Following publication of our two articles [1, 2], a critique of the methodology of Phenotype-Genotype Many-to-Many Relations Analysis (PGMRA) [1, 3, 4] questioned the validity of our results from the perspective of polygenic risk scores (PRS) [5]. We appreciate the importance of these questions, and here provide a concise discussion of the assumptions and mathematical constraints of both approaches. We thank this commentator and others who have discussed our articles with us for their thoughtful questions and critique

Cloninger, C. Robert

de Erausquin, Gabriel A.

Del-Val, Coral

Gu, C. Charles

Lehtimäki, Terho

Mishra, Pashupati

Zwir, Igor

English

Scholarworks@UTRGV Univ. of Texas RioGrande Valley

University of Texas Rio Grande Valley ScholarWorks @ UTRGV School of Medicine Publications and Presentations School of Medicine 2020 Uncovering the complex genetics of human personality: response from authors on the PGMRA Model Igor Zwir Pashupati Mishra Coral Del-Val C. Charles Gu Gabriel A. de Erausquin The University of Texas Rio Grande Valley See next page for additional authors Follow this and additional works at: https://scholarworks.utrgv.edu/som_pub  Part of the Medicine and Health Sciences Commons, and the Psychology Commons Recommended Citation Zwir, I., Mishra, P., Del-Val, C. et al. Uncovering the complex genetics of human personality: response from authors on the PGMRA Model. Mol Psychiatry 25, 2210–2213 (2020). https://doi.org/10.1038/s41380-019-0399-z This Article is brought to you for free and open access by the School of Medicine at ScholarWorks @ UTRGV. It has been accepted for inclusion in School of Medicine Publications and Presentations by an authorized administrator of ScholarWorks @ UTRGV. For more information, please contact justin.white@utrgv.edu, william.flores01@utrgv.edu. Authors Igor Zwir, Pashupati Mishra, Coral Del-Val, C. Charles Gu, Gabriel A. de Erausquin, Terho Lehtimäki, and C. Robert Cloninger This article is available at ScholarWorks @ UTRGV: https://scholarworks.utrgv.edu/som_pub/213 Molecular Psychiatry (2020) 25:2210–2213https://doi.org/10.1038/s41380-019-0399-zCORRESPONDENCEUncovering the complex genetics of human personality: responsefrom authors on the PGMRA ModelIgor Zwir1,2 ● Pashupati Mishra3 ● Coral Del-Val2 ● C. Charles Gu4 ● Gabriel A. de Erausquin5 ●Terho Lehtimäki3 ● C. Robert Cloninger 1,6Received: 10 February 2019 / Accepted: 14 February 2019 / Published online: 18 March 2019© The Author(s) 2019. This article is published with open accessFollowing publication of our two articles [1, 2], a critique ofthe methodology of Phenotype-Genotype Many-to-ManyRelations Analysis (PGMRA) [1, 3, 4] questioned thevalidity of our results from the perspective of polygenic riskscores (PRS) [5]. We appreciate the importance of thesequestions, and here provide a concise discussion of theassumptions and mathematical constraints of both approa-ches. We thank this commentator and others who havediscussed our articles with us for their thoughtful questionsand critiques.Complex phenotypes present several challenges forgenome-wide association studies including the presenceof epistasis, pleiotropy, and heterogeneity. We approa-ched these problems in a data-driven fashion to test thehypothesis that the heritability expected from twin studiesbut unexplained by genetic studies is distributed in het-erogeneous partitions of a complex trait, each withdistinct genotypic-phenotypic associations. We designeda machine learning algorithm termed PGMRA [1, 3, 4] toidentify naturally occurring partitions in the data in anunsupervised fashion. PGMRA first dissects genome-widedata and uncovers a genotypic architecture composed ofsets of SNPs shared by subsets of individuals (i.e., SNPsets [3, 6]). Next, phenotypic data are independentlyorganized into natural sets of features such as clinicalmanifestations [4], voxels of neuroimages [7], or per-sonality traits [1, 2] in a phenomic-like approach [8].Cross-matching of the two types of sets reveals multipleassociations restricted to subgroups of individuals,thereby uncovering the genotypic-phenotypic architectureof a trait and accounting for its distributed genetic risk orpropensity.Both approaches, PRS and PGMRA, rely