Copy number variations (CNVs) play a role in the etiology of various neuropsychiatric disorders including intellectual disability (Malhotra and Sebat, 2012), developmental delay (Malhotra and Sebat, 2012), congenital malformations (Cooper et al., 2011), autism spectrum disorder (ASD) (Sebat et al., 2007; Pinto et al., 2010), schizophrenia (SCZ) (Walsh et al., 2008; Levinson et al., 2011), bipolar disorder (BD) (Rucker et al., 2013), and recurrent depression (Malhotra et al., 2011). Microdeletions and microduplications of a 593 kb region of chromosome 16p11.2 have been implicated as a penetrant risk factor in the onset of neurodevelopmental disorders including ASD and SCZ (Weiss et al., 2008; McCarthy et al., 2009). This chromosomal region spans 29.4–32.2 Mb in the reference genome (GRCh37/hg19) and encompasses 29 genes, of which 25 are protein coding (Weiss et al., 2008; Jacquemont et al., 2011; Blumenthal et al., 2014). While estimates of prevalence vary, multiple studies report the presence of a deletion or duplication at the 16p11.2 chromosomal locus in approximately 1% of individuals with ASD (Sebat et al., 2007; Weiss et al., 2008; Jacquemont et al., 2011; Stefansson et al., 2014) and between 0.01% and 0.1% of the general population (Weiss et al., 2008; Jacquemont et al., 2011; Stefansson et al., 2014). A meta-analysis of seven studies suggests an overall prevalence of 0.76% 16p11.2 CNVs among idiopathic ASD probands (Walsh and Bracken, 2011).

The behavioral and physiological phenotypes associated with the 16p11.2 CNV include reciprocal, shared, and gene dosage-dependent abnormalities [Figure 1A]. Both microdeletions and microduplications of 16p11.2 are associated with ASD, although ASD represents a greater proportion of the diagnoses associated with 16p11.2 deletion (Walsh and Bracken, 2011; Glessner et al., 2009; Shen et al., 2010). Conversely, the risk of SCZ is greater in 16p11.2 microduplication carriers (Walsh et al., 2008; McCarthy et al., 2009). Reciprocal neuroanatomical phenotypes of the 16p11.2 CNV include differences in head size and brain volume. Specifically, individuals with a microdeletion of the 16p11.2 locus present with macrocephaly, increased overall gray and white matter volumes, increased cortical surface area, and increased axial diffusivity of white matter tracts such as the anterior corpus callosum and the internal and external capsules (Shinawi et al., 2010; Qureshi et al., 2014; Owen et al., 2014). Individuals with a microduplication of 16p11.2 present with microcephaly and corresponding decreases in gray matter, white matter, and cortical surface area (Shinawi et al., 2010; Qureshi et al., 2014). Recent studies have characterized independent and reciprocal abnormalities in both auditory processing delays and the amplitude of visual evoked potentials among individuals with the 16p11.2 CNVs (Jenkins et al., 2016; LeBlanc and Nelson, 2016). Finally, obesity and hyperphagia are observed among deletion carriers, while low body mass index (BMI) is observed in duplication carriers (Jacquemont et al., 2011; Walters et al., 2010).

Figure 1

Download asset Open asset

The underlying cellular and molecular mechanisms by which neuropsychiatric disorders develop remain largely unknown. Investigators probing the etiology of neurodevelopmental disorders acquired a powerful new tool with the discovery that human somatic cells can be reprogrammed into human induced pluripotent stem cells (hiPSCs) which, in turn, can be differentiated into ectodermal, endodermal, and mesodermal derivatives (Takahashi et al., 2007; Yu et al., 2007). In the years that followed, the field of hiPSC-mediated neurodevelopmental disease modeling, while hampered by intra- and inter-patient variability (Brennand et al., 2012), has capitalized on the medium’s unique ability to provide: (1) a model of human development at pathologically-relevant time points, (2) an unlimited source of cells for examination, and (3) tissue-specific cell types which share the genetic background of the somatic cell donor. While initial efforts were focused on monogenic neuropsychiatric disorders (Marchetto et al., 2010; Urbach et al., 2010; Paşca et al., 2011), recent work has expanded to include early investigations of gene-environment interactions (Hogberg et al., 2013) and complex polygenic disorders (Brennand, 2011; Shcheglovitov et al., 2013; Yoon et al., 2014; Mariani et al., 2015; Madison et al., 2015).

hiPSCs have offered a novel window into human-specific alterations in neurodevelopment for a number of disorders. Previous work has shown that neurons derived from 16p11.2 patient hiPSCs exhibit abnormal somatic size and dendritic morphology (Deshpande et al., 2017). Interestingly, transcriptional and phenotypic abnormalities have also been observed in hiPSC-derived cortical neural stem cells derived from individuals with idiopathic ASD (Marchetto et al., 2017; Schafer et al., 2019). The 16p11.2 deletion has widespread effects on signaling pathways that underpin critical neural progenitor functions, including proliferation and fate choice (Pucilowska et al., 2018), yet 16p11.2 CNV-related alterations in gene expression patterns have not been examined in early neuroepithelial precursors (radial glia), the stem and progenitor cells of the developing cortex.

Here, we report the derivation, neural differentiation, and transcriptomic characterization of hiPSCs reprogrammed from donors with the 16p11.2 CNV. In total, 65 hiPSC clones were generated from 13 donors. Ten donors harbor a microdeletion at the 16p11.2 chromosomal locus and three donors carry a microduplication. All lines reported here are available through the Simons Foundation Autism Research Initiative (SFARI) (https://sfari.org/resources/autism-models/ips-cells). As a cautionary note, we also report on the relatively high frequency at which hiPSC clones were found to contain randomly integrated reprogramming vectors, in spite of the use of non-integrating episomal reprogramming strategies. In many clones, this led to the inappropriate expression of reprogramming genes in neural progenitor cells (NPCs), which had a more penetrant impact on genome-wide gene expression patterns than the 16p11.2 CNVs. Moreover, we report that only a subset of genes within the 16p11.2 CNV locus are expressed in early NPCs, and 14 of these genes show significant reduction in RNA abundance in clones that carry the 16p11.2 deletion. We also show that these alterations are accompanied by changes in the expression of 93 additional genes that are not located within the 16p11.2 deletion interval, including genes with known relevance in neurodevelopment. These genes impinge on signaling pathways relevant to recently described phenotypic abnormalities in neurons in vitro (Deshpande et al., 2017) and in human carriers in vivo (Shinawi et al., 2010; Qureshi et al., 2014). Finally, we expand upon these findings by exploring the correlation between gene networks and clinical traits from donors and identify cellular metabolism as a compelling avenue for continued investigation.

Current investigations into the etiology of neurodevelopmental disorders have been limited by an inability to recapitulate the developmental trajectory of the human brain under controlled laboratory conditions. hiPSCs offer a unique opportunity to study these early developmental mechanisms. Here, we report the generation and banking of publicly available resource of 65 hiPSC lines with CNVs at the 16p11.2 chromosomal locus. Furthermore, we employ this resource to identify transcriptional alterations that accompany deletion of the 16p11.2 locus in hiPSC-derived NPCs. 16p11.2 CNVs remain one of the most commonly identified variants associated with ASD and, in addition to the previously described links with ASD and SCZ, 16p11.2 CNVs are implicated in intellectual disability, language disorders, ADHD, motor disorders, and epilepsy (Shinawi et al., 2010; Rosenfeld et al., 2010; Hanson et al., 2015).

The hiPSC lines examined here were generated from biospecimens in the Simons Foundation VIP Collection and represent lines from 10 deletion donors and three duplication donors. These 65 lines are a significant addition to the 14 hiPSC clones recently described by Deshpande et al., 2017 and include donors that are age and sex matched across genotypes. 16p11.2 CNVs are implicated in a spectrum of non-CNS phenotypes, including those of the heart, kidneys, digestive tract, genitals, and bones (Zufferey et al., 2012). The recruited individuals for this study encompass a broad spectrum of physiological attributes and psychiatric diagnoses. A substantial number of donors is required to verify gene-specific phenotypes in hiPSC models due to intra- and inter-individual variability between clones (Brennand et al., 2012). Clone-to-clone variability, in part due to incomplete epigenetic remodeling and the resultant influence of epigenetic memory (Polo et al., 2010; Kim et al., 2010; Lister et al., 2011), may confound conclusions built upon line-to-line comparisons (Onder and Daley, 2012). Additionally, generating large genomic rearrangements in isogenic control lines through genetic engineering strategies such as the CRISPR/Cas9 system is technically challenging (Grobarczyk et al., 2015; Tai et al., 2016). The availability of a large number of clones provides an opportunity to explore phenotypes that may be relevant to both CNV-specific effects and the influence of an individual’s genetic background on the penetrance of a given phenotype. Importantly, this hiPSC collection contains at least three independently derived clones for most donors. It follows that this resource may significantly improve the field’s ability to identify morphological, physiological, and circuit-level characteristics at different stages of development that contribute to the etiology of 16p11.2-related phenotypes.

The large number of genes within the 16p11.2 region, and their wide-ranging functions, make it challenging to pinpoint specific genes relevant to neuropsychiatric disease. One of the goals of this work was to determine which 16p11.2 genes are expressed in early cortical development, as well as which show altered expression following the deletion of one allele. Clones that were free of integrated reprogramming vectors represented genomes from six individuals carrying 16p11.2 deletions. Of these, the deletion intervals were known for five subjects [Figure 5A]. We found that all 14 DE genes within the canonical 16p11.2 interval were located within a shorter interval of the chromosome where deletions overlapped for the five subjects with known breakpoints. This clustering to a sub-interval of the locus most likely reflects the fact that the majority of clones analyzed were missing one allele of each DE gene in this region. It is likely that clones with larger deletions may also show copy-number-dependent expression for genes outside of the shared interval. For example, it is reasonable to suspect that genes unique to the much larger deletion present in DEL_6_7 are downregulated, but that the difference is diluted in the current analysis by clones with normal copy number in this region.

