Publications by authors named "Francis J McMahon"

158 Publications

HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders.

Sci Rep 2021 Sep 8;11(1):17823. Epub 2021 Sep 8.

Department of Psychiatry & Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan.

Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.
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http://dx.doi.org/10.1038/s41598-021-97140-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426488PMC
September 2021

Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.

J Am Stat Assoc 2021 28;116(534):531-545. Epub 2020 Jul 28.

Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD.

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: and . Using rare variants, we find suggestive signals in four genes: , , , and . Intriguingly, is down-regulated in AMD aqueous humor, and deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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http://dx.doi.org/10.1080/01621459.2020.1799809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315575PMC
July 2020

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses: A Multisite Study of Multiplex Pedigrees.

Biol Psychiatry 2021 Sep 17;90(6):373-384. Epub 2021 Mar 17.

Department of Psychiatry, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut.

Background: Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups.

Methods: Data were from 4 samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed.

Results: Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average endophenotype ranking value [ERV] across samples from a random-effects meta-analysis = 0.32), followed by verbal memory (ERV = 0.24), executive function (ERV = 0.22), and working memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with processing speed (ERV = 0.05) and verbal memory (ERV = 0.11), but these were confined to select samples. Major depressive disorder was characterized by enhanced working and face memory performance, as reflected in significant genetic overlap in 2 samples.

Conclusions: There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tends to be specific to ascertainment strategy, ethnicity, and cognitive test battery.
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http://dx.doi.org/10.1016/j.biopsych.2021.03.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403107PMC
September 2021

Multiple dimensions of stress vs. genetic effects on depression.

Transl Psychiatry 2021 04 29;11(1):254. Epub 2021 Apr 29.

Maryland Psychiatric Research Center, Department of Psychiatry, , University of Maryland School of Medicine, Baltimore, MD, 21201, USA.

Many psychiatric disorders including depression involve complex interactions of genetics and environmental stressors. Environmental influence is challenging to measure objectively and account for in genetic studies because the necessary large population samples in these studies involve individuals with varying cultures and life experiences, clouding genetic findings. In a unique population with relative sociocultural homogeneity and a narrower range of types of stress experiences, we quantitatively assessed multiple stress dimensions and measured their potential influence in biasing the heritability estimate of depression. We quantified depressive symptoms, major lifetime stressors, current perceived stress, and a culturally specific community stress measure in individuals with depression-related diagnoses and community controls in Old Order Amish and Mennonite populations. Results showed that lifetime stressors measured by lifetime stressor inventory (R= 0.06, p = 2 × 10) and current stress measured by Perceived Stress Scale (R= 0.13, p < 1 × 10) were both associated with current depressive symptoms quantified by Beck Depression Inventory in community controls, but current stress was the only measure associated with current depressive symptoms in individuals with a depression diagnosis, and to a greater degree (R = 0.41, p < 1 × 10). A novel, culturally specific community stress measure demonstrated internal reliability and was associated with current stress but was not significantly related to depression. Heritability (h) for depression diagnosis (0.46 ± 0.14) and quantitative depression severity as measured by Beck Depression Inventory (0.45 ± 0.12) were significant, but h for depression diagnosis decreased to 0.25 ± 0.14 once stressors were accounted for in the model. This quantifies and demonstrates the importance of accounting for environmental influence in reducing phenotypic heterogeneity of depression and improving the power and replicability of genetic association findings that can be better translated to patient groups.
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http://dx.doi.org/10.1038/s41398-021-01369-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085217PMC
April 2021

Genetic versus stress and mood determinants of sleep in the Amish.

Am J Med Genet B Neuropsychiatr Genet 2021 03 1;186(2):113-121. Epub 2021 Mar 1.

Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Sleep is essential to the human brain and is regulated by genetics with many features conserved across species. Sleep is also influenced by health and environmental factors; identifying replicable genetic variants contributing to sleep may require accounting for these factors. We examined how stress and mood disorder contribute to sleep and impact its heritability. Our sample included 326 Amish/Mennonite individuals with a lifestyle with limited technological interferences with sleep. Sleep measures included Pittsburgh Sleep Quality Index (PSQI), bedtime, wake time, and time to sleep onset. Current stress level, cumulative life stressors, and mood disorder were also evaluated. We estimated the heritability of sleep features and examined the impact of current stress, lifetime stress, mood diagnosis on sleep quality. The results showed current stress, lifetime stress, and mood disorder were independently associated with PSQI score (p < .05). Heritability of PSQI was low (0-0.23) before and after accounting for stress and mood. Bedtime, wake time, and minutes to sleep time did show significant heritability at 0.44, 0.42, and 0.29. However, after adjusting for shared environment, only heritability of wake time remained significant. Sleep is affected by environmental stress and mental health factors even in a society with limited technological interference with sleep. Wake time may be a more biological marker of sleep as compared to the evening measures which are more influenced by other household members. Accounting for nongenetic and partially genetic determinants of sleep particularly stress and mood disorder is likely important for improving the precision of genetic studies of sleep.
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http://dx.doi.org/10.1002/ajmg.b.32840DOI Listing
March 2021

