Fernando S Goes1, Mehdi Pirooznia1, Jennifer S Parla2, Melissa Kramer2, Elena Ghiban2, Senem Mavruk2, Yun-Ching Chen3, Eric T Monson4, Virginia L Willour4, Rachel Karchin3, Matthew Flickinger5, Adam E Locke5, Shawn E Levy6, Laura J Scott5, Michael Boehnke5, Eli Stahl7, Jennifer L Moran8, Christina M Hultman9, Mikael Landén10, Shaun M Purcell11, Pamela Sklar12, Peter P Zandi13, W Richard McCombie2, James B Potash4. 1. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland. 2. Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York. 3. Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland4Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland. 4. Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City. 5. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor. 6. HudsonAlpha Institute of Biotechnology, Huntsville, Alabama. 7. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York9Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York. 8. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts. 9. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 10. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden12Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden. 11. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York9Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York10Stanley Center for Psychiat. 12. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York9Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York15Friedman Brain Institute, I. 13. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Abstract
IMPORTANCE: Complex disorders, such as bipolar disorder (BD), likely result from the influence of both common and rare susceptibility alleles. While common variation has been widely studied, rare variant discovery has only recently become feasible with next-generation sequencing. OBJECTIVE: To utilize a combined family-based and case-control approach to exome sequencing in BD using multiplex families as an initial discovery strategy, followed by association testing in a large case-control meta-analysis. DESIGN, SETTING, AND PARTICIPANTS: We performed exome sequencing of 36 affected members with BD from 8 multiplex families and tested rare, segregating variants in 3 independent case-control samples consisting of 3541 BD cases and 4774 controls. MAIN OUTCOMES AND MEASURES: We used penalized logistic regression and 1-sided gene-burden analyses to test for association of rare, segregating damaging variants with BD. Permutation-based analyses were performed to test for overall enrichment with previously identified gene sets. RESULTS: We found 84 rare (frequency <1%), segregating variants that were bioinformatically predicted to be damaging. These variants were found in 82 genes that were enriched for gene sets previously identified in de novo studies of autism (19 observed vs. 10.9 expected, P = .0066) and schizophrenia (11 observed vs. 5.1 expected, P = .0062) and for targets of the fragile X mental retardation protein (FMRP) pathway (10 observed vs. 4.4 expected, P = .0076). The case-control meta-analyses yielded 19 genes that were nominally associated with BD based either on individual variants or a gene-burden approach. Although no gene was individually significant after correction for multiple testing, this group of genes continued to show evidence for significant enrichment of de novo autism genes (6 observed vs 2.6 expected, P = .028). CONCLUSIONS AND RELEVANCE: Our results are consistent with the presence of prominent locus and allelic heterogeneity in BD and suggest that very large samples will be required to definitively identify individual rare variants or genes conferring risk for this disorder. However, we also identify significant associations with gene sets composed of previously discovered de novo variants in autism and schizophrenia, as well as targets of the FRMP pathway, providing preliminary support for the overlap of potential autism and schizophrenia risk genes with rare, segregating variants in families with BD.
IMPORTANCE: Complex disorders, such as bipolar disorder (BD), likely result from the influence of both common and rare susceptibility alleles. While common variation has been widely studied, rare variant discovery has only recently become feasible with next-generation sequencing. OBJECTIVE: To utilize a combined family-based and case-control approach to exome sequencing in BD using multiplex families as an initial discovery strategy, followed by association testing in a large case-control meta-analysis. DESIGN, SETTING, AND PARTICIPANTS: We performed exome sequencing of 36 affected members with BD from 8 multiplex families and tested rare, segregating variants in 3 independent case-control samples consisting of 3541 BD cases and 4774 controls. MAIN OUTCOMES AND MEASURES: We used penalized logistic regression and 1-sided gene-burden analyses to test for association of rare, segregating damaging variants with BD. Permutation-based analyses were performed to test for overall enrichment with previously identified gene sets. RESULTS: We found 84 rare (frequency <1%), segregating variants that were bioinformatically predicted to be damaging. These variants were found in 82 genes that were enriched for gene sets previously identified in de novo studies of autism (19 observed vs. 10.9 expected, P = .0066) and schizophrenia (11 observed vs. 5.1 expected, P = .0062) and for targets of the fragile X mental retardation protein (FMRP) pathway (10 observed vs. 4.4 expected, P = .0076). The case-control meta-analyses yielded 19 genes that were nominally associated with BD based either on individual variants or a gene-burden approach. Although no gene was individually significant after correction for multiple testing, this group of genes continued to show evidence for significant enrichment of de novo autism genes (6 observed vs 2.6 expected, P = .028). CONCLUSIONS AND RELEVANCE: Our results are consistent with the presence of prominent locus and allelic heterogeneity in BD and suggest that very large samples will be required to definitively identify individual rare variants or genes conferring risk for this disorder. However, we also identify significant associations with gene sets composed of previously discovered de novo variants in autism and schizophrenia, as well as targets of the FRMP pathway, providing preliminary support for the overlap of potential autism and schizophrenia risk genes with rare, segregating variants in families with BD.
Authors: Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich Journal: Nat Genet Date: 2006-07-23 Impact factor: 38.330
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