PERSPECTIVE Cognitive genomics: Linking genes to behavior in the human brain Genevieve Konopka Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390-9111, USA ABSTRACT Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. However, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic Downloaded from http://direct.mit.edu/netn/article-pdf/1/1/3/1091843/netn_a_00003.pdf by guest on 30 September 2021 localization as well as tissue-specific epigenetics and expression. The use of brain-derived an open access journal expression datasets could build upon the foundation of these initial genetic insights and yield genes and molecular pathways for testing new hypotheses regarding the molecular bases of human brain development, cognition, and disease. Thus, coupling these human brain gene expression data with measurements of brain activity may provide genes with critical roles in brain function. However, these brain gene expression datasets have their own set of caveats, most notably a reliance on postmortem tissue. In this perspective, I summarize and examine the progress that has been made in this realm to date, and discuss the various frontiers remaining, such as the inclusion of cell-type-specific information, additional physiological measurements, and genomic data from patient cohorts. Progress in understanding the inner workings of the brain has come a long way from the preneu- roscience era of phrenology, when we were limited to conjectures about human behavior Citation: Konopka G. (2017). Cognitive genomics: Linking genes to behavior based on the shape of the skull. Over the past quarter century, technological breakthroughs in the human brain. Network have given us the ability to noninvasively peer into the operations of the human brain during Neuroscience, 1(1), 3–13. doi:10.1162/netn_a_00003 behavior, by means of a host of imaging and physiological techniques. Functional imaging DOI: has provided elegant maps of human activity at rest, as well as during any number of cogni- http://doi.org/10.1162/netn_a_00003 tive tasks. By coupling these results with neuroanatomical and structural imaging, function and Supporting Information: structure can be married to identify brain regions that work in concert to execute specific func- tions. Furthermore, when such approaches are carried out in patients with neuropsychiatric Received: 16 September 2016 disorders, the regional brain activity relevant to cognitive phenotypes can be uncovered. Accepted: 22 November 2016 Competing Interests: The author has Genetic Contributions to Cognition declared that no competing interests exist. Determining the relative contribution of genes to cognition has been a longstanding interest Corresponding Author: in the field of genetic research. Recent inquiries have focused on unlocking the genetic and Genevieve Konopka molecular mechanisms underlying human brain activity (see the discussion and references [email protected] in Medland, Jahanshad, Neale, & Thompson, 2014,andThompson, Ge, Glahn, Jahanshad, Handling Editor: & Nichols, 2013). Key insights have been made, such as the heritability of functional brain Olaf Sporns networks (Fornito et al., 2011; Fu et al., 2015; Glahn et al., 2010; Yang et al., 2016)andthe correlation of genetic variation in altered functional connectivity in specific diseases Copyright: © 2017 or phenotypes (see the references in Gaiteri, Mostafavi, Honey, De Jager, & Bennett, 2016; Massachusetts Institute of Technology Hernandez, Rudie, Green, Bookheimer, & Dapretto, 2015). As such, these advances could Published under a Creative Commons Attribution 4.0 International have profound implications for how we diagnose and treat such disorders (see the discus- (CC BY 4.0) license sion and references in Matthews & Hampshire, 2016). Furthermore, genome-wide association The MIT Press Cognitive genomics: Linking genes to behavior in the human brain studies have identified specific genomic loci that are significantly associated with subcorti- cal brain structures (Hibar et al., 2015); with educational attainment as a proxy for cognition in general (Okbay, Beauchamp, et al., 2016); with personality traits such as subjective well-being, depressive symptoms, and neuroticism (Okbay, Baselmans, et al., 2016); and with cognitive disorders such as schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). These molecular and genetic insights provide a baseline for ultimately pinpointing drug targets in a number of cognitive disorders, as well as deepening our understanding of both the developmental and evolutionary origins of human cognition. Thus, further investigations into the molecular mechanisms underlying human brain activity