The data presented can be used to confidently identify genes within the 16p11.2 locus that are expressed in early NPCs. Of 56 genes within and flanking the deletion intervals of the clones evaluated, 15 are expressed at or below the limits of detection. Although these genes may play roles at later stages of brain development, they seem unlikely to be relevant for the early patterning and initial establishment of neuronal subtype identity. Conversely, it also stands to reason that the remaining 41 genes are likely to have roles in early cortical development that may be affected when copy number is altered. Of these, 14 are differentially expressed and all are downregulated in DEL: MAZ, DOC2A, PRRT2, PAGR1, TAOK2, KIF22, HIRIP3, ALDOA, C16orf92, INO80E, MAPK3, KCTD13, SEZ6L2, and FAM57B. Their statistically significant downregulation suggests that these 16p11.2 loci are worthy of increased scrutiny as genes that may commonly influence developmental outcomes across individuals that share this deletion interval. Of particular note is the 16p11.2 region gene MAPK3, as its protein product Erk1 is known to interact with several other observed DE genes (e.g. SELE, CALML6, RAF1, SEMA4A). This finding supports existing work suggesting that the Erk1 MAPK pathway may drive phenotypic abnormalities associated with the 16p11.2 CNV (Pucilowska et al., 2018; Pucilowska et al., 2015). Although we detected a decrease in MAPK3 gene expression in all the 16p11.2 deletion lines [Figure 5—figure supplement 1A], we did not observe any changes in NPC proliferation between wild type and deletion lines indicating that MAPK3 may not influence NPC proliferation in these 16p11.2 patient lines [Figure 4—figure supplement 2A]. This is consistent with prior work that also found no significant difference in the proliferation of DEL cells at the NPC stage (Deshpande et al., 2017).

In addition to the 14 DE genes within the 16p11.2 locus, there are 93 additional genes that are differentially expressed. Fifteen of these genes are identified as being generally related to psychiatric disease, and nine are more specifically identified as related to ASD [Figure 5—figure supplement 3A]. All these genes are listed in the SFARI Gene Scoring Module database (Abrahams et al., 2013) and are associated with at least five published reports supporting their role as a candidate gene in ASD. Of these genes, CNTNAP2, a neurexin family member that is important excitatory synapse formation and function, is one of the strongest known candidate-genes for ASD risk (Abrahams et al., 2013). Several additional DE genes are also relevant to excitatory synapses in mature neurons (e.g. PRRT2, CALML6, C1QL3, DOC2A, GABRE, C1QL3, CNTNAP2), and may reflect early perturbations in gene networks relevant to cortical excitatory networks. Previous work reports changes in neurite branching and synapse development in more mature neurons (Deshpande et al., 2017), and here, we find genes that may be related to these later changes are differentially expressed well before such neurons emerge in culture.

Future studies might utilize these early transcriptional features to test whether related functions are affected in mature neurons. For example, the 16p11.2 region gene KCTD13 is important for the maintenance of synaptic transmission in the CA1 region of the hippocampus, with its loss resulting in decreased fEPSP slope and the frequency of mEPSP currents (Escamilla et al., 2017). Interestingly, the 16p11.2 region gene TAOK2 has been shown to influence the differentiation, basal dendrite formation, and axonal projection of pyramidal neurons in the cortex with its deletion resulting in abnormal dendrite formation and impaired axonal elongation in mouse brains (de Anda et al., 2012). We found the expression of KCTD13 and TAOK2 to be reduced across all deletion lines suggesting that reduced KCTD13 and TAOK2 expression in cortical progenitors may impact cortical network development and the maintenance of appropriate synaptic tone in the cortex of autistic individuals.

Other features that the DE gene list may predict include alterations related to movement or locomotion of the cell [Figure 5G]. Several DE genes modulate actin and cytoskeletal organization, which could contribute to abnormal cell morphology (e.g. SELE, TEKT4, TUBGCP4, KLHL4). Additionally, three downregulated genes are related to glucose metabolism and glycogen synthesis (e.g. TLCD3B, FBP2, ALDOA), suggesting that changes in cellular metabolism and energy management may influence outcomes. Finally, we observed that the transcript with the highest fold-change and smallest p-value was aligned the microRNA (miRNA) miR-6723. Although our sequencing protocol was not designed to accurately profile mature miRNAs, the library preparation would capture polyadenylated pri-miRNAs that could later be converted into mature miRNAs (Cai et al., 2004). The functions of many of these miRNAs are not well characterized but existing literature supports further investigation. miR-6723 itself is enriched in neurons relative to microglia, astrocytes, and endothelial cells (Spaethling et al., 2017). Interestingly, it is also downregulated in term placentas of women with chronic Chagas disease, suggesting a role in cellular proliferation or immune response (Winn et al., 2007; Juiz et al., 2018). Four other miRNAs appear in the differential expression gene list and have roles in proliferation that may impact progenitor function. Of note, miR-301B impairs cellular senescence and has a role in the spread of certain cancers (Ramalho-Carvalho et al., 2017; Danesh et al., 2018). miR-637 inhibits tumorigenesis, and its downregulation may enhance cell proliferation (Zhang et al., 2011; Zhang et al., 2018).

It is also worth noting that SNP array data show that 4 of the 13 DEL Int- clones contained additional CNVs, including microdeletion in 14q11.1, a microduplication in 14q11.2, and a microdeletion in 7p11.2. All of these CNVs are potentially associated with neurodevelopmental abnormalities (Zahir et al., 2007; Varvagiannis et al., 2014), and it is possible that these additional CNVs contribute to the transcriptional alterations. However, there is no significant difference in the average fold change of DE gene expression in DEL relative to WT between the subset of clones carrying these additional CNVs and the remaining DEL clones [Figure 5—figure supplement 1B]. Although it is important to document all background CNVs for future reference, these CNVs are unlikely to account for the transcriptional changes in the NPCs reported here.

This work also suggests that patient-specific transcriptional abnormalities, in addition to a core 16p11.2 transcript deficiency, may interact to generate a heterogeneous patient phenotype. Our WGCNA analysis identified several modules correlated with phenotypes within 16p11.2 deletion patients [Figure 6A]. Interestingly, these signals were related not only to higher order neurological functions, but to physical attributes and body plan. These modules do not contain genes identified as distinguishing DEL from WT samples. 16p11.2 region genes were most frequently observed in the grey module (n = 11 genes), followed by the blue (n = 3) and turquoise (n = 1) module. The grey module also contained the most DE genes (n = 32) in the DE pipeline presented in Figure 5 and genes identified in the linear mixed model pipeline (n = 16) [Figure 5—figure supplement 4]. This is to be expected given that the contrast examined with WGCNA was within DEL patients, rather than comparing all DEL clones with WT clones as in the DE analysis of Figure 5. However, it is possible that the gene programs accounting for phenotype heterogeneity within DEL are due to transcriptional programs that exist independent of the 16p11.2 deletion region. This further reinforces the possibility that patient-specific etiology may be due to an interaction of a generalized existing liability posed by the presence of the 16p11.2 deletion interacting with patient-specific features.

Within the four modules most highly correlated with a spectrum of patient traits, the member genes were most often significantly associated with metabolic functions [Figure 6C]. However, the strongest enrichments fell within a select set of functions related to cell responses to external stimuli [Figure 6D]. This supports the hypothesis that patient phenotypic variance might account for heterogeneous clinical presentation, and that this variance might emerge both in metabolic and signaling functions of the cell. However, the greater enrichment of cell-cell signaling pathways suggest that the phenotype might extend beyond simple metabolic dysfunction and include a breakdown of intercellular communication systems tightly linked to growth and metabolism of the cell. Several modules were significantly associated with one patient flagged with abnormal Behavior scores. However, we do not consider those modules here because Behavior-specific transcriptional associations are confounded with clone-specific associations.

Of the modules most highly associated with patient traits, the pink4 module of genes appeared to have the highest correlation with DEL patient phenotypes, and was one of the few modules highly associated with poor outcomes on a Language metric [Figure 6—figure supplement 2]. The genes most highly associated with this module contain regulators of major developmental cascades in development that could conceivably disrupt gene programs related to both nervous system function and general cellular growth, including Wnt pathway negative regulator WIF1, and Erk1/2 MAPK pathway effector FGF1 [Figure 6E]. Interestingly, increased Weight Z-score in this patient cohort was associated with reduced expression of insulin (INS), but increased expression of fetal growth-regulating insulin-like growth factor 2. Although the present study characterizes neural tissue, if generalized insulin-related signaling abnormalities exist in these patients, this disruption would likely be compounded by established alterations in MAPK signaling, an insulin signaling effector mediating cell growth, leading to a high BMI phenotype. Taken together with the observation made about the larger four-module block, the identification of BMI-associated genes within NPCs could be attributable to the fact that some aspects of the gene program modulated by 16p11.2 deletion may represent a generalized phenotype with tissue type-specific effects.

The pink4 module is similarly associated with two functions that could be conceivably impacted by dysregulation of the developing brain. One of these changes, Head Circumference Z-score, suggests a possible link with abnormal activity within NPCs, as head circumference has been associated with aberrant proliferation in NPCs, and DEL patients have been observed to have decreased head circumference (Qureshi et al., 2014; Vaccarino et al., 2009). Additionally, the association of this module with language deficits suggest a possible link to more mature circuit assembly and function, coincident with previous reports of language disruption associated within the 16p11.2 deletion phenotype (Blackmon et al., 2018; Kim et al., 2020). This could arise through a negative correlation with expression of AMIGO1, a adhesion molecule expressed throughout the nervous system associated with the development of dendrite outgrowth and the formation of fasciculated fiber tracts (Zhao et al., 2014) and dendritic outgrowth (Chen et al., 2012). These features are also negatively associated with calcium-activated potassium channel member KCNMB4, which has been previously associated with high-order disruption of neural function through its association with ASD in SNP association studies (Skafidas et al., 2014). Together, the correlation between these sets of genes and phenotypes associated with abnormal neurodevelopment invites speculation into the heterogeneous nature of language deficits in patients with 16p11.2 CNVs.

It is surprising that the pink4 module is associated not only with neural function and BMI, but also Age. While it is not possible to pinpoint what aspects of the genetic signature might be attributable to this association, the observed relationship between trait and expression of specific genes (e.g. Weight Z-score and INS expression) indicate that the combined correlations are not entirely attributable to normal changes in weight with age. The results of the WGCNA analysis suggest, as a whole, that several gene modules may be associated with a spectrum of clinical outcomes with 16p11.2 microdeletion, that some BMI-associated changes are detected even in a cell type presumably unrelated to BMI (neural progenitors), and that some of these correlations with clinical features involve genes outside of the 16p11.2 microdeletion. While further investigation is needed to untangle the complexity of patient-specific gene programs and generic 16p11.2 deletion effects, this work provides a springboard for further interrogation of hiPSC-derived cell types and features common to 16p11.2 microdeletion relative to features that may arise in individual donors due to other genetic background effects.

