Publications by authors named "Chun Chieh Fan"

49 Publications

Sex differences in Alzheimer's disease: do differences in tau explain the verbal memory gap?

Neurobiol Aging 2021 Jun 4;107:70-77. Epub 2021 Jun 4.

Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA.

To determine if sex differences in verbal memory in AD are related to differences in extent or distribution of pathological tau, we studied 275 participants who were amyloid PET positive and carried clinical classifications of normal cognition, mild cognitive impairment (MCI) or dementia, and had tau (AV1451) PET. We compared tau distribution between men and women, and as a function of genetic risk. In MCI we further explored the relationship between quantity and distribution of tau in relation to verbal memory scores. Women had more tau burden overall, but this was driven by sex differences at the MCI stage. There was no significant difference in tau load by APOE e4 status. Within the MCI group the association between tau and performance in verbal memory tasks was stronger in women than men. The topography of the associations between tau and verbal memory also differed in MCI; women demonstrated stronger relationships between tau distribution and verbal memory performance, especially in the left hemisphere. These findings have implications for understanding tau distribution and spread, and in interpretation of verbal memory performance.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.05.013DOI Listing
June 2021

Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness.

JAMA Psychiatry 2021 Sep;78(9):1020-1030

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Importance: Schizophrenia is a complex heritable disorder associated with many genetic variants, each with a small effect. While cortical differences between patients with schizophrenia and healthy controls are consistently reported, the underlying molecular mechanisms remain elusive.

Objective: To investigate the extent of shared genetic architecture between schizophrenia and brain cortical surface area (SA) and thickness (TH) and to identify shared genomic loci.

Design, Setting, And Participants: Independent genome-wide association study data on schizophrenia (Psychiatric Genomics Consortium and CLOZUK: n = 105 318) and SA and TH (UK Biobank: n = 33 735) were obtained. The extent of polygenic overlap was investigated using MiXeR. The specific shared genomic loci were identified by conditional/conjunctional false discovery rate analysis and were further examined in 3 independent cohorts. Data were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021.

Main Outcomes And Measures: The primary outcomes were estimated fractions of polygenic overlap between schizophrenia, total SA, and average TH and a list of functionally characterized shared genomic loci.

Results: Based on genome-wide association study data from 139 053 participants, MiXeR estimated schizophrenia to be more polygenic (9703 single-nucleotide variants [SNVs]) than total SA (2101 SNVs) and average TH (1363 SNVs). Most SNVs associated with total SA (1966 of 2101 [93.6%]) and average TH (1322 of 1363 [97.0%]) may be associated with the development of schizophrenia. Subsequent conjunctional false discovery rate analysis identified 44 and 23 schizophrenia risk loci shared with total SA and average TH, respectively. The SNV associations of shared loci between schizophrenia and total SA revealed en masse concordant association between the discovery and independent cohorts. After removing high linkage disequilibrium regions, such as the major histocompatibility complex region, the shared loci were enriched in immunologic signature gene sets. Polygenic overlap and shared loci between schizophrenia and schizophrenia-associated regions of interest for SA (superior frontal and middle temporal gyri) and for TH (superior temporal, inferior temporal, and superior frontal gyri) were also identified.

Conclusions And Relevance: This study demonstrated shared genetic loci between cortical morphometry and schizophrenia, among which a subset are associated with immunity. These findings provide an insight into the complex genetic architecture and associated with schizophrenia.
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http://dx.doi.org/10.1001/jamapsychiatry.2021.1435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223140PMC
September 2021

Meaningful associations in the adolescent brain cognitive development study.

Neuroimage 2021 10 18;239:118262. Epub 2021 Jun 18.

Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA 92093, United States; Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA 92093, United States. Electronic address:

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118262DOI Listing
October 2021

The genetic architecture of human complex phenotypes is modulated by linkage disequilibrium and heterozygosity.

Genetics 2021 03;217(3)

Department of Radiology, University of California, San Francisco, San Francisco, CA 94158, USA.