Deep transcriptome sequencing of subgenual anterior cingulate cortex reveals cross-diagnostic and diagnosis-specific RNA expression changes in major psychiatric disorders.

Neuropsychopharmacology 2021 06 8;46(7):1364-1372. Epub 2021 Feb 8.

Human Genetics Branch, National Institute of Mental Health Intramural Research Program, NIH, DHHS, Bethesda, MD, USA.

Despite strong evidence of heritability and growing discovery of genetic markers for major mental illness, little is known about how gene expression in the brain differs across psychiatric diagnoses, or how known genetic risk factors shape these differences. Here we investigate expressed genes and gene transcripts in postmortem subgenual anterior cingulate cortex (sgACC), a key component of limbic circuits linked to mental illness. RNA obtained postmortem from 200 donors diagnosed with bipolar disorder, schizophrenia, major depression, or no psychiatric disorder was deeply sequenced to quantify expression of over 85,000 gene transcripts, many of which were rare. Case-control comparisons detected modest expression differences that were correlated across disorders. Case-case comparisons revealed greater expression differences, with some transcripts showing opposing patterns of expression between diagnostic groups, relative to controls. The ~250 rare transcripts that were differentially-expressed in one or more disorder groups were enriched for genes involved in synapse formation, cell junctions, and heterotrimeric G-protein complexes. Common genetic variants were associated with transcript expression (eQTL) or relative abundance of alternatively spliced transcripts (sQTL). Common genetic variants previously associated with disease risk were especially enriched for sQTLs, which together accounted for disproportionate fractions of diagnosis-specific heritability. Genetic risk factors that shape the brain transcriptome may contribute to diagnostic differences between broad classes of mental illness.
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http://dx.doi.org/10.1038/s41386-020-00949-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134494PMC
June 2021

Prediction of lithium response using genomic data.

Sci Rep 2021 01 13;11(1):1155. Epub 2021 Jan 13.

Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.

Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen's kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [- 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.
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http://dx.doi.org/10.1038/s41598-020-80814-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806976PMC
January 2021

Review and Consensus on Pharmacogenomic Testing in Psychiatry.

Pharmacopsychiatry 2021 Jan 4;54(1):5-17. Epub 2020 Nov 4.

Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City and School of Medicine, University of Missouri-Kansas City, Kansas City, MO, USA.

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (), oxcarbazepine (), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes () is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.
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http://dx.doi.org/10.1055/a-1288-1061DOI Listing
January 2021

Review and Consensus on Pharmacogenomic Testing in Psychiatry.

Pharmacopsychiatry 2021 Jan 4;54(1):5-17. Epub 2020 Nov 4.

Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City and School of Medicine, University of Missouri-Kansas City, Kansas City, MO, USA.

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (), oxcarbazepine (), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes () is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.
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http://dx.doi.org/10.1055/a-1288-1061DOI Listing
January 2021

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder.

Mol Psychiatry 2021 Jun 16;26(6):2457-2470. Epub 2020 Mar 16.

Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLiGen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
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http://dx.doi.org/10.1038/s41380-020-0689-5DOI Listing
June 2021

Validity of the Mood Disorder Questionnaire (MDQ) as a screening tool for bipolar spectrum disorders in anabaptist populations.

J Psychiatr Res 2020 04 25;123:159-163. Epub 2020 Jan 25.