Finally, we emphasize the importance of screening clones for cryptically integrated reprogramming plasmids, especially as published methods highlight the lack of integration when using episomal reprogramming vectors. It is likely that these effects are most relevant for reprogramming methods that utilize DNA expression vectors and less likely for RNA-based delivery methods, such as modified-RNA or Sendai virus-based reprogramming. Many of the clones used in this study and available in the repository contain random integration of reprogramming plasmids that introduce strong transcriptional artifacts. The impact of cryptic reprogramming vector integration is well illustrated by the segregation of Int+ and Int- clones into identifiable clusters within the first two PCs in Figure 4A. These transcriptional perturbations could emerge from two possible sources: (1) an increase in reprogramming factor protein product brought about by the insertion of transcriptionally active transcripts or (2) a disruptive insertion of integrant into a transcriptionally active locus. We note that one Int+ clone (DEL_4_1) has a silenced insertion that contains integrant DNA [Figure 3—figure supplement 1] but did not produce detectable transcript that aligns to the integrated plasmid. Moreover, this Int+ clone has an expression profile that closely resembles those of the Int- clones [Figure 4D]. This is consistent with the observed transcriptional effects being primarily related to expression of the reprogramming factors rather than random insertional mutations. The strongest correlation appears to be due to plasmid-based expression of POU5F1. This is also suggested in our qRT-PCR data, where POU5F1 transcripts are not detected in clone DE_4_2, which is most similar in DE gene transcriptional patterns observed in the Int- clones [Figure 3—figure supplement 1]. The integration and expression of reprogramming vectors is accompanied by the differential expression of more than 3000 genes. Not surprisingly, the integration-associated DE gene list contains 136 genes previously identified as POU5F1 transcriptional targets, representing approximately 16% of genes cataloged in three putative POU5F1 target gene lists (Sharov et al., 2008). Given that such cryptic integrations may also exist in other hiPSC lines that have been reprogrammed using non-integrating episomal systems, we provide a customizable RNA-seq analysis pipeline that can detect expression from cryptic plasmids. It is computationally lightweight and can feasibly be performed on a laptop computer. This method provides users with the means to quickly interrogate their data and detect whether similar integration effects should be considered and addressed.

In summary, the collection of 65 human hiPSC clones from 13 donors with either a microdeletion or microduplication at the 16p11.2 chromosomal locus provides an important resource for studying the early neurodevelopmental alterations that may underlie physiological and behavioral features seen in individuals carrying 16p11.2 CNVs. The data presented herein identify several early transcriptomic network perturbations which may act as priming events for later functional alterations in more mature cell types (Schafer et al., 2019). A majority of the available clones have been thoroughly evaluated for reprogramming success, ploidy, SNPs, and the capacity to differentiate down the cortical neural lineage. The clones have also been screened and sorted for cryptic integration of the ‘non-integrating’ reprogramming vectors, and data from the subset of footprint-free lines provide several novel candidates for future studies of 16p11.2 deletion. The complete demographic and diagnostic information for each fibroblast donor, and instructions for obtaining the cells themselves, are available through SFARI at https://sfari.org/resources/autism-models/ips-cells. The comprehensive information collected from donors makes this hiPSC resource particularly valuable for investigating associations between in vitro phenotypes and clinical diagnoses that may be unique to a given individual or shared among 16p11.2 CNV carriers.

RNAseq data GEO Submission GSE144736 All additional data is included in the manuscript and supporting files.

1. 1. Palmer TD
   2. Muench K

   (2020) NCBI Gene Expression Omnibus

   ID GSE144736. Copy Number Variation at 16p11.2 Imparts Transcriptional Alterations in Neural Development in an hiPSC-derived Model of Corticogenesis.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144736

1. 1. Abrahams BS
   2. Arking DE
   3. Campbell DB
   4. Mefford HC
   5. Morrow EM
   6. Weiss LA
   7. Menashe I
   8. Wadkins T
   9. Banerjee-Basu S
   10. Packer A

   (2013) SFARI gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs)

   Molecular Autism 4:36.

   https://doi.org/10.1186/2040-2392-4-36

   • PubMed
   • Google Scholar
2. 1. Ang LT
   2. Tan AKY
   3. Autio MI
   4. Goh SH
   5. Choo SH
   6. Lee KL
   7. Tan J
   8. Pan B
   9. Lee JJH
   10. Lum JJ
   11. Lim CYY
   12. Yeo IKX
   13. Wong CJY
   14. Liu M
   15. Oh JLL
   16. Chia CPL
   17. Loh CH
   18. Chen A
   19. Chen Q
   20. Weissman IL
   21. Loh KM
   22. Lim B

   (2018) A roadmap for human liver differentiation from pluripotent stem cells

   Cell Reports 22:2190–2205.

   https://doi.org/10.1016/j.celrep.2018.01.087

   • Google Scholar
3. 1. Blackmon K
   2. Thesen T
   3. Green S
   4. Ben-Avi E
   5. Wang X
   6. Fuchs B
   7. Kuzniecky R
   8. Devinsky O

   (2018) Focal cortical anomalies and language impairment in 16p11.2 Deletion and Duplication Syndrome

   Cerebral Cortex 28:2422–2430.

   https://doi.org/10.1093/cercor/bhx143

   • PubMed
   • Google Scholar
4. 1. Blumenthal I
   2. Ragavendran A
   3. Erdin S
   4. Klei L
   5. Sugathan A
   6. Guide JR
   7. Manavalan P
   8. Zhou JQ
   9. Wheeler VC
   10. Levin JZ
   11. Ernst C
   12. Roeder K
   13. Devlin B
   14. Gusella JF
   15. Talkowski ME

   (2014) Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families

   The American Journal of Human Genetics 94:870–883.

   https://doi.org/10.1016/j.ajhg.2014.05.004

   • PubMed
   • Google Scholar
5. 1. Boyer LA
   2. Lee TI
   3. Cole MF
   4. Johnstone SE
   5. Levine SS
   6. Zucker JP
   7. Guenther MG
   8. Kumar RM
   9. Murray HL
   10. Jenner RG
   11. Gifford DK
   12. Melton DA
   13. Jaenisch R
   14. Young RA

   (2005) Core transcriptional regulatory circuitry in human embryonic stem cells

   Cell 122:947–956.

   https://doi.org/10.1016/j.cell.2005.08.020

   • PubMed
   • Google Scholar
6. 1. Bray NL
   2. Pimentel H
   3. Melsted P
   4. Pachter L

   (2016) Near-optimal probabilistic RNA-seq quantification

   Nature Biotechnology 34:525–527.

   https://doi.org/10.1038/nbt.3519

   • PubMed
   • Google Scholar
7. 1. Brennand K

   (2011) Modeling schizophrenia using hiPSC neurons

   Nature 473:221–225.

   https://doi.org/10.1038/nature09915

   • Google Scholar
8. 1. Brennand KJ
   2. Simone A
   3. Tran N
   4. Gage FH

   (2012) Modeling psychiatric disorders at the cellular and network levels

   Molecular Psychiatry 17:1239–1253.

   https://doi.org/10.1038/mp.2012.20

   • PubMed
   • Google Scholar
9. 1. Cai X
   2. Hagedorn CH
   3. Cullen BR

   (2004) Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs

   RNA 10:1957–1966.

   https://doi.org/10.1261/rna.7135204

   • PubMed
   • Google Scholar
10. 1. Chen Y
    2. Hor HH
    3. Tang BL

    (2012) AMIGO is expressed in multiple brain cell types and may regulate dendritic growth and neuronal survival

    Journal of Cellular Physiology 227:2217–2229.

    https://doi.org/10.1002/jcp.22958

    • PubMed
    • Google Scholar
11. 1. Cooper GM
    2. Coe BP
    3. Girirajan S
    4. Rosenfeld JA
    5. Vu TH
    6. Baker C
    7. Williams C
    8. Stalker H
    9. Hamid R
    10. Hannig V
    11. Abdel-Hamid H
    12. Bader P
    13. McCracken E
    14. Niyazov D
    15. Leppig K
    16. Thiese H
    17. Hummel M
    18. Alexander N
    19. Gorski J
    20. Kussmann J
    21. Shashi V
    22. Johnson K
    23. Rehder C
    24. Ballif BC
    25. Shaffer LG
    26. Eichler EE

    (2011) A copy number variation morbidity map of developmental delay

    Nature Genetics 43:838–846.

    https://doi.org/10.1038/ng.909

    • PubMed
    • Google Scholar
12. 1. Danesh H
    2. Hashemi M
    3. Bizhani F
    4. Hashemi SM
    5. Bahari G

    (2018) Association study of miR-100, miR-124-1, miR-218-2, miR-301b, miR-605, and miR-4293 polymorphisms and the risk of breast Cancer in a sample of iranian population

    Gene 647:73–78.

    https://doi.org/10.1016/j.gene.2018.01.025

    • PubMed
    • Google Scholar
13. 1. de Anda FC
    2. Rosario AL
    3. Durak O
    4. Tran T
    5. Gräff J
    6. Meletis K
    7. Rei D
    8. Soda T
    9. Madabhushi R
    10. Ginty DD
    11. Kolodkin AL
    12. Tsai LH

    (2012) Autism spectrum disorder susceptibility gene TAOK2 affects basal dendrite formation in the neocortex

    Nature Neuroscience 15:1022–1031.

    https://doi.org/10.1038/nn.3141

    • PubMed
    • Google Scholar
14. 1. Deshpande A
    2. Yadav S
    3. Dao DQ
    4. Wu Z-Y
    5. Hokanson KC
    6. Cahill MK
    7. Wiita AP
    8. Jan Y-N
    9. Ullian EM
    10. Weiss LA

    (2017) Cellular phenotypes in human iPSC-Derived neurons from a genetic model of autism spectrum disorder

    Cell Reports 21:2678–2687.

    https://doi.org/10.1016/j.celrep.2017.11.037

    • Google Scholar
15. 1. Elkabetz Y
    2. Panagiotakos G
    3. Al Shamy G
    4. Socci ND
    5. Tabar V
    6. Studer L

    (2008) Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage

    Genes & Development 22:152–165.

    https://doi.org/10.1101/gad.1616208

    • PubMed
    • Google Scholar
16. 1. Escamilla CO
    2. Filonova I
    3. Walker AK
    4. Xuan ZX
    5. Holehonnur R
    6. Espinosa F
    7. Liu S
    8. Thyme SB
    9. López-García IA
    10. Mendoza DB
    11. Usui N
    12. Ellegood J
    13. Eisch AJ
    14. Konopka G
    15. Lerch JP
    16. Schier AF
    17. Speed HE
    18. Powell CM

    (2017) Kctd13 deletion reduces synaptic transmission via increased RhoA

    Nature 551:227–231.