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWASs) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model-primarily through quantification of selection pressure-reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.
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http://dx.doi.org/10.1093/genetics/iyaa046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045737PMC
March 2021

Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men.

Prostate Cancer Prostatic Dis 2021 09 15;24(3):845-851. Epub 2021 Mar 15.

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.

Background: Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.

Methods: Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.

Results: Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10).

Conclusions: PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.
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http://dx.doi.org/10.1038/s41391-021-00341-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387332PMC
September 2021

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations.

Nat Commun 2021 02 23;12(1):1236. Epub 2021 Feb 23.

Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, UK.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS (PHS, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10). Comparing the 80/20 PHS percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.
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http://dx.doi.org/10.1038/s41467-021-21287-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902617PMC
February 2021

Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex.

Cereb Cortex 2021 02;31(3):1478-1488

Center for Human Development, University of California, La Jolla, CA 92161, USA.

Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.
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http://dx.doi.org/10.1093/cercor/bhaa290DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7869101PMC
February 2021

Enriching the design of Alzheimer's disease clinical trials: Application of the polygenic hazard score and composite outcome measures.

Alzheimers Dement (N Y) 2020 13;6(1):e12071. Epub 2020 Sep 13.

University of California San Diego San Diego California USA.

Introduction: Selecting individuals at high risk of Alzheimer's disease (AD) dementia and using the most sensitive outcome measures are important aspects of trial design.

Methods: We divided participants from Alzheimer's Disease Neuroimaging Initiative at the 50th percentile of the predicted absolute risk of the polygenic hazard score (PHS). Outcome measures were the Alzheimer's Disease Assessment Schedule-Cognitive Subscale (ADAS-Cog), ADNI-Mem, Clinical Dementia Rating-Sum of Boxes (CDR SB), and Cognitive Function Composite 2 (CFC2). In addition to modeling, we use a power analysis compare numbers needed with each technique.

Results: Data from 188 cognitively normal and 319 mild cognitively impaired (MCI) participants were analyzed. Using the ADAS-Cog to estimate sample sizes, without stratification over 24 months, would require 930 participants with MCI, while using the CFC2 and restricting participants to those in the upper 50th percentile would require only 284 participants.

Discussion: Combining stratification by PHS and selection of a sensitive combined outcome measure in a cohort of patients with MCI can allow trial design that is more efficient, potentially less burdensome on participants, and more cost effective.
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http://dx.doi.org/10.1002/trc2.12071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507583PMC
September 2020

Author Correction: Understanding the genetic determinants of the brain with MOSTest.

Nat Commun 2020 09 14;11(1):4700. Epub 2020 Sep 14.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-18628-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490377PMC
September 2020

Understanding the genetic determinants of the brain with MOSTest.

Nat Commun 2020 07 14;11(1):3512. Epub 2020 Jul 14.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.
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http://dx.doi.org/10.1038/s41467-020-17368-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360598PMC
July 2020

Sex-dependent autosomal effects on clinical progression of Alzheimer's disease.

Brain 2020 07;143(7):2272-2280

Department of Radiology, University of California, San Diego, USA.

Sex differences in the manifestations of Alzheimer's disease are under intense investigation. Despite the emerging importance of polygenic predictions for Alzheimer's disease, sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on Alzheimer's disease can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Relative to sex-mismatched scores, sex-matched polygenic hazard scores showed significantly stronger associations with age-at-disease-onset, clinical progression, amyloid deposition, neurofibrillary tangles, and composite neuropathological scores, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores, showed lower predictive power than polygenic hazard scores with no evidence for sex differences. Our results indicate that revealing sex-dependent genetic architecture requires the consideration of temporal processes of Alzheimer's disease. This has strong implications not only for the genetic underpinning of Alzheimer's disease but also for how we estimate sex-dependent polygenic effects for clinical use.
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http://dx.doi.org/10.1093/brain/awaa164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364740PMC
July 2020

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Cancer Epidemiol Biomarkers Prev 2020 09 24;29(9):1731-1738. Epub 2020 Jun 24.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-1527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483627PMC
September 2020

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model.