10Center Drive R3D54, National Institute of Mental Health, Bethesda, MD, 20892, United States. Electronic address:

The Mood Disorder Questionnaire (MDQ) is an established screening tool for bipolar spectrum disorders (BSD), but has not been validated in diverse populations and the best scoring method remains uncertain. This study assessed diagnostic validity of the MDQ among Anabaptists, an underserved population frequently involved in genetic research. 161 participants completed the MDQ and were diagnosed by a best-estimate final diagnosis (BEFD). Diagnostic agreements between alternate MDQ scoring methods and the BEFD were quantified using Cohen's Kappa (κ), sensitivity (α), and specificity (β). Scoring criteria evaluated included >7 simultaneous symptoms and at least moderate impairment, >7 simultaneous symptoms, with at least mild impairment, >7 symptoms only, with no further requirement, and three novel scoring methods that require >5 symptoms or fewer. Diagnostic agreement varied. The original method proved most specific but had the lowest κ and sensitivity. κ increased with more liberal scoring criteria, reaching a maximum under the lower-threshold symptom methods, with little loss of specificity in the 5-symptom method. Decreasing the symptom threshold below 5 conferred little or no benefit. These results support the diagnostic validity of the MDQ among this Anabaptist sample and suggest that a 5-symptom scoring method may increase diagnostic sensitivity in populations at high risk for bipolar disorder.
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http://dx.doi.org/10.1016/j.jpsychires.2020.01.011DOI Listing
April 2020

The genetics of bipolar disorder.

Mol Psychiatry 2020 03 6;25(3):544-559. Epub 2020 Jan 6.

Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Department of Health and Human Services, National Institutes of Health, Bethesda, MD, USA.

Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.
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http://dx.doi.org/10.1038/s41380-019-0634-7DOI Listing
March 2020

Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression.

Mol Psychiatry 2019 Nov 11. Epub 2019 Nov 11.

Department of Psychology, Humboldt-University Berlin, Berlin, Germany.

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10  × 10). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.
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http://dx.doi.org/10.1038/s41380-019-0590-2DOI Listing
November 2019

Clinical and genetic validity of quantitative bipolarity.

Transl Psychiatry 2019 09 16;9(1):228. Epub 2019 Sep 16.

Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, 21228, USA.

Research has yet to provide a comprehensive understanding of the genetic basis of bipolar disorder (BP). In genetic studies, defining the phenotype by diagnosis may miss risk-allele carriers without BP. The authors aimed to test whether quantitatively detected subclinical symptoms of bipolarity identifies a heritable trait that infers risk for BP. The Quantitative Bipolarity Scale (QBS) was administered to 310 Old Order Amish or Mennonite individuals from multigenerational pedigrees; 110 individuals had psychiatric diagnoses (20 BP, 61 major depressive disorders (MDD), 3 psychotic disorders, 26 other psychiatric disorders). Familial aggregation of QBS was calculated using the variance components method to derive heritability and shared household effects. The QBS score was significantly higher in BP subjects (31.5 ± 3.6) compared to MDD (16.7 ± 2.0), other psychiatric diagnoses (7.0 ± 1.9), and no psychiatric diagnosis (6.0 ± 0.65) (all p < 0.001). QBS in the whole sample was significantly heritable (h = 0.46 ± 0.15, p < 0.001) while the variance attributed to the shared household effect was not significant (p = 0.073). When subjects with psychiatric illness were removed, the QBS heritability was similar (h = 0.59 ± 0.18, p < 0.001). These findings suggest that quantitative bipolarity as measured by QBS can separate BP from other psychiatric illnesses yet is significantly heritable with and without BP included in the pedigrees suggesting that the quantitative bipolarity describes a continuous heritable trait that is not driven by a discrete psychiatric diagnosis. Bipolarity trait assessment may be used to supplement the diagnosis of BP in future genetic studies and could be especially useful for capturing subclinical genetic contributions to a BP phenotype.
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http://dx.doi.org/10.1038/s41398-019-0561-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746871PMC
September 2019

Clinical and genetic validity of quantitative bipolarity.

Transl Psychiatry 2019 09 16;9(1):228. Epub 2019 Sep 16.

Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, 21228, USA.

Research has yet to provide a comprehensive understanding of the genetic basis of bipolar disorder (BP). In genetic studies, defining the phenotype by diagnosis may miss risk-allele carriers without BP. The authors aimed to test whether quantitatively detected subclinical symptoms of bipolarity identifies a heritable trait that infers risk for BP. The Quantitative Bipolarity Scale (QBS) was administered to 310 Old Order Amish or Mennonite individuals from multigenerational pedigrees; 110 individuals had psychiatric diagnoses (20 BP, 61 major depressive disorders (MDD), 3 psychotic disorders, 26 other psychiatric disorders). Familial aggregation of QBS was calculated using the variance components method to derive heritability and shared household effects. The QBS score was significantly higher in BP subjects (31.5 ± 3.6) compared to MDD (16.7 ± 2.0), other psychiatric diagnoses (7.0 ± 1.9), and no psychiatric diagnosis (6.0 ± 0.65) (all p < 0.001). QBS in the whole sample was significantly heritable (h = 0.46 ± 0.15, p < 0.001) while the variance attributed to the shared household effect was not significant (p = 0.073). When subjects with psychiatric illness were removed, the QBS heritability was similar (h = 0.59 ± 0.18, p < 0.001). These findings suggest that quantitative bipolarity as measured by QBS can separate BP from other psychiatric illnesses yet is significantly heritable with and without BP included in the pedigrees suggesting that the quantitative bipolarity describes a continuous heritable trait that is not driven by a discrete psychiatric diagnosis. Bipolarity trait assessment may be used to supplement the diagnosis of BP in future genetic studies and could be especially useful for capturing subclinical genetic contributions to a BP phenotype.
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http://dx.doi.org/10.1038/s41398-019-0561-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746871PMC
September 2019

