Very interesting times for genetics & cognition - finding 8 genetic clusters of variants predicting schizophrenia symptoms: "Uncovering the Hidden Risk Architecture of the Schizophrenias: Confirmation in Three Independent Genome-Wide Association Studies", Arnedo et al 2014 (Fulltext: https://pdf.yt/d/rErZm8HUluBbj8xu / https://dl.dropboxusercontent.com/u/5317066/2014-arnedo.pdf / https://www.sendspace.com/file/ht7shm ; Press release: http://news.wustl.edu/news/Pages/27358.aspx ; journal abstract: http://ajp.psychiatryonline.org/article.aspx?articleid=1906049 ); abstract:

"*Objective*: The authors sought to demonstrate that schizophrenia is a heterogeneous group of heritable disorders caused by different genotypic networks that cause distinct clinical syndromes.
Method: In a large genome-wide association study of cases with schizophrenia and controls, the authors first identified sets of interacting single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (SNP sets) regardless of clinical status. Second, they examined the risk of schizophrenia for each SNP set and tested replicability in two independent samples. Third, they identified genotypic networks composed of SNP sets sharing SNPs or subjects. Fourth, they identified sets of distinct clinical features that cluster in particular cases (phenotypic sets or clinical syndromes) without regard for their genetic background. Fifth, they tested whether SNP sets were associated with distinct phenotypic sets in a replicable manner across the three studies.
Results: The authors identified 42 SNP sets associated with a 70% or greater risk of schizophrenia, and confirmed 34 (81%) or more with similar high risk of schizophrenia in two independent samples. Seventeen networks of SNP sets did not share any SNP or subject. These disjoint genotypic networks were associated with distinct gene products and clinical syndromes (i.e., the schizophrenias) varying in symptoms and severity. Associations between genotypic networks and clinical syndromes were complex, showing multifinality and equifinality. The interactive networks explained the risk of schizophrenia more than the average effects of all SNPs (24%).
Conclusions: Schizophrenia is a group of heritable disorders caused by a moderate number of separate genotypic networks associated with several distinct clinical syndromes.

...For example, twin and family studies of schizophrenia consistently indicate that the variability in risk of disease is highly heritable (81%) (6, 7), but only 25% of the variability has been explained by specific genetic variants identified in genome-wide association studies (GWAS) (8).

- "Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs" http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3327879/ , Lee et al 2012

...We have chosen to measure and characterize the complexity of both the genotypic and the phenotypic architecture of schizophrenia (Figure 1C). Past studies have generally ignored variation in clinical features, categorizing people as either having or not having schizophrenia, and they have looked only at the average effects of genetic variants, ignoring their organization into interactive genotypic networks. We postulate that schizophrenia heritability is not missing but is distributed into different networks of interacting genes that influence different people (15–17). Unlike previous studies that neglected clinical heterogeneity among subjects with schizophrenia (14, 18, 19), we characterized the clinical phenotype in detail. We also allowed for possible developmental complexity, including equifinality (or heterogeneity) and multifinality (or pleiotropy).
We investigated the architecture of schizophrenia in the Molecular Genetics of Schizophrenia (MGS) study, in which all subjects had consistent and detailed genotypic and phenotypic assessments (9). We then replicated the results in two other independent samples in which comparable genotypic and phenotypic features were available: the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE) and the Portuguese Island studies from the Psychiatric Genomics Consortium (PGC) (19–23).

- 9: Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe’er Dudbridge F, Holmans PA, Whittemore AS, Mowry BJ, Olincy A, Amin F, Cloninger CR, Silverman JM, Buccola NG, Byerley WF, Black DW, Crowe RR, Oksenberg JR, Mirel DB, Kendler KS, Freedman R, Gejman PV: "Common variants on chromosome 6p22.1 are associated with schizophrenia" http://www.researchgate.net/publication/26335355_Common_variants_on_chromosome_6p22.1_are_associated_with_schizophrenia/file/72e7e5212c534bd1b4.pdf . Nature 2009; 460:753–757