on genome-wide markers (Fig. 1). However, PRS treats these markersas independent variables with additive effects, whereasPGMRA searches for sets of structurally connected mar-kers, which may have interactive effects (epistasis). PRSassumes a global linear association model and relies onincreasing sample size to improve performance [9, 10]. Incontrast, PGMRA uncovers a family of models (i.e., SNPsets), each of which computes in a local partition of thedata. Each model can be represented as either a linearcombination of data (as in regression trees) or as a non-linear combination (as in some neural networks) [11].Therefore, PGMRA uses a more complex model than PRS,focusing on incorporating more phenotypic variables ratherthan more individuals, but allows the use of smaller samplesby reducing multiple comparisons.PRS algorithms must reduce phenotypes to a singledependent variable because they use a linear supervisedmodel [12]. In contrast, PGMRA uses an unbiased andunsupervised model to consider all possible phenotypicpatterns common to a subset of individuals, regardless oftheir trait status (i.e., does not assign cases and controls apriori). Distinct patterns of phenotypic features can thus be* C. Robert Cloningercrcloninger44@gmail.com1 Washington University School of Medicine, Department ofPsychiatry, St. Louis, MO, USA2 University of Granada, Department of Computer Science,Granada, Spain3 Department of Clinical Chemistry, Fimlab Laboratories, andFinnish Cardiovascular Research Center - Tampere, Faculty ofMedicine and Health Technology, Tampere University,Tampere, Finland4 Washington University, School of Medicine, Division ofBiostatistics, St. Louis, MO, USA5 University of Texas Rio-Grande Valley, School of Medicine,Department of Psychiatry and Neurology, and Institute ofNeurosciences, Harlingen, TX, USA6 Washington University, School of Arts and Sciences, Departmentof Psychological and Brain Sciences, and School of Medicine,Department of Genetics, St. Louis, MO, USA1234567890();,:1234567890();,:associated with different SNP sets, thereby uncoveringheterogeneous subtypes of the trait [1, 2, 4]. Finally,PGMRA incorporates trait status a posteriori to calculate therisk of such associations, and then independently tests thesignificance of the associations by a SNP-set KernelAssociation Test [6, 13].The validity of the replication procedure used byPGMRA was questioned too [5]. The “gold standard”approach used by PRS evaluates the reproducibility of anassociation by building a linear classifier trained in a dis-covery sample and testing it in a new sample assumingsample homogeneity [9, 10]. Homogeneity is a strongassumption that should be supported. By contrast, PGMRAuncovers genotypic-phenotypic associations for samplepartitions and computes their corresponding risk or pro-pensity post hoc; this process is blindly repeated indepen-dently for each new sample without assuming homogeneitywithin or across samples (Fig. 1). Then, similar genotypic-phenotypic associations across samples with comparablerisk/propensity are uncovered using parsimonious modelsthat balance accuracy with model complexity, therebyavoiding overfitting [11, 14, 15].Inconsistent results obtained from applying PRS to het-erogeneous samples [16, 17] has led to the suggestion ofaveraging scores from multiple samples [18] ignoring, atleast in part, the phenotypic heterogeneity of the samples.When there is complexity derived from genetic, cultural,ethnic and environmental heterogeneity, the same globallinear model is unlikely to predict across samples, especiallywhen markers have relatively small effect [12, 16, 17].Models learned independently in diverse samples allowanalysis of replication across potentially heterogeneoussamples, thereby providing a more stringent test of repro-ducibility [19, 20].Fig. 1 Flow chart describing thecommon, as well as thedifferent, roads followed bymethods developed to buildpolygenic scores and thePGMRA methodUncovering the complex genetics of human personality: response from authors on the PGMRA Model 2211PRS calculates heritability as an adjusted R2 from aglobal linear regression, which additively estimates varianceexplained by the markers. In the absence of a validatedestimator