From genetics to biology: advancing mental health research in the Genomics ERA.

Mol Psychiatry 2019 11 4;24(11):1576-1582. Epub 2019 Jun 4.

National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

The Genomics Workgroup of the National Advisory Mental Health Council (NAMHC) recently issued a set of recommendations for advancing the NIMH psychiatric genetics research program and prioritizing subsequent follow-up studies. The report emphasized the primacy of rigorous statistical support from properly designed, well-powered studies for pursuing genetic variants robustly associated with disease. Here we discuss the major points NIMH program staff consider when assessing research applications based on common and rare variants, as well as genetic syndromes, associated with psychiatric disorders. These are broad guiding principles for investigators to consider prior to submission of their applications. NIMH staff weigh these points in the context of reviewer comments, the existing literature, and current investments in related projects. Following the recommendations of the NAMHC, statistical strength and robustness of the underlying genetic discovery weighs heavily in our funding considerations as does the suitability of the proposed experimental approach. We specifically address our evaluation of applications motivated in whole, or in part, by an association between human DNA sequence variation and a disease or trait relevant to the mission of the NIMH.
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http://dx.doi.org/10.1038/s41380-019-0445-xDOI Listing
November 2019

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Nat Genet 2019 05 1;51(5):793-803. Epub 2019 May 1.

Department of Psychiatry, Weill Cornell Medical College, New York, NY, USA.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
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http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 2019

Correction: Exploratory genome-wide association analysis of response to ketamine and a polygenic analysis of response to scopolamine in depression.

Transl Psychiatry 2019 03 5;9(1):108. Epub 2019 Mar 5.

Statistical Genomics and Data Analysis Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Michael F. Grunebaum's name was misspelled/misstated as "Gruenbaum M.F." in the original Article. This has now been updated in the HTML and PDF versions of this Article.
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http://dx.doi.org/10.1038/s41398-019-0442-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6401175PMC
March 2019

Exploratory genome-wide association analysis of response to ketamine and a polygenic analysis of response to scopolamine in depression.

Transl Psychiatry 2018 12 14;8(1):280. Epub 2018 Dec 14.

Statistical Genomics and Data Analysis Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Growing evidence suggests that the glutamatergic modulator ketamine has rapid antidepressant effects in treatment-resistant depressed subjects. The anticholinergic agent scopolamine has also shown promise as a rapid-acting antidepressant. This study applied genome-wide markers to investigate the role of genetic variants in predicting acute antidepressant response to both agents. The ketamine-treated sample included 157 unrelated European subjects with major depressive disorder (MDD) or bipolar disorder (BD). The scopolamine-treated sample comprised 37 unrelated European subjects diagnosed with either MDD or BD who had a current Major Depressive Episode (MDE), and had failed at least two adequate treatment trials for depression. Change in Montgomery-Asberg Depression Rating Scale (MADRS) or the 17-item Hamilton Depression Rating Scale (HAM-D) scale scores at day 1 (24 h post-treatment) was considered the primary outcome. Here, we conduct pilot genome-wide association study (GWAS) analyses to identify potential markers of ketamine response and dissociative side effects. Polygenic risk score analysis of SNPs ranked by the strength of their association with ketamine response was then calculated in order to assess whether common genetic markers from the ketamine study could predict response to scopolamine. Findings require replication in larger samples in light of low power of analyses of these small samples. Neverthless, these data provide a promising illustration of our future potential to identify genetic variants underlying rapid treatment response in mood disorders and may ultimately guide individual patient treatment selection in the future.
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http://dx.doi.org/10.1038/s41398-018-0311-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294748PMC
December 2018

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

Genet Epidemiol 2019 03 9;43(2):189-206. Epub 2018 Dec 9.

Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.
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http://dx.doi.org/10.1002/gepi.22177DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375753PMC
March 2019

Efficient region-based test strategy uncovers genetic risk factors for functional outcome in bipolar disorder.

Eur Neuropsychopharmacol 2019 01 29;29(1):156-170. Epub 2018 Nov 29.

U.S. Department of Health & Human Services, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20894, United States.

Genome-wide association studies of case-control status have advanced the understanding of the genetic basis of psychiatric disorders. Further progress may be gained by increasing sample size but also by new analysis strategies that advance the exploitation of existing data, especially for clinically important quantitative phenotypes. The functionally-informed efficient region-based test strategy (FIERS) introduced herein uses prior knowledge on biological function and dependence of genotypes within a powerful statistical framework with improved sensitivity and specificity for detecting consistent genetic effects across studies. As proof of concept, FIERS was used for the first genome-wide single nucleotide polymorphism (SNP)-based investigation on bipolar disorder (BD) that focuses on an important aspect of disease course, the functional outcome. FIERS identified a significantly associated locus on chromosome 15 (hg38: chr15:48965004 - 49464789 bp) with consistent effect strength between two independent studies (GAIN/TGen: European Americans, BOMA: Germans; n = 1592 BD patients in total). Protective and risk haplotypes were found on the most strongly associated SNPs. They contain a CTCF binding site (rs586758); CTCF sites are known to regulate sets of genes within a chromatin domain. The rs586758 - rs2086256 - rs1904317 haplotype is located in the promoter flanking region of the COPS2 gene, close to microRNA4716, and the EID1, SHC4, DTWD1 genes as plausible biological candidates. While implication with BD is novel, COPS2, EID1, and SHC4 are known to be relevant for neuronal differentiation and function and DTWD1 for psychopharmacological side effects. The test strategy FIERS that enabled this discovery is equally applicable for tag SNPs and sequence data.
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http://dx.doi.org/10.1016/j.euroneuro.2018.10.005DOI Listing
January 2019

Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics.

Int J Bipolar Disord 2018 Nov 11;6(1):24. Epub 2018 Nov 11.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.

Background: Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.

Results: Two of these rules-one associated with eating disorder and the other with anxiety-remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.

Conclusion: Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
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http://dx.doi.org/10.1186/s40345-018-0132-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230336PMC
November 2018

Population-Based Estimates of Heritability Shed New Light on Clinical Features of Major Depression.

Am J Psychiatry 2018 11;175(11):1058-1060

From the Genetic Basis of Mood and Anxiety Disorders Section, Human Genetics Branch, NIMH Intramural Research Program, NIH, Bethesda, Md.

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http://dx.doi.org/10.1176/appi.ajp.2018.18070789DOI Listing
November 2018

Convergent analysis of genome-wide genotyping and transcriptomic data suggests association of zinc finger genes with lithium response in bipolar disorder.

Am J Med Genet B Neuropsychiatr Genet 2018 10 14;177(7):658-664. Epub 2018 Oct 14.

Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.

Lithium is the mainstay treatment in bipolar disorder (BD) for its effectiveness in the acute phases of illness and in prevention of recurrences. Lithium's mechanism of action is complex, and while it modulates the function of hundreds of molecular targets, most of these effects could be unspecific and not relevant for its clinical efficacy. In this study, we applied an integrated analytical approach using genome-wide expression and genotyping data from BD patients to identify lithium-responsive genes that may serve as biomarkers of its efficacy. To this purpose, we tested the effect of treatment with lithium chloride 1 mM on the transcriptome of lymphoblasts from 10 lithium responders (LR) and 10 nonresponders (NR) patients and identified genes significantly influenced by the treatment exclusively in LR. These findings were integrated with gene-based analysis on genome-wide genotyping data from an extended sample of 205 BD patients characterized for lithium response. The expression of 29 genes was significantly changed by lithium exclusively in LR. Gene-based analysis showed that two of these genes, zinc finger protein 429 (ZNF429) and zinc finger protein 493 (ZNF493), were also significantly associated with lithium response. Validation with quantitative real-time polymerase chain reaction confirmed the lithium-induced downregulation of ZNF493 in LR (p = .036). Using convergent analyses of genome-wide expression and genotyping data, we identified ZNF493 as a potential lithium-responsive target that may be involved in modulating lithium efficacy in BD. To our knowledge, this is the first evidence supporting the involvement of zinc finger proteins in lithium response.
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http://dx.doi.org/10.1002/ajmg.b.32663DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230310PMC
October 2018

Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree.