...We first identified sets of interacting single-nucleotide polymorphisms (SNPs) that cluster within subgroups of individuals (SNP sets) regardless of clinical status in the MGS Consortium study, employing our generalized factorization method (24–27) combined with non-negative matrix factorization to identify candidates for functional clusters (17) (see Figures S1 and S2 in the data supplement that accompanies the online edition of this article). This approach performs an unsupervised coclustering of subjects together with distinguishing genotypic/ phenotypic features based on the empirical data alone. We combined the Genetic Association Information Network (GAIN) and non-GAIN samples of the MGS study, which constitute one GWAS (9). The 4,196 cases and 3,827 controls in the MGS study were combined to identify SNP sets. We had data of good quality on 696,788 SNPs on these cases and controls, and from these we preselected 2,891 SNPs that had at least a loose association (p values ,1.0310 22 ) with a global phenotype of schizophrenia (see the data supplement). SNP sets were labeled by a pair of numbers based on the order in which they were chosen by the algorithm (see the data supplement). Each SNP set was composed of a particular group of subjects described by a particular set of homozygotic and/or heterozygotic alleles; subjects and/or SNPs may be present in more than one set (17, 24, 25). The SNP sets identified by our generalized factorization method are optimal clusters of SNPs in particular subjects that encode AND/OR interactions between SNPs and subjects (Figure 2A–F, Table 1; see also Figure S3 and the Method section in the data supplement). These SNP sets and their relations with one another characterize the genetic architecture of schizophreniaassociated SNPs in all subjects, including cases and controls (Figure 1A).

...The risk of schizophrenia was normally distributed, as expected when capturing the full range of variability. Ninety-eight of the 723 SNP sets had a risk of schizophrenia greater than 66% and accounted for 90% of schizophrenia cases in the MGS study. Forty-two SNP sets had a risk of schizophrenia $70% (Table 1; see also Figure S4 in the data supplement). For example, SNP set 19\_2 had a risk of 100%, meaning that all carriers were schizophrenia cases. The ability of SNP sets to predict schizophrenia risk is illustrated in Figure 2G. SKAT showed that the association of schizophrenia with particular SNP sets was stronger than with the average effects of their constituent SNPs (Table 1)...The global variance in liability to schizophrenia explained by the average effects of all SNPs simultaneously (8, 35) in our sample was 24%. While individual SNPs were mostly low penetrant, many high-risk SNP sets were highly penetrant (e.g., 100% to 70%; see Table 1) and much more informative in predicting schizophrenia risk.

...We hypothesized that schizophrenia may be an etiologically heterogeneous group of illnesses in which some genotypic networks are disjoint, that is, share neither SNPs nor subjects. To test this, we first checked for overlap in constituent SNPs and/or subjects among all the SNP sets at high risk for schizophrenia (see Figure S5 in the online data supplement). We found that 17 genotypic networks were disjoint, sharing neither SNPs nor subjects (Figure 3A), suggesting that these are distinct antecedents of schizophrenia. These networks vary in size and complexity: one highly connected network associates 11 SNP sets, whereas eight networks are composed of only a single isolated SNP set...Notably, all of these pathways are interconnected by the overlapping gene products that include genes previously associated with schizophrenia by GWAS, as well as genes known to be abnormally expressed in the brains of schizophrenia patients (see Table S4, Figure S7, and the Pathways section in the data supplement). The emerging picture is suggestive of a possible pathophysiology in which abnormal brain development interacts with environmental events triggering abnormal or exaggerated immune and oxidative processes that increase risk of schizophrenia.

...Using data from the Diagnostic Interview for Genetic Studies (30), as well as from the Best Estimate Diagnosis Code Sheet submitted by GAIN/non-GAIN to dbGaP (see Appendix I, Figures S1 and S2, and the Method section in the online data supplement), we originally identified 342 nonidentical and possibly overlapping phenotypic sets of distinct clinical features that cluster in particular cases with schizophrenia (i.e., phenotypic sets or clinical syndromes) without regard for their genetic background. Different SNP sets were significantly associated with particular clinical syndromes (hypergeometric statistics, p values from 2310 213 to 1310 23 ). However, the genotypicphenotypic relations were complex (i.e., many-to-many [29]): the same genotypic network could be associated with multiple clinical outcomes (i.e., multifinality or pleiotropy) and different genotypic networks could lead to the same clinical outcome (i.e., equifinality or heterogeneity; Table 3; see also Table S5 in the data supplement). The genotypicphenotypic relations were highly significant by a permutation test (empirical p value ,4.7310 23 ; Table 3; see also Table S5).
Specifically, we identified a phenotypic set indicating a general process of severe deterioration (i.e., continuous positive symptoms with marked and progressive impairment) that was associated with many SNP sets (e.g., SNP sets 75\_67 and 56\_30, with p values ,2.3310 213 and 2.55310 25 , respectively; Table 3, Figure 3A). Other SNP sets were associated with a general process of moderate deterioration (moderate or fluctuating impairment despite a continuous mixture of symptoms), as in SNP sets 14\_6, and 42\_37 (p values ,5310 24 ; Table 3, Figure 3A). We identified specific clinical syndromes that were unambiguously associated with particular genotypic networks. For example, specific phenotypic sets differentiate among SNP sets even within the same network, which illustrate similar but not identical forms of multifinality in schizophrenia (e.g., 76\_74 and 58\_29; Table 3, Figure 3A, blue lines). Particular phenotype sets can also distinguish SNP sets connected only by shared subjects (Figure 3A, red lines). For example, SNP set 76\_74 shares subjects with 56\_30 and with 81\_13; however, the latter SNP sets are associated with a specific phenotypic set not present in 76\_74 (Table 3).