of variance for “sets” of markers [6, 13], PGMRAused a similar approach (Fig. 1). For example, the estimatedheritability of character, without controlling for outliers andjackknife resampling, in the Finns sample [1] was 45.67%.A criticism [5] questioned the lack of application of anothersampling technique such as cross-validation. As suggested,we applied cross-validation within and across samples (e.g.,R2 of 10 k-fold is 45.05% with SD 0.049) and confirmed theobserved results by bootstrapping (1,000 iterations, SE <1.6%). We also found that the estimates of heritability forcharacter in our paper [1] are conservative: the aggregationof the local variances explained by all SNP sets delivers ahigher estimation of heritability (R2 > 15%) than the 45.67%described above (Fig. 1, unpublished results).Some suggest that our sample size (2126+ 972+ 902individuals from 3 cohorts, respectively [1, 2]) has insuffi-cient power, even though others have calculated 80% powerat nominal significance to detect heritability with the samesample size [12]. PGMRA computes genotypic-phenotypicassociations based on “sets” of genotypes and “sets” ofphenotypes, so the number of multiple comparisons aresignificantly reduced, making PGMRA less greedy ofobservations than PRS.The nature of human beings embraces complex functionswhere every expressed gene may affect the function of any celland their derived traits of our body in many different ways(many-to-many relationships). Complex traits are expected andknown to be influenced by multiple genes acting in concert,not independently [21]. Most of the heritability in geneexpression is determined by many genes far apart on the sameor different chromosomes [21–23], whose effects are difficultto detect due to their small magnitude (e.g., trans eQTLseffects), as well as co-expressed genes that are vulnerable todecoherence in response to environmental perturbations [24].PGMRA opens the door to develop new methods to explaincomplex genotypic-phenotypic relationships, including epis-tasis, pleiotropy and heterogeneous phenotypes, which presentproblems for PRS due to its restrictive linear model anddoubtful assumption of homogeneity. Use of PGMRA wouldallow more thorough study of moderate-sized samples byefficient data-driven methods, which can help to bring meth-ods of precision medicine into practice [1–3, 7, 20, 25].Compliance with ethical standardsConflict of interest The authors declare that they have no conflict ofinterest.Publisher’s note: Springer Nature remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which permits use, sharing,adaptation, distribution and reproduction in any medium or format, aslong as you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons license, and indicate ifchanges were made. The images or other third party material in thisarticle are included in the article’s Creative Commons license, unlessindicated otherwise in a credit line to the material. If material is notincluded in the article’s Creative Commons license and your intendeduse is not permitted by statutory regulation or exceeds the permitteduse, you will need to obtain permission directly from the copyrightholder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.References1. Zwir I, Arnedo J, Del-Val C, Pulkki-Raback L, Konte B, Yang SSet al. Uncovering the complex genetics of human character. MolPsychiatry. (2018). https://doi.org/10.1038/s41380-018-0263-6.[Epub ahead of print].2. Zwir I, Arnedo J, Del-Val C, Pulkki-Raback L, Konte B, Yang SSet al. Uncovering the complex genetics of human temperament.Mol Psychiatry. (2018). https://doi.org/10.1038/s41380-018-0264-5. [Epub ahead of print].3. Arnedo J, del Val C, de Erausquin GA, Romero-Zaliz R, SvrakicD, Cloninger CR, et al. PGMRA: A web server for (Phenotype XGenotype) many-to-many relation analysis in GWAS. NucleicAcids Res. 2013;41(Web Server issue):W142–9.4. 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Uncovering the complex genetics of human personality: response from authors on the PGMRA Model

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Uncovering the complex genetics of human personality: response from authors on the PGMRA Model

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