PLoS Genet 2020 05 19;16(5):e1008612. Epub 2020 May 19.

Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
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http://dx.doi.org/10.1371/journal.pgen.1008612DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272101PMC
May 2020

The genetic architecture of the human cerebral cortex.

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Discovery of shared genomic loci using the conditional false discovery rate approach.

Hum Genet 2020 Jan 13;139(1):85-94. Epub 2019 Sep 13.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway.

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.
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http://dx.doi.org/10.1007/s00439-019-02060-2DOI Listing
January 2020

The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges.

Psychiatr Genet 2019 10;29(5):152-159

NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway Center for Human Development, University of California, San Diego Department of Radiology, University of California Department of Neuroscience, University of California, San Diego Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California, USA.

Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.
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http://dx.doi.org/10.1097/YPG.0000000000000234DOI Listing
October 2019

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

Bioinformatics 2020 02;36(3):930-933

Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

Summary: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.

Availability And Implementation: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz633DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868045PMC
February 2020

Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation.

Nat Commun 2019 06 3;10(1):2417. Epub 2019 Jun 3.

Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA, 92037, USA.

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.
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http://dx.doi.org/10.1038/s41467-019-10310-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547727PMC
June 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

Identification of genetic heterogeneity of Alzheimer's disease across age.

Neurobiol Aging 2019 12 12;84:243.e1-243.e9. Epub 2019 Mar 12.

Center for Multimodal Imaging and Genetics, Department of Radiology, University of California, San Diego, CA, USA. Electronic address:

The risk of APOE for Alzheimer's disease (AD) is modified by age. Beyond APOE, the polygenic architecture may also be heterogeneous across age. We aim to investigate age-related genetic heterogeneity of AD and identify genomic loci with differential effects across age. Stratified gene-based genome-wide association studies and polygenic variation analyses were performed in the younger (60-79 years, N = 14,895) and older (≥80 years, N = 6559) age-at-onset groups using Alzheimer's Disease Genetics Consortium data. We showed a moderate genetic correlation (r = 0.64) between the two age groups, supporting genetic heterogeneity. Heritability explained by variants on chromosome 19 (harboring APOE) was significantly larger in younger than in older onset group (p < 0.05). APOE region, BIN1, OR2S2, MS4A4E, and PICALM were identified at the gene-based genome-wide significance (p < 2.73 × 10) with larger effects at younger age (except MS4A4E). For the novel gene OR2S2, we further performed leave-one-out analyses, which showed consistent effects across subsamples. Our results suggest using genetically more homogeneous individuals may help detect additional susceptible loci.
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http://dx.doi.org/10.1016/j.neurobiolaging.2019.02.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783343PMC
December 2019

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration.

Brain 2019 02;142(2):460-470

Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.
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http://dx.doi.org/10.1093/brain/awy327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6351776PMC
February 2019

Exploring Comorbidity Within Mental Disorders Among a Danish National Population.

JAMA Psychiatry 2019 03;76(3):259-270

Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.

Importance: Individuals with mental disorders often develop comorbidity over time. Past studies of comorbidity have often restricted analyses to a subset of disorders and few studies have provided absolute risks of later comorbidity.

Objectives: To undertake a comprehensive study of comorbidity within mental disorders, by providing temporally ordered age- and sex-specific pairwise estimates between the major groups of mental disorders, and to develop an interactive website to visualize all results and guide future research and clinical practice.

Design, Setting, And Participants: This population-based cohort study included all individuals born in Denmark between January 1, 1900, and December 31, 2015, and living in the country between January 1, 2000, and December 31, 2016. The analyses were conducted between June 2017 and May 2018.