...Genotypic and phenotypic relationships could be grouped into eight classes of schizophrenia, as shown in Figure 3B and Table 3 (31, 32, 36). First, we identified SNP sets involving subjects with predominantly positive symptoms (e.g., 41\_12 and 88\_64) and few residual symptoms. Second, we identified SNP sets represented by predominantly negative and disorganized symptoms (e.g., 10\_4 and 61\_39), decreased psychosocial function, and continuous residual symptoms. As discussed in the online data supplement (see the Replicability of the Phenotypic Features section), bizarre delusions and symptoms of cognitive and behavioral disorganization, such as thought insertion and disorganized speech among others, were accepted as fuzzy indicators of either positive or negative classes of schizophrenia but were considered to be more common in negative and disorganized classes (e.g., in Table 3, thought echo and commenting hallucinations in “negative schizophrenia” with phenotypic set 46\_29 associated with SNP set 14\_6). Third, several SNP sets harbor mixed positive and negative symptoms (e.g., 59\_48 and 54\_51). These three classes were enriched by considering the general severe and moderate patterns, which were frequent in several networks (Figure 3B), as described above. Because the latter patterns appear in combination with a set of only positive symptoms (e.g., 81\_13), both positive and negative symptoms (e.g., 75\_67), and only negative symptoms (e.g., 19\_2), we were able to classify schizophrenia into eight classes (Figure 3B). A principal-components analysis of the phenotypic features in the Diagnostic Interview for Genetic Studies confirmed this classification (see Table S6 and the Method section in the online data supplement).
...Panel B shows the classes of schizophrenia mapped to the disease architecture (see Table 3). Eight classes of schizophrenia were identified by independently characterizing each phenotypic feature included in a genotypicphenotypic relationship; classifying each item based on the symptoms as purely positive, purely negative, primarily positive, or primarily negative symptoms; and clustering these relationships based on their recoded phenotypic domain using non-negative matrix factorization. SNP sets harboring only positive symptoms are indicated in red, whereas those displaying negative symptoms are in green. Intermediate combinations including severe and/or moderate processes combined with positive and/or negative and/or disorganized symptoms were also color-coded. Dashed lines indicate nonsignificant matching.

...Consequently, identifying the organization of SNPs into interactive SNP sets enabled us to increase the power to detect associations: 98 SNP sets with greater than 66% risk accounted for 90% of cases.

...our initial pool of 2,891 SNPs, preselected for at least loose association with schizophrenia in the MGS study (9), might be missing additional risk SNPs that would eventually show up in an even more exhaustive genomic analysis.

...Here, internal replicability was addressed by resampling techniques (94% support; see the online data supplement), where the same SNP sets are systematically identified despite the random alteration of the parameters of the method (17) and/or the sample (38)....
In most GWAS, phenotypic data have been of “secondary” interest, using a variety of structured or even unstructured interviews (14, 18, 19) (see the Replicability section in the data supplement). So why not attempt to replicate the genotypic architecture alone? The same answer applies for any method for validation of associations: genetic variants associated with individuals may be, and in all likelihood often are, completely unrelated to the disease. The only way to make sense of these associations is to cross-match genomics with high-resolution phenomics (29). One can think of it as a “lock and key” combination (or, more precisely, many such combinations), where both pieces of information are needed to be able to interpret the results with confidence. Note that our approach complements meta-analysis (39) and/or pathway analyses (40), focusing the search on the combined genotypic-phenotypic architecture.
Despite the described constraints, we successfully identified more than 81% of the genotypic-phenotypic relationships previously found in the MGS data set in two independent samples. These samples were the only ones where both genotypic and detailed phenotypic features were available and provided by the researchers. Remarkably, the identification was performed with half of the SNPs used in the MGS study, because of the different platforms and our conservative preference to avoid external imputations. The success of our replication efforts strongly supports the validity and power resulting from combining genomic and phenomic information in association studies."