    https://doi.org/10.1038/nature24470

    • PubMed
    • Google Scholar
17. 1. Glessner JT
    2. Wang K
    3. Cai G
    4. Korvatska O
    5. Kim CE
    6. Wood S
    7. Zhang H
    8. Estes A
    9. Brune CW
    10. Bradfield JP
    11. Imielinski M
    12. Frackelton EC
    13. Reichert J
    14. Crawford EL
    15. Munson J
    16. Sleiman PM
    17. Chiavacci R
    18. Annaiah K
    19. Thomas K
    20. Hou C
    21. Glaberson W
    22. Flory J
    23. Otieno F
    24. Garris M
    25. Soorya L
    26. Klei L
    27. Piven J
    28. Meyer KJ
    29. Anagnostou E
    30. Sakurai T
    31. Game RM
    32. Rudd DS
    33. Zurawiecki D
    34. McDougle CJ
    35. Davis LK
    36. Miller J
    37. Posey DJ
    38. Michaels S
    39. Kolevzon A
    40. Silverman JM
    41. Bernier R
    42. Levy SE
    43. Schultz RT
    44. Dawson G
    45. Owley T
    46. McMahon WM
    47. Wassink TH
    48. Sweeney JA
    49. Nurnberger JI
    50. Coon H
    51. Sutcliffe JS
    52. Minshew NJ
    53. Grant SF
    54. Bucan M
    55. Cook EH
    56. Buxbaum JD
    57. Devlin B
    58. Schellenberg GD
    59. Hakonarson H

    (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes

    Nature 459:569–573.

    https://doi.org/10.1038/nature07953

    • PubMed
    • Google Scholar
18. 1. Grobarczyk B
    2. Franco B
    3. Hanon K
    4. Malgrange B

    (2015) Generation of isogenic human iPS cell line precisely corrected by genome editing using the CRISPR/Cas9 system

    Stem Cell Reviews and Reports 11:774–787.

    https://doi.org/10.1007/s12015-015-9600-1

    • PubMed
    • Google Scholar
19. 1. Hanson E
    2. Bernier R
    3. Porche K
    4. Jackson FI
    5. Goin-Kochel RP
    6. Snyder LG
    7. Snow AV
    8. Wallace AS
    9. Campe KL
    10. Zhang Y
    11. Chen Q
    12. D'Angelo D
    13. Moreno-De-Luca A
    14. Orr PT
    15. Boomer KB
    16. Evans DW
    17. Kanne S
    18. Berry L
    19. Miller FK
    20. Olson J
    21. Sherr E
    22. Martin CL
    23. Ledbetter DH
    24. Spiro JE
    25. Chung WK
    26. Simons Variation in Individuals Project Consortium

    (2015) The cognitive and behavioral phenotype of the 16p11.2 deletion in a clinically ascertained population

    Biological Psychiatry 77:785–793.

    https://doi.org/10.1016/j.biopsych.2014.04.021

    • PubMed
    • Google Scholar
20. 1. Hogberg HT
    2. Bressler J
    3. Christian KM
    4. Harris G
    5. Makri G
    6. O'Driscoll C
    7. Pamies D
    8. Smirnova L
    9. Wen Z
    10. Hartung T

    (2013) Toward a 3D model of human brain development for studying gene/environment interactions

    Stem Cell Research & Therapy 4:S4.

    https://doi.org/10.1186/scrt365

    • PubMed
    • Google Scholar
21. 1. Huang daW
    2. Sherman BT
    3. Lempicki RA

    (2009a) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

    Nucleic Acids Research 37:1–13.

    https://doi.org/10.1093/nar/gkn923

    • PubMed
    • Google Scholar
22. 1. Huang daW
    2. Sherman BT
    3. Lempicki RA

    (2009b) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • PubMed
    • Google Scholar
23. 1. Jacquemont S
    2. Reymond A
    3. Zufferey F
    4. Harewood L
    5. Walters RG
    6. Kutalik Z
    7. Martinet D
    8. Shen Y
    9. Valsesia A
    10. Beckmann ND
    11. Thorleifsson G
    12. Belfiore M
    13. Bouquillon S
    14. Campion D
    15. de Leeuw N
    16. de Vries BB
    17. Esko T
    18. Fernandez BA
    19. Fernández-Aranda F
    20. Fernández-Real JM
    21. Gratacòs M
    22. Guilmatre A
    23. Hoyer J
    24. Jarvelin MR
    25. Kooy RF
    26. Kurg A
    27. Le Caignec C
    28. Männik K
    29. Platt OS
    30. Sanlaville D
    31. Van Haelst MM
    32. Villatoro Gomez S
    33. Walha F
    34. Wu BL
    35. Yu Y
    36. Aboura A
    37. Addor MC
    38. Alembik Y
    39. Antonarakis SE
    40. Arveiler B
    41. Barth M
    42. Bednarek N
    43. Béna F
    44. Bergmann S
    45. Beri M
    46. Bernardini L
    47. Blaumeiser B
    48. Bonneau D
    49. Bottani A
    50. Boute O
    51. Brunner HG
    52. Cailley D
    53. Callier P
    54. Chiesa J
    55. Chrast J
    56. Coin L
    57. Coutton C
    58. Cuisset JM
    59. Cuvellier JC
    60. David A
    61. de Freminville B
    62. Delobel B
    63. Delrue MA
    64. Demeer B
    65. Descamps D
    66. Didelot G
    67. Dieterich K
    68. Disciglio V
    69. Doco-Fenzy M
    70. Drunat S
    71. Duban-Bedu B
    72. Dubourg C
    73. El-Sayed Moustafa JS
    74. Elliott P
    75. Faas BH
    76. Faivre L
    77. Faudet A
    78. Fellmann F
    79. Ferrarini A
    80. Fisher R
    81. Flori E
    82. Forer L
    83. Gaillard D
    84. Gerard M
    85. Gieger C
    86. Gimelli S
    87. Gimelli G
    88. Grabe HJ
    89. Guichet A
    90. Guillin O
    91. Hartikainen AL
    92. Heron D
    93. Hippolyte L
    94. Holder M
    95. Homuth G
    96. Isidor B
    97. Jaillard S
    98. Jaros Z
    99. Jiménez-Murcia S
    100. Helas GJ
    101. Jonveaux P
    102. Kaksonen S
    103. Keren B
    104. Kloss-Brandstätter A
    105. Knoers NV
    106. Koolen DA
    107. Kroisel PM
    108. Kronenberg F
    109. Labalme A
    110. Landais E
    111. Lapi E
    112. Layet V
    113. Legallic S
    114. Leheup B
    115. Leube B
    116. Lewis S
    117. Lucas J
    118. MacDermot KD
    119. Magnusson P
    120. Marshall C
    121. Mathieu-Dramard M
    122. McCarthy MI
    123. Meitinger T
    124. Mencarelli MA
    125. Merla G
    126. Moerman A
    127. Mooser V
    128. Morice-Picard F
    129. Mucciolo M
    130. Nauck M
    131. Ndiaye NC
    132. Nordgren A
    133. Pasquier L
    134. Petit F
    135. Pfundt R
    136. Plessis G
    137. Rajcan-Separovic E
    138. Ramelli GP
    139. Rauch A
    140. Ravazzolo R
    141. Reis A
    142. Renieri A
    143. Richart C
    144. Ried JS
    145. Rieubland C
    146. Roberts W
    147. Roetzer KM
    148. Rooryck C
    149. Rossi M
    150. Saemundsen E
    151. Satre V
    152. Schurmann C
    153. Sigurdsson E
    154. Stavropoulos DJ
    155. Stefansson H
    156. Tengström C
    157. Thorsteinsdóttir U
    158. Tinahones FJ
    159. Touraine R
    160. Vallée L
    161. van Binsbergen E
    162. Van der Aa N
    163. Vincent-Delorme C
    164. Visvikis-Siest S
    165. Vollenweider P
    166. Völzke H
    167. Vulto-van Silfhout AT
    168. Waeber G
    169. Wallgren-Pettersson C
    170. Witwicki RM
    171. Zwolinksi S
    172. Andrieux J
    173. Estivill X
    174. Gusella JF
    175. Gustafsson O
    176. Metspalu A
    177. Scherer SW
    178. Stefansson K
    179. Blakemore AI
    180. Beckmann JS
    181. Froguel P

    (2011) Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

    Nature 478:97–102.

    https://doi.org/10.1038/nature10406

    • PubMed
    • Google Scholar
24. 1. Jenkins J
    2. Chow V
    3. Blaskey L
    4. Kuschner E
    5. Qasmieh S
    6. Gaetz L
    7. Edgar JC
    8. Mukherjee P
    9. Buckner R
    10. Nagarajan SS
    11. Chung WK
    12. Spiro JE
    13. Sherr EH
    14. Berman JI
    15. Roberts TPL

    (2016) Auditory evoked M100 response latency is delayed in children with 16p11.2 Deletion but not 16p11.2 Duplication

    Cerebral Cortex 26:1957–1964.

    https://doi.org/10.1093/cercor/bhv008

    • Google Scholar
25. 1. Juiz NA
    2. Torrejón I
    3. Burgos M
    4. Torres AMF
    5. Duffy T
    6. Cayo NM
    7. Tabasco A
    8. Salvo M
    9. Longhi SA
    10. Schijman AG

    (2018) Alterations in placental gene expression of pregnant women with chronic chagas disease

    The American Journal of Pathology 188:1345–1353.

    https://doi.org/10.1016/j.ajpath.2018.02.011

    • PubMed
    • Google Scholar
26. 1. Kim K
    2. Doi A
    3. Wen B
    4. Ng K
    5. Zhao R
    6. Cahan P
    7. Kim J
    8. Aryee MJ
    9. Ji H
    10. Ehrlich LI
    11. Yabuuchi A
    12. Takeuchi A
    13. Cunniff KC
    14. Hongguang H
    15. McKinney-Freeman S
    16. Naveiras O
    17. Yoon TJ
    18. Irizarry RA
    19. Jung N
    20. Seita J
    21. Hanna J
    22. Murakami P
    23. Jaenisch R
    24. Weissleder R
    25. Orkin SH
    26. Weissman IL
    27. Feinberg AP
    28. Daley GQ

    (2010) Epigenetic memory in induced pluripotent stem cells

    Nature 467:285–290.

    https://doi.org/10.1038/nature09342

    • PubMed
    • Google Scholar
27. 1. Kim SH
    2. Green‐Snyder L
    3. Lord C
    4. Bishop S
    5. Steinman KJ
    6. Bernier R
    7. Hanson E
    8. Goin‐Kochel RP
    9. Chung WK

    (2020) Language characterization in 16p11.2 deletion and duplication syndromes

    American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 183:380–391.