Main Outcomes And Measures: Danish health registers were used to identify mental disorders, which were examined within the broad 10-level International Statistical Classification of Diseases and Related Health Problems, 10th Revision, subchapter groups (eg, codes F00-F09 and F10-F19). For each temporally ordered pair of disorders, overall and lagged hazard ratios and 95% CIs were calculated using Cox proportional hazards regression models. Absolute risks were estimated using competing risks survival analyses. Estimates for each sex were generated.

Results: A total of 5 940 778 persons were included in this study (2 958 293 men and 2 982 485 women; mean [SD] age at beginning of follow-up, 32.1 [25.4] years). They were followed up for 83.9 million person-years. All mental disorders were associated with an increased risk of all other mental disorders when adjusting for sex, age, and calendar time (hazard ratios ranging from 2.0 [95% CI, 1.7-2.4] for prior intellectual disabilities and later eating disorders to 48.6 [95% CI, 46.6-50.7] for prior developmental disorders and later intellectual disabilities). The hazard ratios were temporally patterned, with higher estimates during the first year after the onset of the first disorder, but with persistently elevated rates during the entire observation period. Some disorders were associated with substantial absolute risks of developing specific later disorders (eg, 30.6% [95% CI, 29.3%-32.0%] of men and 38.4% [95% CI, 37.5%-39.4%] of women with a diagnosis of mood disorders before age 20 years developed neurotic disorders within the following 5 years).

Conclusions And Relevance: Comorbidity within mental disorders is pervasive, and the risk persists over time. This study provides disorder-, sex-, and age-specific relative and absolute risks of the comorbidity of mental disorders. Web-based interactive data visualization tools are provided for clinical utility.
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http://dx.doi.org/10.1001/jamapsychiatry.2018.3658DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439836PMC
March 2019

Spatial fine-mapping for gene-by-environment effects identifies risk hot spots for schizophrenia.

Nat Commun 2018 12 13;9(1):5296. Epub 2018 Dec 13.

National Centre for Register-based Research, Aarhus University, Aarhus, 8210, Denmark.

Spatial mapping is a promising strategy to investigate the mechanisms underlying the incidence of psychosis. We analyzed a case-cohort study (n = 24,028), drawn from the 1.47 million Danish persons born between 1981 and 2005, using a novel framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing and polygenic scores. Upbringing in a high environmental risk locale increases the risk for schizophrenia by 122%. Individuals living in a high gene-by-environmental risk locale have a 78% increased risk compared to those who have the same genetic liability but live in a low-risk locale. Effects of specific locales vary substantially within the most densely populated city of Denmark, with hazard ratios ranging from 0.26 to 9.26 for environment and from 0.20 to 5.95 for gene-by-environment. These findings indicate the critical synergism of gene and environment on the etiology of schizophrenia and demonstrate the potential of incorporating geolocation in genetic studies.
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http://dx.doi.org/10.1038/s41467-018-07708-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294340PMC
December 2018

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer's disease.

Acta Neuropathol 2019 02 9;137(2):209-226. Epub 2018 Nov 9.

Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, USA.

Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer's disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10), MINK1 (chromosome 17, meta-p = 1.98 × 10) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10 and closest gene = MYBPC3, meta-p = 5.62 × 10). In a large 'AD-by-proxy' cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.
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http://dx.doi.org/10.1007/s00401-018-1928-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358498PMC
February 2019

Regionally specific TSC1 and TSC2 gene expression in tuberous sclerosis complex.

Sci Rep 2018 09 6;8(1):13373. Epub 2018 Sep 6.

Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94143, USA.

