10.1371/journal.pgen.1006646
Pividori, M. et al. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Sci. Adv. 6, eaba2083 (2020).
pubmed: 32917697
doi: 10.1126/sciadv.aba2083
Hoffman, G. E. et al. CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder. Sci. Data 6, 180 (2019).
pubmed: 31551426
pmcid: 6760149
doi: 10.1038/s41597-019-0183-6
Storey, J. D., Bass, A. J., Dabney, A. & Robinson, D. Q-value estimation for false discovery rate control. Medicine 344, 48 (2020).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
pubmed: 27207943
doi: 10.1093/bioinformatics/btw313
Wickham, H. ggplot2 - Elegant Graphics for Data Analysis. (Springer International Publishing, 2016) https://doi.org/10.1007/978-3-319-24277-4 .
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
pubmed: 24930139
doi: 10.1093/bioinformatics/btu393
Plaisier, S. B., Taschereau, R., Wong, J. A. & Graeber, T. G. Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res. 38, e169 (2010).
pubmed: 20660011
pmcid: 2943622
doi: 10.1093/nar/gkq636
Cahill, K. M., Huo, Z., Tseng, G. C., Logan, R. W. & Seney, M. L. Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach. Sci. Rep. 8, 9588 (2018).
pubmed: 29942049
pmcid: 6018631
doi: 10.1038/s41598-018-27903-2
Benjamin, K. J. LieberInstitute/sex_differences_sz: updates for revision. Zenodo https://doi.org/10.5281/zenodo.8410992 (2023).
doi: 10.5281/zenodo.8410992
Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).
pubmed: 30718901
pmcid: 6522363
doi: 10.1038/s41593-018-0326-7
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
pubmed: 31043756
pmcid: 6956732
doi: 10.1038/s41588-019-0397-8
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
pubmed: 30804558
pmcid: 6454898
doi: 10.1038/s41588-019-0344-8
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
pubmed: 30478444
doi: 10.1038/s41588-018-0269-7
Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).
pubmed: 31308545
pmcid: 6779477
doi: 10.1038/s41588-019-0439-2
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
pubmed: 30124842
pmcid: 6488973
doi: 10.1093/hmg/ddy271
Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).
pubmed: 27863252
pmcid: 5300907
doi: 10.1016/j.cell.2016.10.042