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Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.
We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.
We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).
Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
This chapter reviews the family studies supporting the role of genetics and recent molecular genetic results. Mapping studies using linkage and association methods have had modest success to date despite difficulties in replication between studies. Linkage studies have shown the best support for chromosomal regions: 6q, 8q, 9p, 13q, 14q, and 22q. Several candidate genes first identified in studies of schizophrenia have shown reproducible association in bipolar disorder. Genome-wide association studies (GWAS) have been successful in identifying a few genes with small effects on risk. The data overall suggest a high level of both genic and allelic heterogeneity, as well as, a complex mode of inheritance. The coming availability of economical whole genome sequencing promises availability of complete genomic information. This, and large samples now being collected, may provide the datasets necessary to unravel the genetic complexities of this illness.
Louis A. Roussos, Senior Psychometrician, Measured Progress,
Louis V. DiBello, Research Professor of Psychology, University of Illinois at Chicago,
William Stout, Professor of Statistics, University of Illinois at Urbana-Champaign,
Sarah M. Hartz, Resident, Department of Psychiatry, University of Iowa,
Robert A. Henson, Assistant Professor of Education Research and Methodology, University of North Carolina at Greensboro,
Jonathan L. Templin, Assistant Professor of Psychology, University of Kansas
There is a long history of calls for combining cognitive science and psychometrics (Cronbach, 1975; Snow & Lohman, 1989). The U.S. standards movement, begun more than 20 years ago (McKnight et al., 1987; National Council of Teachers of Mathematics, 1989), sought to articulate public standards for learning that would define and promote successful performance by all students; establish a common base for curriculum development and instructional practice; and provide a foundation for measuring progress for students, teachers and programs. The standards movement provided the first widespread call for assessment systems that directly support learning. For success, such systems must satisfy a number of conditions having to do with cognitive-science–based design, psychometrics, and implementation. This chapter focuses on the psychometric aspects of one particular system that builds on a carefully designed test and a user-selected set of relevant skills measured by that test to assess student mastery of each of the chosen skills. This type of test-based skills level assessment is called skills diagnosis. The system that the chapter describes in detail is called the Fusion Model system.
This chapter focuses on the statistical and psychometric aspects of the Fusion Model system, with skills diagnosis researchers and practitioners in mind who may be interested in working with this system. We view the statistical and psychometric aspects as situated within a comprehensive framework for diagnostic assessment test design and implementation.
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