Tuberous sclerosis complex (TSC), a heritable neurodevelopmental disorder, is caused by mutations in the TSC1 or TSC2 genes. To date, there has been little work to elucidate regional TSC1 and TSC2 gene expression within the human brain, how it changes with age, and how it may influence disease. Using a publicly available microarray dataset, we found that TSC1 and TSC2 gene expression was highest within the adult neo-cerebellum and that this pattern of increased cerebellar expression was maintained throughout postnatal development. During mid-gestational fetal development, however, TSC1 and TSC2 expression was highest in the cortical plate. Using a bioinformatics approach to explore protein and genetic interactions, we confirmed extensive connections between TSC1/TSC2 and the other genes that comprise the mammalian target of rapamycin (mTOR) pathway, and show that the mTOR pathway genes with the highest connectivity are also selectively expressed within the cerebellum. Finally, compared to age-matched controls, we found increased cerebellar volumes in pediatric TSC patients without current exposure to antiepileptic drugs. Considered together, these findings suggest that the cerebellum may play a central role in TSC pathogenesis and may contribute to the cognitive impairment, including the high incidence of autism spectrum disorder, observed in the TSC population.
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http://dx.doi.org/10.1038/s41598-018-31075-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6127129PMC
September 2018

Williams Syndrome neuroanatomical score associates with GTF2IRD1 in large-scale magnetic resonance imaging cohorts: a proof of concept for multivariate endophenotypes.

Transl Psychiatry 2018 06 8;8(1):114. Epub 2018 Jun 8.

Department of Neurosciences, School of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92037, USA.

Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William's Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus (p = 5e-5) located at GTF2IRD1, which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes (n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e-4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.
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http://dx.doi.org/10.1038/s41398-018-0166-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993783PMC
June 2018

Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease.

Front Neurosci 2018 30;12:260. Epub 2018 Apr 30.

Department of Radiology, University of California, San Diego, La Jolla, CA, United States.

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond . Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months ( = 1.07e-5), and PHS was significantly more predictive than alone ( = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.
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http://dx.doi.org/10.3389/fnins.2018.00260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937163PMC
April 2018

A molecule-based genetic association approach implicates a range of voltage-gated calcium channels associated with schizophrenia.

Am J Med Genet B Neuropsychiatr Genet 2018 06 28;177(4):454-467. Epub 2018 Apr 28.

NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Traditional genome-wide association studies (GWAS) have successfully detected genetic variants associated with schizophrenia. However, only a small fraction of heritability can be explained. Gene-set/pathway-based methods can overcome limitations arising from single nucleotide polymorphism (SNP)-based analysis, but most of them place constraints on size which may exclude highly specific and functional sets, like macromolecules. Voltage-gated calcium (Ca ) channels, belonging to macromolecules, are composed of several subunits whose encoding genes are located far away or even on different chromosomes. We combined information about such molecules with GWAS data to investigate how functional channels associated with schizophrenia. We defined a biologically meaningful SNP-set based on channel structure and performed an association study by using a validated method: SNP-set (sequence) kernel association test. We identified eight subtypes of Ca channels significantly associated with schizophrenia from a subsample of published data (N = 56,605), including the L-type channels (Ca 1.1, Ca 1.2, Ca 1.3), P-/Q-type Ca 2.1, N-type Ca 2.2, R-type Ca 2.3, T-type Ca 3.1, and Ca 3.3. Only genes from Ca 1.2 and Ca 3.3 have been implicated by the largest GWAS (N = 82,315). Each subtype of Ca channels showed relatively high chip heritability, proportional to the size of its constituent gene regions. The results suggest that abnormalities of Ca channels may play an important role in the pathophysiology of schizophrenia and these channels may represent appropriate drug targets for therapeutics. Analyzing subunit-encoding genes of a macromolecule in aggregate is a complementary way to identify more genetic variants of polygenic diseases. This study offers the potential of power for discovery the biological mechanisms of schizophrenia.
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http://dx.doi.org/10.1002/ajmg.b.32634DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093061PMC
June 2018

Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.

Front Genet 2018 5;9:77. Epub 2018 Mar 5.

Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States.

With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to implement and capable of integrating functional annotation information into the estimation procedure, is proposed in this work. The idea is to use the prior distribution of the effect sizes explicitly as a penalty function. The estimates obtained are shown to be better correlated with the true effect sizes (in comparison with a few existing techniques). An increase in the positive and negative predictive value is demonstrated using Hapgen2 simulated data.
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http://dx.doi.org/10.3389/fgene.2018.00077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844985PMC
March 2018
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