    https://doi.org/10.1002/ajmg.b.32809

    • Google Scholar
28. Software

    1. Kolde R
    2. Kolde MR

    (2018) Package ‘pheatmap', version 1.0.12

    CRAN.

    https://CRAN.R-project.org/package=pheatmap
29. 1. Kriegstein A
    2. Alvarez-Buylla A

    (2009) The glial nature of embryonic and adult neural stem cells

    Annual Review of Neuroscience 32:149–184.

    https://doi.org/10.1146/annurev.neuro.051508.135600

    • PubMed
    • Google Scholar
30. 1. LeBlanc JJ
    2. Nelson CA

    (2016) Deletion and duplication of 16p11.2 are associated with opposing effects on visual evoked potential amplitude

    Molecular Autism 7:30.

    https://doi.org/10.1186/s13229-016-0095-7

    • PubMed
    • Google Scholar
31. 1. Leek JT
    2. Johnson WE
    3. Parker HS
    4. Jaffe AE
    5. Storey JD

    (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments

    Bioinformatics 28:882–883.

    https://doi.org/10.1093/bioinformatics/bts034

    • PubMed
    • Google Scholar
32. 1. Levinson DF
    2. Duan J
    3. Oh S
    4. Wang K
    5. Sanders AR
    6. Shi J
    7. Zhang N
    8. Mowry BJ
    9. Olincy A
    10. Amin F
    11. Cloninger CR
    12. Silverman JM
    13. Buccola NG
    14. Byerley WF
    15. Black DW
    16. Kendler KS
    17. Freedman R
    18. Dudbridge F
    19. Pe'er I
    20. Hakonarson H
    21. Bergen SE
    22. Fanous AH
    23. Holmans PA
    24. Gejman PV

    (2011) Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications

    American Journal of Psychiatry 168:302–316.

    https://doi.org/10.1176/appi.ajp.2010.10060876

    • PubMed
    • Google Scholar
33. 1. Lister R
    2. Pelizzola M
    3. Kida YS
    4. Hawkins RD
    5. Nery JR
    6. Hon G
    7. Antosiewicz-Bourget J
    8. O'Malley R
    9. Castanon R
    10. Klugman S
    11. Downes M
    12. Yu R
    13. Stewart R
    14. Ren B
    15. Thomson JA
    16. Evans RM
    17. Ecker JR

    (2011) Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells

    Nature 471:68–73.

    https://doi.org/10.1038/nature09798

    • PubMed
    • Google Scholar
34. 1. Loh KM
    2. Ang LT
    3. Zhang J
    4. Kumar V
    5. Ang J
    6. Auyeong JQ
    7. Lee KL
    8. Choo SH
    9. Lim CY
    10. Nichane M
    11. Tan J
    12. Noghabi MS
    13. Azzola L
    14. Ng ES
    15. Durruthy-Durruthy J
    16. Sebastiano V
    17. Poellinger L
    18. Elefanty AG
    19. Stanley EG
    20. Chen Q
    21. Prabhakar S
    22. Weissman IL
    23. Lim B

    (2014) Efficient endoderm induction from human pluripotent stem cells by logically directing signals controlling lineage bifurcations

    Cell Stem Cell 14:237–252.

    https://doi.org/10.1016/j.stem.2013.12.007

    • PubMed
    • Google Scholar
35. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
36. 1. Madison JM
    2. Zhou F
    3. Nigam A
    4. Hussain A
    5. Barker DD
    6. Nehme R
    7. van der Ven K
    8. Hsu J
    9. Wolf P
    10. Fleishman M
    11. O'Dushlaine C
    12. Rose S
    13. Chambert K
    14. Lau FH
    15. Ahfeldt T
    16. Rueckert EH
    17. Sheridan SD
    18. Fass DM
    19. Nemesh J
    20. Mullen TE
    21. Daheron L
    22. McCarroll S
    23. Sklar P
    24. Perlis RH
    25. Haggarty SJ

    (2015) Characterization of bipolar disorder patient-specific induced pluripotent stem cells from a family reveals neurodevelopmental and mRNA expression abnormalities

    Molecular Psychiatry 20:703–717.

    https://doi.org/10.1038/mp.2015.7

    • PubMed
    • Google Scholar
37. 1. Malhotra D
    2. McCarthy S
    3. Michaelson JJ
    4. Vacic V
    5. Burdick KE
    6. Yoon S
    7. Cichon S
    8. Corvin A
    9. Gary S
    10. Gershon ES
    11. Gill M
    12. Karayiorgou M
    13. Kelsoe JR
    14. Krastoshevsky O
    15. Krause V
    16. Leibenluft E
    17. Levy DL
    18. Makarov V
    19. Bhandari A
    20. Malhotra AK
    21. McMahon FJ
    22. Nöthen MM
    23. Potash JB
    24. Rietschel M
    25. Schulze TG
    26. Sebat J

    (2011) High frequencies of de novo CNVs in bipolar disorder and schizophrenia

    Neuron 72:951–963.

    https://doi.org/10.1016/j.neuron.2011.11.007

    • PubMed
    • Google Scholar
38. 1. Malhotra D
    2. Sebat J

    (2012) CNVs: harbingers of a rare variant revolution in psychiatric genetics

    Cell 148:1223–1241.

    https://doi.org/10.1016/j.cell.2012.02.039

    • PubMed
    • Google Scholar
39. 1. Marchetto MC
    2. Carromeu C
    3. Acab A
    4. Yu D
    5. Yeo GW
    6. Mu Y
    7. Chen G
    8. Gage FH
    9. Muotri AR

    (2010) A model for neural development and treatment of rett syndrome using human induced pluripotent stem cells

    Cell 143:527–539.

    https://doi.org/10.1016/j.cell.2010.10.016

    • PubMed
    • Google Scholar
40. 1. Marchetto MC
    2. Belinson H
    3. Tian Y
    4. Freitas BC
    5. Fu C
    6. Vadodaria K
    7. Beltrao-Braga P
    8. Trujillo CA
    9. Mendes APD
    10. Padmanabhan K
    11. Nunez Y
    12. Ou J
    13. Ghosh H
    14. Wright R
    15. Brennand K
    16. Pierce K
    17. Eichenfield L
    18. Pramparo T
    19. Eyler L
    20. Barnes CC
    21. Courchesne E
    22. Geschwind DH
    23. Gage FH
    24. Wynshaw-Boris A
    25. Muotri AR

    (2017) Altered proliferation and networks in neural cells derived from idiopathic autistic individuals

    Molecular Psychiatry 22:820–835.

    https://doi.org/10.1038/mp.2016.95

    • PubMed
    • Google Scholar
41. 1. Mariani J
    2. Coppola G
    3. Zhang P
    4. Abyzov A
    5. Provini L
    6. Tomasini L
    7. Amenduni M
    8. Szekely A
    9. Palejev D
    10. Wilson M
    11. Gerstein M
    12. Grigorenko EL
    13. Chawarska K
    14. Pelphrey KA
    15. Howe JR
    16. Vaccarino FM

    (2015) FOXG1-Dependent dysregulation of GABA/Glutamate neuron differentiation in autism spectrum disorders

    Cell 162:375–390.

    https://doi.org/10.1016/j.cell.2015.06.034

    • PubMed
    • Google Scholar
42. 1. McCarthy SE
    2. Makarov V
    3. Kirov G
    4. Addington AM
    5. McClellan J
    6. Yoon S
    7. Perkins DO
    8. Dickel DE
    9. Kusenda M
    10. Krastoshevsky O
    11. Krause V
    12. Kumar RA
    13. Grozeva D
    14. Malhotra D
    15. Walsh T
    16. Zackai EH
    17. Kaplan P
    18. Ganesh J
    19. Krantz ID
    20. Spinner NB
    21. Roccanova P
    22. Bhandari A
    23. Pavon K
    24. Lakshmi B
    25. Leotta A
    26. Kendall J
    27. Lee YH
    28. Vacic V
    29. Gary S
    30. Iakoucheva LM
    31. Crow TJ
    32. Christian SL
    33. Lieberman JA
    34. Stroup TS
    35. Lehtimäki T
    36. Puura K
    37. Haldeman-Englert C
    38. Pearl J
    39. Goodell M
    40. Willour VL
    41. Derosse P
    42. Steele J
    43. Kassem L
    44. Wolff J
    45. Chitkara N
    46. McMahon FJ
    47. Malhotra AK
    48. Potash JB
    49. Schulze TG
    50. Nöthen MM
    51. Cichon S
    52. Rietschel M
    53. Leibenluft E
    54. Kustanovich V
    55. Lajonchere CM
    56. Sutcliffe JS
    57. Skuse D
    58. Gill M
    59. Gallagher L
    60. Mendell NR
    61. Craddock N
    62. Owen MJ
    63. O'Donovan MC
    64. Shaikh TH
    65. Susser E
    66. Delisi LE
    67. Sullivan PF
    68. Deutsch CK
    69. Rapoport J
    70. Levy DL
    71. King MC
    72. Sebat J
    73. Wellcome Trust Case Control Consortium

    (2009) Microduplications of 16p11.2 are associated with schizophrenia

    Nature Genetics 41:1223–1227.

    https://doi.org/10.1038/ng.474

    • PubMed
    • Google Scholar
43. 1. Niwa H
    2. Miyazaki J
    3. Smith AG

    (2000) Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells

    Nature Genetics 24:372–376.

    https://doi.org/10.1038/74199

    • PubMed
    • Google Scholar
44. 1. Okita K
    2. Matsumura Y
    3. Sato Y
    4. Okada A
    5. Morizane A
    6. Okamoto S
    7. Hong H
    8. Nakagawa M
    9. Tanabe K
    10. Tezuka K
    11. Shibata T
    12. Kunisada T
    13. Takahashi M
    14. Takahashi J
    15. Saji H
    16. Yamanaka S

    (2011) A more efficient method to generate integration-free human iPS cells

    Nature Methods 8:409–412.

    https://doi.org/10.1038/nmeth.1591

    • PubMed
    • Google Scholar
45. 1. Onder TT
    2. Daley GQ

    (2012) New lessons learned from disease modeling with induced pluripotent stem cells

    Current Opinion in Genetics & Development 22:500–508.

    https://doi.org/10.1016/j.gde.2012.05.005

    • PubMed
    • Google Scholar
46. 1. Owen JP
    2. Chang YS
    3. Pojman NJ
    4. Bukshpun P
    5. Wakahiro ML
    6. Marco EJ
    7. Berman JI
    8. Spiro JE
    9. Chung WK
    10. Buckner RL
    11. Roberts TP
    12. Nagarajan SS
    13. Sherr EH
    14. Mukherjee P
    15. Simons VIP Consortium