are needed to bridge the gap between genes and behavior. Quantifying Gene Expression in the Human Brain Downloaded from http://direct.mit.edu/netn/article-pdf/1/1/3/1091843/netn_a_00003.pdf by guest on 30 September 2021 The genome revolution, followed rapidly by implementation of the high-throughput techno- Microarrays: logies of microarrays and next-generation sequencing, has permitted investigations of human Technology that uses preselected brain gene expression in a spatiotemporal manner, by quantifying RNA amounts at a genome- oligonucleotides to quantify RNA wide level (e.g., Kang et al., 2011). The analysis of gene transcription across the entire human amounts on a genome-wide basis. brain allows for distinguishing the genes expressed in specific brain regions during a given (See Box 1.) developmental time period, and thus results in a quantitative measurement of gene expres- sion levels. These datasets are different from the genetic associations mentioned above, in which changes at the DNA level are identified. Such genetic variation might be within re- gions of DNA of unknown functional significance (e.g., do the variants affect gene expres- sion?) and might also interact with unknown epigenetic markers in a tissue-specific manner, leading to further ambiguity about the resultant gene expression. Surveying the vast transcrip- tional landscape of the developing and adult human brain has been facilitated by the work of the Allen Institute for Brain Science in collaboration with a number of academic groups, to develop several reference gene expression atlases of the human brain, by using a combi- In situ hybridization: nation of in situ hybridization, microarrays, and RNA sequencing (RNA-seq) throughout the Probe-based technology used to human lifespan (Hawrylycz et al., 2015; Hawrylycz et al., 2012; Miller et al., 2014; Zeng et al., assess the spatial expression of 2012;seeBox 1). One of the caveats to these assessments of human brain gene ex- specific RNA molecules (See Box 1.) pression is that they are naturally limited to postmortem tissue. Although careful statistical RNA sequencing (RNA-seq): analyses take into consideration experimental covariates such as postmortem interval and RNA Unbiased quantification of RNA quality, there is always the possibility that patterns of gene expression in behaving molecules on a genome-wide basis. individuals cannot be fully recapitulated in postmortem tissue. Nevertheless, these assess- (See Box 1.) ments provide critical insights into human brain gene expression patterns, based on develop- mental stage (Kang et al., 2011; Miller et al., 2014), gender (Kang et al., 2011), hemispheric lateralization (or lack thereof; Hawrylycz et al., 2012; Johnson et al., 2009; Pletikos et al., 2014), and human-specific evolution (Bakken et al., 2016; Bernard et al., 2012). These obser- vations can then be compared with disease-relevant datasets. For example, genetic data from autism spectrum disorder (ASD) patients were integrated with the BrainSpan gene expression dataset (www.brainspan.org) to identify ASD-relevant coexpression networks (Parikshak et al., 2013; Willsey et al., 2013). Furthermore, genomic profiling of disease tissue itself can be insightful, as has been the case for ASD, where such profiling has identified differentially ex- pressed networks of mRNAs and microRNAs in ASD brains as compared to matched con- trols (Voineagu et al., 2011; Y. E. Wu, Parikshak, Belgard, & Geschwind, 2016). Together, these studies of human brain gene expression have facilitated the prioritization of specific genes and molecular pathways for further in-depth analyses. However, such follow-up studies are largely limited to animal models, and there appears to be a large divide between what can be observed at the gene expression level in brain tissue and at the behavioral level in humans. Network Neuroscience 4 Cognitive genomics: Linking genes to behavior in the human brain Box 1. Descriptions of Gene Expression Detection Methods In situ hybridization is carried out by hybridizing gene-specific RNA probes to tissue samples. This method provides spatial resolution of the mRNA expression of individual genes. High- quality tissue specimens and highly specific probes to each gene are required for accurate detection. A major advantage of in situ hybridization is the ability to couple it with immuno- histochemistry to make mRNA and protein correlations. However, the quantification of in situ hybridization is challenging, depending on the method used. The greatest advantages of in situ hybridization over the other technologies discussed below are its spatial resolution and ability to detect expression