Transl Psychiatry 2018 10 12;8(1):218. Epub 2018 Oct 12.

Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual's risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10), diabetes (p = 1 × 10), and lipid traits such as triglyceride levels (p = 3 × 10) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.
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http://dx.doi.org/10.1038/s41398-018-0226-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6185949PMC
October 2018

Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness.

Mol Psychiatry 2019 04 22;24(4):613-624. Epub 2018 Aug 22.

Human Genetics Branch, National Institute of Mental Health Intramural Research Program (NIMH-IRP), Bethesda, MD, 20892, USA.

Biological characterization of genetic variants identified in genome-wide association studies (GWAS) remains a substantial challenge. Here we used human-induced pluripotent stem cells (iPSC) and their neural derivatives to characterize common variants on chromosome 3p22 that have been associated by GWAS with major mental illnesses. IPSC-derived neural progenitor cells carrying the risk allele of the single nucleotide polymorphism (SNP), rs9834970, displayed lower baseline TRANK1 expression that was rescued by chronic treatment with therapeutic dosages of valproic acid (VPA). VPA had the greatest effects on TRANK1 expression in iPSC, NPC, and astrocytes. Although rs9834970 has no known function, we demonstrated that a nearby SNP, rs906482, strongly affects binding by the transcription factor, CTCF, and that the high-affinity allele usually occurs on haplotypes carrying the rs9834970 risk allele. Decreased expression of TRANK1 perturbed expression of many genes involved in neural development and differentiation. These findings have important implications for the pathophysiology of major mental illnesses and the development of novel therapeutics.
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http://dx.doi.org/10.1038/s41380-018-0207-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894932PMC
April 2019

Evaluation of Recipients of Positive and Negative Secondary Findings Evaluations in a Hybrid CLIA-Research Sequencing Pilot.

Am J Hum Genet 2018 09 16;103(3):358-366. Epub 2018 Aug 16.

Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD 20892, USA. Electronic address:

While consensus regarding the return of secondary genomic findings in the clinical setting has been reached, debate about such findings in the research setting remains. We developed a hybrid, research-clinical translational genomics process for research exome data coupled with a CLIA-validated secondary findings analysis. Eleven intramural investigators from ten institutes at the National Institutes of Health piloted this process. Nearly 1,200 individuals were sequenced and 14 secondary findings were identified in 18 participants. Positive secondary findings were returned by a genetic counselor following a standardized protocol, including referrals for specialty follow-up care for the secondary finding local to the participants. Interviews were undertaken with 13 participants 4 months after receipt of a positive report. These participants reported minimal psychologic distress within a process to assimilate their results. Of the 13, 9 reported accessing the recommended health care services. A sample of 107 participants who received a negative findings report were surveyed 4 months after receiving it. They demonstrated good understanding of the negative secondary findings result and most expressed reassurance (64%) from that report. However, a notable minority (up to 17%) expressed confusion regarding the distinction of primary from secondary findings. This pilot shows it is feasible to couple CLIA-compliant secondary findings to research sequencing with minimal harms. Participants managed the surprise of a secondary finding with most following recommended follow up, yet some with negative findings conflated secondary and primary findings. Additional work is needed to understand barriers to follow-up care and help participants distinguish secondary from primary findings.
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http://dx.doi.org/10.1016/j.ajhg.2018.07.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128311PMC
September 2018

Investigating polygenic burden in age at disease onset in bipolar disorder: Findings from an international multicentric study.

Bipolar Disord 2019 02 28;21(1):68-75. Epub 2018 Jun 28.

Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.

Objectives: Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early-onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD- and schizophrenia (SCZ)-associated risk variants is associated with an earlier AAO in BD patients.

Methods: A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn-Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18 years] vs adulthood [>18 years]) or considered as a continuous measure. The associations between BD- and SCZ-PRSs and AAO were evaluated with regression models.

Results: BD- and SCZ-PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment.

Conclusions: The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype.
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http://dx.doi.org/10.1111/bdi.12659DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585855PMC
February 2019

Rediscovering the value of families for psychiatric genetics research.

Mol Psychiatry 2019 04 28;24(4):523-535. Epub 2018 Jun 28.

South Texas Diabetes and Obesity Institute, Department of Human Genetics, School of Medicine, University of Texas of the Rio Grande Valley, Brownsville, TX, USA.

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.
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http://dx.doi.org/10.1038/s41380-018-0073-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028329PMC
April 2019
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