    (2014) Aberrant white matter microstructure in children with 16p11.2 deletions

    Journal of Neuroscience 34:6214–6223.

    https://doi.org/10.1523/JNEUROSCI.4495-13.2014

    • PubMed
    • Google Scholar
47. 1. Paşca SP
    2. Portmann T
    3. Voineagu I
    4. Yazawa M
    5. Shcheglovitov A
    6. Paşca AM
    7. Cord B
    8. Palmer TD
    9. Chikahisa S
    10. Nishino S
    11. Bernstein JA
    12. Hallmayer J
    13. Geschwind DH
    14. Dolmetsch RE

    (2011) Using iPS cell-derived neurons to uncover cellular phenotypes associated with timothy syndrome

    Nature Medicine 17:1657–1662.

    https://doi.org/10.1038/nm.2576

    • PubMed
    • Google Scholar
48. 1. Pinto D
    2. Pagnamenta AT
    3. Klei L
    4. Anney R
    5. Merico D
    6. Regan R
    7. Conroy J
    8. Magalhaes TR
    9. Correia C
    10. Abrahams BS
    11. Almeida J
    12. Bacchelli E
    13. Bader GD
    14. Bailey AJ
    15. Baird G
    16. Battaglia A
    17. Berney T
    18. Bolshakova N
    19. Bölte S
    20. Bolton PF
    21. Bourgeron T
    22. Brennan S
    23. Brian J
    24. Bryson SE
    25. Carson AR
    26. Casallo G
    27. Casey J
    28. Chung BH
    29. Cochrane L
    30. Corsello C
    31. Crawford EL
    32. Crossett A
    33. Cytrynbaum C
    34. Dawson G
    35. de Jonge M
    36. Delorme R
    37. Drmic I
    38. Duketis E
    39. Duque F
    40. Estes A
    41. Farrar P
    42. Fernandez BA
    43. Folstein SE
    44. Fombonne E
    45. Freitag CM
    46. Gilbert J
    47. Gillberg C
    48. Glessner JT
    49. Goldberg J
    50. Green A
    51. Green J
    52. Guter SJ
    53. Hakonarson H
    54. Heron EA
    55. Hill M
    56. Holt R
    57. Howe JL
    58. Hughes G
    59. Hus V
    60. Igliozzi R
    61. Kim C
    62. Klauck SM
    63. Kolevzon A
    64. Korvatska O
    65. Kustanovich V
    66. Lajonchere CM
    67. Lamb JA
    68. Laskawiec M
    69. Leboyer M
    70. Le Couteur A
    71. Leventhal BL
    72. Lionel AC
    73. Liu XQ
    74. Lord C
    75. Lotspeich L
    76. Lund SC
    77. Maestrini E
    78. Mahoney W
    79. Mantoulan C
    80. Marshall CR
    81. McConachie H
    82. McDougle CJ
    83. McGrath J
    84. McMahon WM
    85. Merikangas A
    86. Migita O
    87. Minshew NJ
    88. Mirza GK
    89. Munson J
    90. Nelson SF
    91. Noakes C
    92. Noor A
    93. Nygren G
    94. Oliveira G
    95. Papanikolaou K
    96. Parr JR
    97. Parrini B
    98. Paton T
    99. Pickles A
    100. Pilorge M
    101. Piven J
    102. Ponting CP
    103. Posey DJ
    104. Poustka A
    105. Poustka F
    106. Prasad A
    107. Ragoussis J
    108. Renshaw K
    109. Rickaby J
    110. Roberts W
    111. Roeder K
    112. Roge B
    113. Rutter ML
    114. Bierut LJ
    115. Rice JP
    116. Salt J
    117. Sansom K
    118. Sato D
    119. Segurado R
    120. Sequeira AF
    121. Senman L
    122. Shah N
    123. Sheffield VC
    124. Soorya L
    125. Sousa I
    126. Stein O
    127. Sykes N
    128. Stoppioni V
    129. Strawbridge C
    130. Tancredi R
    131. Tansey K
    132. Thiruvahindrapduram B
    133. Thompson AP
    134. Thomson S
    135. Tryfon A
    136. Tsiantis J
    137. Van Engeland H
    138. Vincent JB
    139. Volkmar F
    140. Wallace S
    141. Wang K
    142. Wang Z
    143. Wassink TH
    144. Webber C
    145. Weksberg R
    146. Wing K
    147. Wittemeyer K
    148. Wood S
    149. Wu J
    150. Yaspan BL
    151. Zurawiecki D
    152. Zwaigenbaum L
    153. Buxbaum JD
    154. Cantor RM
    155. Cook EH
    156. Coon H
    157. Cuccaro ML
    158. Devlin B
    159. Ennis S
    160. Gallagher L
    161. Geschwind DH
    162. Gill M
    163. Haines JL
    164. Hallmayer J
    165. Miller J
    166. Monaco AP
    167. Nurnberger JI
    168. Paterson AD
    169. Pericak-Vance MA
    170. Schellenberg GD
    171. Szatmari P
    172. Vicente AM
    173. Vieland VJ
    174. Wijsman EM
    175. Scherer SW
    176. Sutcliffe JS
    177. Betancur C

    (2010) Functional impact of global rare copy number variation in autism spectrum disorders

    Nature 466:368–372.

    https://doi.org/10.1038/nature09146

    • PubMed
    • Google Scholar
49. 1. Polo JM
    2. Liu S
    3. Figueroa ME
    4. Kulalert W
    5. Eminli S
    6. Tan KY
    7. Apostolou E
    8. Stadtfeld M
    9. Li Y
    10. Shioda T
    11. Natesan S
    12. Wagers AJ
    13. Melnick A
    14. Evans T
    15. Hochedlinger K

    (2010) Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells

    Nature Biotechnology 28:848–855.

    https://doi.org/10.1038/nbt.1667

    • PubMed
    • Google Scholar
50. 1. Pucilowska J
    2. Vithayathil J
    3. Tavares EJ
    4. Kelly C
    5. Karlo JC
    6. Landreth GE

    (2015) The 16p11.2 deletion mouse model of autism exhibits altered cortical progenitor proliferation and brain cytoarchitecture linked to the ERK MAPK pathway

    Journal of Neuroscience 35:3190–3200.

    https://doi.org/10.1523/JNEUROSCI.4864-13.2015

    • PubMed
    • Google Scholar
51. 1. Pucilowska J
    2. Vithayathil J
    3. Pagani M
    4. Kelly C
    5. Karlo JC
    6. Robol C
    7. Morella I
    8. Gozzi A
    9. Brambilla R
    10. Landreth GE

    (2018) Pharmacological inhibition of ERK signaling rescues pathophysiology and behavioral phenotype associated with 16p11.2 Chromosomal Deletion in Mice

    The Journal of Neuroscience 38:6640–6652.

    https://doi.org/10.1523/JNEUROSCI.0515-17.2018

    • PubMed
    • Google Scholar
52. 1. Qureshi AY
    2. Mueller S
    3. Snyder AZ
    4. Mukherjee P
    5. Berman JI
    6. Roberts TP
    7. Nagarajan SS
    8. Spiro JE
    9. Chung WK
    10. Sherr EH
    11. Buckner RL
    12. Simons VIP Consortium

    (2014) Opposing brain differences in 16p11.2 deletion and duplication carriers

    Journal of Neuroscience 34:11199–11211.

    https://doi.org/10.1523/JNEUROSCI.1366-14.2014

    • PubMed
    • Google Scholar
53. 1. Ramalho-Carvalho J
    2. Graça I
    3. Gomez A
    4. Oliveira J
    5. Henrique R
    6. Esteller M
    7. Jerónimo C

    (2017) Downregulation of miR-130b~301b cluster is mediated by aberrant promoter methylation and impairs cellular senescence in prostate Cancer

    Journal of Hematology & Oncology 10:43.

    https://doi.org/10.1186/s13045-017-0415-1

    • PubMed
    • Google Scholar
54. 1. Ritchie ME
    2. Phipson B
    3. Wu D
    4. Hu Y
    5. Law CW
    6. Shi W
    7. Smyth GK

    (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies

    Nucleic Acids Research 43:e47.

    https://doi.org/10.1093/nar/gkv007

    • PubMed
    • Google Scholar
55. 1. Rosenfeld JA
    2. Coppinger J
    3. Bejjani BA
    4. Girirajan S
    5. Eichler EE
    6. Shaffer LG
    7. Ballif BC

    (2010) Speech delays and behavioral problems are the predominant features in individuals with developmental delays and 16p11.2 microdeletions and microduplications

    Journal of Neurodevelopmental Disorders 2:26–38.

    https://doi.org/10.1007/s11689-009-9037-4

    • PubMed
    • Google Scholar
56. 1. Rubenstein JL
    2. Shimamura K
    3. Martinez S
    4. Puelles L

    (1998) Regionalization of the prosencephalic neural plate

    Annual Review of Neuroscience 21:445–477.

    https://doi.org/10.1146/annurev.neuro.21.1.445

    • PubMed
    • Google Scholar
57. 1. Rucker JJ
    2. Breen G
    3. Pinto D
    4. Pedroso I
    5. Lewis CM
    6. Cohen-Woods S
    7. Uher R
    8. Schosser A
    9. Rivera M
    10. Aitchison KJ
    11. Craddock N
    12. Owen MJ
    13. Jones L
    14. Jones I
    15. Korszun A
    16. Muglia P
    17. Barnes MR
    18. Preisig M
    19. Mors O
    20. Gill M
    21. Maier W
    22. Rice J
    23. Rietschel M
    24. Holsboer F
    25. Farmer AE
    26. Craig IW
    27. Scherer SW
    28. McGuffin P

    (2013) Genome-wide association analysis of copy number variation in recurrent depressive disorder

    Molecular Psychiatry 18:183–189.

    https://doi.org/10.1038/mp.2011.144

    • PubMed
    • Google Scholar
58. 1. Schafer ST
    2. Paquola ACM
    3. Stern S
    4. Gosselin D
    5. Ku M
    6. Pena M
    7. Kuret TJM
    8. Liyanage M
    9. Mansour AA
    10. Jaeger BN
    11. Marchetto MC
    12. Glass CK
    13. Mertens J
    14. Gage FH

    (2019) Pathological priming causes developmental gene network heterochronicity in autistic subject-derived neurons

    Nature Neuroscience 22:243–255.

    https://doi.org/10.1038/s41593-018-0295-x

    • PubMed
    • Google Scholar
59. 1. Sebat J
    2. Lakshmi B
    3. Malhotra D
    4. Troge J
    5. Lese-Martin C
    6. Walsh T
    7. Yamrom B
    8. Yoon S
    9. Krasnitz A
    10. Kendall J
    11. Leotta A
    12. Pai D
    13. Zhang R
    14. Lee YH
    15. Hicks J
    16. Spence SJ
    17. Lee AT
    18. Puura K
    19. Lehtimäki T
    20. Ledbetter D
    21. Gregersen PK
    22. Bregman J
    23. Sutcliffe JS
    24. Jobanputra V
    25. Chung W
    26. Warburton D
    27. King MC
    28. Skuse D
    29. Geschwind DH
    30. Gilliam TC
    31. Ye K
    32. Wigler M

    (2007) Strong association of de novo copy number mutations with autism

    Science 316:445–449.

    https://doi.org/10.1126/science.1138659

    • PubMed
    • Google Scholar
60. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
61. 1. Sharov AA
    2. Masui S
    3. Sharova LV
    4. Piao Y
    5. Aiba K
    6. Matoba R
    7. Xin L
    8. Niwa H
    9. Ko MS

    (2008) Identification of Pou5f1, Sox2, and nanog downstream target genes with statistical confidence by applying a novel algorithm to time course microarray and genome-wide chromatin immunoprecipitation data

    BMC Genomics 9:269.

    https://doi.org/10.1186/1471-2164-9-269

    • PubMed
    • Google Scholar
62. 1. Shcheglovitov A
    2. Shcheglovitova O
    3. Yazawa M
    4. Portmann T
    5. Shu R
    6. Sebastiano V
    7. Krawisz A
    8. Froehlich W
    9. Bernstein JA
    10. Hallmayer JF
    11. Dolmetsch RE

    (2013) SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients

    Nature 503:267–271.

    https://doi.org/10.1038/nature12618

    • PubMed
    • Google Scholar
63. 1. Shen Y
    2. Dies KA
    3. Holm IA
    4. Bridgemohan C
    5. Sobeih MM
    6. Caronna EB
    7. Miller KJ
    8. Frazier JA
    9. Silverstein I
    10. Picker J
    11. Weissman L
    12. Raffalli P
    13. Jeste S
    14. Demmer LA
    15. Peters HK
    16. Brewster SJ
    17. Kowalczyk SJ
    18. Rosen-Sheidley B
    19. McGowan C
    20. Duda AW
    21. Lincoln SA
    22. Lowe KR
    23. Schonwald A
    24. Robbins M
    25. Hisama F
    26. Wolff R
    27. Becker R
    28. Nasir R
    29. Urion DK
    30. Milunsky JM
    31. Rappaport L
    32. Gusella JF
    33. Walsh CA
    34. Wu BL
    35. Miller DT
    36. Autism Consortium Clinical Genetics/DNA Diagnostics Collaboration

    (2010) Clinical genetic testing for patients with autism spectrum disorders

    Pediatrics 125:e727–e735.

    https://doi.org/10.1542/peds.2009-1684

    • PubMed
    • Google Scholar
64. 1. Shi Y
    2. Kirwan P
    3. Smith J
    4. Robinson HP
    5. Livesey FJ

    (2012) Human cerebral cortex development from pluripotent stem cells to functional excitatory synapses

    Nature Neuroscience 15:477–486.

    https://doi.org/10.1038/nn.3041

    • PubMed
    • Google Scholar
65. 1. Shinawi M
    2. Liu P
    3. Kang SH
    4. Shen J
    5. Belmont JW
    6. Scott DA
    7. Probst FJ
    8. Craigen WJ
    9. Graham BH
    10. Pursley A
    11. Clark G
    12. Lee J
    13. Proud M
    14. Stocco A
    15. Rodriguez DL
    16. Kozel BA
    17. Sparagana S
    18. Roeder ER
    19. McGrew SG
    20. Kurczynski TW
    21. Allison LJ
    22. Amato S
    23. Savage S
    24. Patel A
    25. Stankiewicz P
    26. Beaudet AL
    27. Cheung SW
    28. Lupski JR

    (2010) Recurrent reciprocal 16p11.2 rearrangements associated with global developmental delay, behavioural problems, dysmorphism, epilepsy, and abnormal head size

    Journal of Medical Genetics 47:332–341.

    https://doi.org/10.1136/jmg.2009.073015

    • PubMed
    • Google Scholar
66. 1. Simmons CS
    2. Sim JY
    3. Baechtold P
    4. Gonzalez A
    5. Chung C
    6. Borghi N
    7. Pruitt BL

    (2011) Integrated strain array for cellular mechanobiology studies

    Journal of Micromechanics and Microengineering 21:054016–054025.

    https://doi.org/10.1088/0960-1317/21/5/054016

    • Google Scholar
67. 1. Skafidas E
    2. Testa R
    3. Zantomio D
    4. Chana G
    5. Everall IP
    6. Pantelis C

    (2014) Predicting the diagnosis of autism spectrum disorder using gene pathway analysis

    Molecular Psychiatry 19:504–510.

    https://doi.org/10.1038/mp.2012.126

    • PubMed
    • Google Scholar
68. 1. Soneson C
    2. Love M
    3. Robinson M

    (2015) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 1; referees: 2 approved]

    F1000Research 4:1521.

    https://doi.org/10.12688/f1000research.7563.1

    • Google Scholar
69. 1. Spaethling JM
    2. Na YJ
    3. Lee J
    4. Ulyanova AV
    5. Baltuch GH
    6. Bell TJ
    7. Brem S
    8. Chen HI
    9. Dueck H
    10. Fisher SA
    11. Garcia MP
    12. Khaladkar M
    13. Kung DK
    14. Lucas TH
    15. O'Rourke DM
    16. Stefanik D
    17. Wang J
    18. Wolf JA
    19. Bartfai T
    20. Grady MS
    21. Sul JY
    22. Kim J
    23. Eberwine JH

    (2017) Primary cell culture of live neurosurgically resected aged adult human brain cells and single cell transcriptomics

    Cell Reports 18:791–803.

    https://doi.org/10.1016/j.celrep.2016.12.066

    • PubMed
    • Google Scholar
70. 1. Stefansson H
    2. Meyer-Lindenberg A
    3. Steinberg S
    4. Magnusdottir B
    5. Morgen K
    6. Arnarsdottir S
    7. Bjornsdottir G
    8. Walters GB
    9. Jonsdottir GA
    10. Doyle OM
    11. Tost H
    12. Grimm O
    13. Kristjansdottir S
    14. Snorrason H
    15. Davidsdottir SR
    16. Gudmundsson LJ
    17. Jonsson GF
    18. Stefansdottir B
    19. Helgadottir I
    20. Haraldsson M
    21. Jonsdottir B
    22. Thygesen JH
    23. Schwarz AJ
    24. Didriksen M
    25. Stensbøl TB
    26. Brammer M
    27. Kapur S
    28. Halldorsson JG
    29. Hreidarsson S
    30. Saemundsen E
    31. Sigurdsson E
    32. Stefansson K

    (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls

    Nature 505:361–366.

    https://doi.org/10.1038/nature12818

    • PubMed
    • Google Scholar
71. 1. Tai DJ
    2. Ragavendran A
    3. Manavalan P
    4. Stortchevoi A
    5. Seabra CM
    6. Erdin S
    7. Collins RL
    8. Blumenthal I
    9. Chen X
    10. Shen Y
    11. Sahin M
    12. Zhang C
    13. Lee C
    14. Gusella JF
    15. Talkowski ME

    (2016) Engineering microdeletions and microduplications by targeting segmental duplications with CRISPR

    Nature Neuroscience 19:517–522.

    https://doi.org/10.1038/nn.4235

    • PubMed
    • Google Scholar
72. 1. Takahashi K
    2. Tanabe K
    3. Ohnuki M
    4. Narita M
    5. Ichisaka T
    6. Tomoda K
    7. Yamanaka S

    (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors

    Cell 131:861–872.

    https://doi.org/10.1016/j.cell.2007.11.019

    • PubMed
    • Google Scholar
73. 1. Urbach A
    2. Bar-Nur O
    3. Daley GQ
    4. Benvenisty N

    (2010) Differential modeling of fragile X syndrome by human embryonic stem cells and induced pluripotent stem cells

    Cell Stem Cell 6:407–411.

    https://doi.org/10.1016/j.stem.2010.04.005

    • Google Scholar
74. 1. Vaccarino FM
    2. Grigorenko EL
    3. Smith KM
    4. Stevens HE

    (2009) Regulation of cerebral cortical size and neuron number by fibroblast growth factors: implications for autism

    Journal of Autism and Developmental Disorders 39:511–520.

    https://doi.org/10.1007/s10803-008-0653-8

    • PubMed
    • Google Scholar
75. 1. Varvagiannis K
    2. Papoulidis I
    3. Koromila T
    4. Kefalas K
    5. Ziegler M
    6. Liehr T
    7. Petersen MB
    8. Gyftodimou Y
    9. Manolakos E

    (2014) De novo 393 kb microdeletion of 7p11.2 characterized by aCGH in a boy with psychomotor retardation and dysmorphic features

    Meta Gene 2:274–282.

    https://doi.org/10.1016/j.mgene.2014.03.004

    • PubMed
    • Google Scholar
76. 1. Walsh T
    2. McClellan JM
    3. McCarthy SE
    4. Addington AM
    5. Pierce SB
    6. Cooper GM
    7. Nord AS
    8. Kusenda M
    9. Malhotra D
    10. Bhandari A
    11. Stray SM
    12. Rippey CF
    13. Roccanova P
    14. Makarov V
    15. Lakshmi B
    16. Findling RL
    17. Sikich L
    18. Stromberg T
    19. Merriman B
    20. Gogtay N
    21. Butler P
    22. Eckstrand K
    23. Noory L
    24. Gochman P
    25. Long R
    26. Chen Z
    27. Davis S
    28. Baker C
    29. Eichler EE
    30. Meltzer PS
    31. Nelson SF
    32. Singleton AB
    33. Lee MK
    34. Rapoport JL
    35. King MC
    36. Sebat J

    (2008) Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia

    Science 320:539–543.

    https://doi.org/10.1126/science.1155174

    • PubMed
    • Google Scholar
77. 1. Walsh KM
    2. Bracken MB

    (2011) Copy number variation in the dosage-sensitive 16p11.2 interval accounts for only a small proportion of autism incidence: a systematic review and meta-analysis

    Genetics in Medicine 13:377–384.

    https://doi.org/10.1097/GIM.0b013e3182076c0c

    • PubMed
    • Google Scholar
78. 1. Walters RG
    2. Jacquemont S
    3. Valsesia A
    4. de Smith AJ
    5. Martinet D
    6. Andersson J
    7. Falchi M
    8. Chen F
    9. Andrieux J
    10. Lobbens S
    11. Delobel B
    12. Stutzmann F
    13. El-Sayed Moustafa JS
    14. Chèvre JC
    15. Lecoeur C
    16. Vatin V
    17. Bouquillon S
    18. Buxton JL
    19. Boute O
    20. Holder-Espinasse M
    21. Cuisset JM
    22. Lemaitre MP
    23. Ambresin AE
    24. Brioschi A
    25. Gaillard M
    26. Giusti V
    27. Fellmann F
    28. Ferrarini A
    29. Hadjikhani N
    30. Campion D
    31. Guilmatre A
    32. Goldenberg A
    33. Calmels N
    34. Mandel JL
    35. Le Caignec C
    36. David A
    37. Isidor B
    38. Cordier MP
    39. Dupuis-Girod S
    40. Labalme A
    41. Sanlaville D
    42. Béri-Dexheimer M
    43. Jonveaux P
    44. Leheup B
    45. Ounap K
    46. Bochukova EG
    47. Henning E
    48. Keogh J
    49. Ellis RJ
    50. Macdermot KD
    51. van Haelst MM
    52. Vincent-Delorme C
    53. Plessis G
    54. Touraine R
    55. Philippe A
    56. Malan V
    57. Mathieu-Dramard M
    58. Chiesa J
    59. Blaumeiser B
    60. Kooy RF
    61. Caiazzo R
    62. Pigeyre M
    63. Balkau B
    64. Sladek R
    65. Bergmann S
    66. Mooser V
    67. Waterworth D
    68. Reymond A
    69. Vollenweider P
    70. Waeber G
    71. Kurg A
    72. Palta P
    73. Esko T
    74. Metspalu A
    75. Nelis M
    76. Elliott P
    77. Hartikainen AL
    78. McCarthy MI
    79. Peltonen L
    80. Carlsson L
    81. Jacobson P
    82. Sjöström L
    83. Huang N
    84. Hurles ME
    85. O'Rahilly S
    86. Farooqi IS
    87. Männik K
    88. Jarvelin MR
    89. Pattou F
    90. Meyre D
    91. Walley AJ
    92. Coin LJ
    93. Blakemore AI
    94. Froguel P
    95. Beckmann JS

    (2010) A new highly penetrant form of obesity due to deletions on chromosome 16p11.2

    Nature 463:671–675.

    https://doi.org/10.1038/nature08727

    • PubMed
    • Google Scholar
79. 1. Wang Z
    2. Oron E
    3. Nelson B
    4. Razis S
    5. Ivanova N

    (2012) Distinct lineage specification roles for NANOG, OCT4, and SOX2 in human embryonic stem cells

    Cell Stem Cell 10:440–454.

    https://doi.org/10.1016/j.stem.2012.02.016

    • PubMed
    • Google Scholar
80. 1. Weiss LA
    2. Shen Y
    3. Korn JM
    4. Arking DE
    5. Miller DT
    6. Fossdal R
    7. Saemundsen E
    8. Stefansson H
    9. Ferreira MA
    10. Green T
    11. Platt OS
    12. Ruderfer DM
    13. Walsh CA
    14. Altshuler D
    15. Chakravarti A
    16. Tanzi RE
    17. Stefansson K
    18. Santangelo SL
    19. Gusella JF
    20. Sklar P
    21. Wu BL
    22. Daly MJ
    23. Autism Consortium

    (2008) Association between microdeletion and microduplication at 16p11.2 and autism

    New England Journal of Medicine 358:667–675.

    https://doi.org/10.1056/NEJMoa075974

    • PubMed
    • Google Scholar
81. 1. Wilson PG
    2. Stice SS

    (2006) Development and differentiation of neural rosettes derived from human embryonic stem cells

    Stem Cell Reviews 2:67–77.

    https://doi.org/10.1007/s12015-006-0011-1

    • PubMed
    • Google Scholar
82. 1. Winn VD
    2. Haimov-Kochman R
    3. Paquet AC
    4. Yang YJ
    5. Madhusudhan MS
    6. Gormley M
    7. Feng KT
    8. Bernlohr DA
    9. McDonagh S
    10. Pereira L
    11. Sali A
    12. Fisher SJ

    (2007) Gene expression profiling of the human maternal-fetal interface reveals dramatic changes between midgestation and term

    Endocrinology 148:1059–1079.

    https://doi.org/10.1210/en.2006-0683

    • PubMed
    • Google Scholar
83. 1. Yoon KJ
    2. Nguyen HN
    3. Ursini G
    4. Zhang F
    5. Kim NS
    6. Wen Z
    7. Makri G
    8. Nauen D
    9. Shin JH
    10. Park Y
    11. Chung R
    12. Pekle E
    13. Zhang C
    14. Towe M
    15. Hussaini SM
    16. Lee Y
    17. Rujescu D
    18. St Clair D
    19. Kleinman JE
    20. Hyde TM
    21. Krauss G
    22. Christian KM
    23. Rapoport JL
    24. Weinberger DR
    25. Song H
    26. Ming GL

    (2014) Modeling a genetic risk for schizophrenia in iPSCs and mice reveals neural stem cell deficits associated with adherens junctions and polarity

    Cell Stem Cell 15:79–91.

    https://doi.org/10.1016/j.stem.2014.05.003

    • PubMed
    • Google Scholar
84. 1. Yu J
    2. Vodyanik MA
    3. Smuga-Otto K
    4. Antosiewicz-Bourget J
    5. Frane JL
    6. Tian S
    7. Nie J
    8. Jonsdottir GA
    9. Ruotti V
    10. Stewart R
    11. Slukvin II
    12. Thomson JA

    (2007) Induced pluripotent stem cell lines derived from human somatic cells

    Science 318:1917–1920.

    https://doi.org/10.1126/science.1151526

    • PubMed
    • Google Scholar
85. 1. Zahir F
    2. Firth HV
    3. Baross A
    4. Delaney AD
    5. Eydoux P
    6. Gibson WT
    7. Langlois S
    8. Martin H
    9. Willatt L
    10. Marra MA
    11. Friedman JM

    (2007) Novel deletions of 14q11.2 associated with developmental delay, cognitive impairment and similar minor anomalies in three children

    Journal of Medical Genetics 44:556–561.

    https://doi.org/10.1136/jmg.2007.050823

    • PubMed
    • Google Scholar
86. 1. Zhang JF
    2. He ML
    3. Fu WM
    4. Wang H
    5. Chen LZ
    6. Zhu X
    7. Chen Y
    8. Xie D
    9. Lai P
    10. Chen G
    11. Lu G
    12. Lin MC
    13. Kung HF

    (2011) Primate-specific microRNA-637 inhibits tumorigenesis in hepatocellular carcinoma by disrupting signal transducer and activator of transcription 3 signaling

    Hepatology 54:2137–2148.

    https://doi.org/10.1002/hep.24595

    • PubMed
    • Google Scholar
87. 1. Zhang Y
    2. Guo B
    3. Hui Q
    4. Li W
    5. Chang P
    6. Tao K

    (2018) Downregulation of miR–637 promotes proliferation and metastasis by targeting Smad3 in keloids

    Molecular Medicine Reports 18:1628–1636.

    https://doi.org/10.3892/mmr.2018.9099

    • PubMed
    • Google Scholar
88. 1. Zhao X
    2. Kuja-Panula J
    3. Sundvik M
    4. Chen YC
    5. Aho V
    6. Peltola MA
    7. Porkka-Heiskanen T
    8. Panula P
    9. Rauvala H

    (2014) Amigo adhesion protein regulates development of neural circuits in zebrafish brain

    Journal of Biological Chemistry 289:19958–19975.

    https://doi.org/10.1074/jbc.M113.545582

    • PubMed
    • Google Scholar
89. 1. Zhu H
    2. Shang D
    3. Sun M
    4. Choi S
    5. Liu Q
    6. Hao J
    7. Figuera LE
    8. Zhang F
    9. Choy KW
    10. Ao Y
    11. Liu Y
    12. Zhang XL
    13. Yue F
    14. Wang MR
    15. Jin L
    16. Patel PI
    17. Jing T
    18. Zhang X

    (2011) X-linked congenital hypertrichosis syndrome is associated with interchromosomal insertions mediated by a human-specific palindrome near SOX3

    The American Journal of Human Genetics 88:819–826.

    https://doi.org/10.1016/j.ajhg.2011.05.004

    • PubMed
    • Google Scholar
90. 1. Zufferey F
    2. Sherr EH
    3. Beckmann ND
    4. Hanson E
    5. Maillard AM
    6. Hippolyte L
    7. Macé A
    8. Ferrari C
    9. Kutalik Z
    10. Andrieux J
    11. Aylward E
    12. Barker M
    13. Bernier R
    14. Bouquillon S
    15. Conus P
    16. Delobel B
    17. Faucett WA
    18. Goin-Kochel RP
    19. Grant E
    20. Harewood L
    21. Hunter JV
    22. Lebon S
    23. Ledbetter DH
    24. Martin CL
    25. Männik K
    26. Martinet D
    27. Mukherjee P
    28. Ramocki MB
    29. Spence SJ
    30. Steinman KJ
    31. Tjernagel J
    32. Spiro JE
    33. Reymond A
    34. Beckmann JS
    35. Chung WK
    36. Jacquemont S
    37. Simons VIP Consortium
    38. 16p11.2 European Consortium

    (2012) A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders

    Journal of Medical Genetics 49:660–668.

    https://doi.org/10.1136/jmedgenet-2012-101203

    • PubMed
    • Google Scholar

Author details

1. Julien G Roth

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Kristin L Muench

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7560-3258

2. Kristin L Muench

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Julien G Roth

   Competing interests

   No competing interests declared

3. Aditya Asokan

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Victoria M Mallett

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Validation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Hui Gai

   1. Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
   2. Department of Neurobiology, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Yogendra Verma

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

7. Stephen Weber

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

8. Carol Charlton

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

9. Jonas L Fowler

   Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

10. Kyle M Loh

    Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

    Contribution

    Formal analysis, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

11. Ricardo E Dolmetsch

    Department of Neurobiology, Stanford University School of Medicine, Stanford, United States

    Present address

    Department of Neuroscience, Novartis Institutes for BioMedical Research, Cambridge, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2738-8338

12. Theo D Palmer

    Department of Neurosurgery and The Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    tpalmer@stanford.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6266-1862

• 3,259

  views

• 346

  downloads

• 31

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.