Nightmares share genetic risk factors with sleep and psychiatric traits.

Nightmares are vivid, extended, and emotionally negative or negative dreams that awaken the dreamer. While sporadic nightmares and bad dreams are common and generally harmless, frequent nightmares often reflect underlying pathologies of emotional regulation. Indeed, insomnia, depression, anxiety, or alcohol use have been associated with nightmares in epidemiological and clinical studies. However, the connection between nightmares and their comorbidities are poorly understood. Our goal was to examine the genetic risk factors for nightmares and estimate correlation or causality between nightmares and comorbidities. We performed a genome-wide association study (GWAS) in 45,255 individuals using a questionnaire-based assessment on the frequency of nightmares during the past month and genome-wide genotyping data. While the GWAS did not reveal individual risk variants, heritability was estimated at 5%. In addition, the genetic correlation analysis showed a robust correlation (rg > 0.4) of nightmares with anxiety (rg = 0.671, p = 7.507e-06), depressive (rg = 0.562, p = 1.282e-07) and posttraumatic stress disorders (rg = 0.4083, p = 0.0152), and personality trait neuroticism (rg = 0.667, p = 4.516e-07). Furthermore, Mendelian randomization suggested causality from insomnia to nightmares (beta = 0.027, p = 0.0002). Our findings suggest that nightmares share genetic background with psychiatric traits and that insomnia may increase an individual's liability to experience frequent nightmares. Given the significant correlations with psychiatric and psychological traits, it is essential to grow awareness of how nightmares affect health and disease and systematically collect information about nightmares, especially from clinical samples and larger cohorts.

A pilot genome-wide association study meta-analysis of gastroparesis.

BACKGROUND: Gastroparesis (GP) is characterized by delayed gastric emptying in the absence of mechanical obstruction. OBJECTIVE: Genetic predisposition may play a role; however, investigation at the genome-wide level has not been performed. METHODS: We carried out a genome-wide association study (GWAS) meta-analysis on (i) 478 GP patients from the National Institute of Diabetes and Digestive and Kidney Diseases Gastroparesis Clinical Research Consortium (GpCRC) compared to 9931 population-based controls from the University of Michigan Health and Retirement Study; and (ii) 402 GP cases compared to 48,340 non-gastroparesis controls from the Michigan Genomics Initiative. Associations for 5,811,784 high-quality SNPs were tested on a total of 880 GP patients and 58,271 controls, using logistic mixed models adjusted for age, sex, and principal components. Gene mapping was obtained based on genomic position and expression quantitative trait loci, and a gene-set network enrichment analysis was performed. Genetic associations with clinical data were tested in GpCRC patients. Protein expression of selected candidate genes was determined in full thickness gastric biopsies from GpCRC patients and controls. RESULTS: While no SNP associations were detected at strict significance (p ≤ 5 × 10-8 ), nine independent genomic loci were associated at suggestive significance (p ≤ 1 × 10-5 ), with the strongest signal (rs9273363, odds ratio = 1.4, p = 1 × 10-7 ) mapped to the human leukocyte antigen region. Computational annotation of suggestive risk loci identified 14 protein-coding candidate genes. Gene-set network enrichment analysis revealed pathways potentially involved in immune and motor dysregulation (pFDR ≤ 0.05). The GP risk allele rs6984536A (Peroxidasin-Like; PXDNL) was associated with increased abdominal pain severity scores (Beta = 0.13, p = 0.03). Gastric muscularis expression of PXDNL also positively correlated with abdominal pain in GP patients (r = 0.8, p = 0.02). Dickkopf WNT Signaling Pathway Inhibitor 1 showed decreased expression in diabetic GP patients (p = 0.005 vs. controls). CONCLUSION: We report preliminary GWAS findings for GP, which highlight candidate genes and pathways related to immune and sensory-motor dysregulation. Larger studies are needed to validate and expand these findings in independent datasets.

Multi-ancestry meta-analysis identifies 5 novel loci for ischemic stroke and reveals heterogeneity of effects between sexes and ancestries.

Stroke is the second leading cause of death and disability worldwide. Stroke prevalence varies by sex and ancestry, possibly due to genetic heterogeneity between subgroups. We performed a genome-wide meta-analysis of 16 biobanks across multiple ancestries to study the genetics of ischemic stroke (60,176 cases, 1,310,725 controls) as part of the Global Biobank Meta-analysis Initiative (GBMI) and further combined the results with previously published MegaStroke. Five novel loci for ischemic stroke (LAMC1, CALCRL, PLSCR1, CDKN1A, and SWAP70) were identified after replication in four additional datasets. One previously reported locus showed significant ancestry heterogeneity (ABO), and one showed significant sex heterogeneity (ALDH2). The ALDH2 association was male specific (males p = 1.67e-24, females p = 0.126) and was additionally observed only in the East Asian ancestry (male) samples. These findings emphasize the need for more diverse datasets with large sample sizes to further understand the genetic predisposition of stroke in different ancestry and sex groups.

The phenotype-genotype reference map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury.

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients.

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.

Limited clinical utility for GWAS or polygenic risk score for postoperative acute kidney injury in non-cardiac surgery in European-ancestry patients.

BACKGROUND: Prior studies support a genetic basis for postoperative acute kidney injury (AKI). We conducted a genome-wide association study (GWAS), assessed the clinical utility of a polygenic risk score (PRS), and estimated the heritable component of AKI in patients who underwent noncardiac surgery. METHODS: We performed a retrospective large-scale genome-wide association study followed by a meta-analysis of patients who underwent noncardiac surgery at the Vanderbilt University Medical Center ("Vanderbilt" cohort) or Michigan Medicine, the academic medical center of the University of Michigan ("Michigan" cohort). In the Vanderbilt cohort, the relationship between polygenic risk score for estimated glomerular filtration rate and postoperative AKI was also tested to explore the predictive power of aggregating multiple common genetic variants associated with AKI risk. Similarly, in the Vanderbilt cohort genome-wide complex trait analysis was used to estimate the heritable component of AKI due to common genetic variants. RESULTS: The study population included 8248 adults in the Vanderbilt cohort (mean [SD] 58.05 [15.23] years, 50.2% men) and 5998 adults in Michigan cohort (56.24 [14.76] years, 49% men). Incident postoperative AKI events occurred in 959 patients (11.6%) and in 277 patients (4.6%), respectively. No loci met genome-wide significance in the GWAS and meta-analysis. PRS for estimated glomerular filtration rate explained a very small percentage of variance in rates of postoperative AKI and was not significantly associated with AKI (odds ratio 1.050 per 1 SD increase in polygenic risk score [95% CI, 0.971-1.134]). The estimated heritability among common variants for AKI was 4.5% (SE = 4.5%) suggesting low heritability. CONCLUSION: The findings of this study indicate that common genetic variation minimally contributes to postoperative AKI after noncardiac surgery, and likely has little clinical utility for identifying high-risk patients.

A survey of functional dyspepsia in 361,360 individuals: Phenotypic and genetic cross-disease analyses.

BACKGROUND: Functional dyspepsia (FD) is a common gastrointestinal condition of poorly understood pathophysiology. While symptoms' overlap with other conditions may indicate common pathogenetic mechanisms, genetic predisposition is suspected but has not been adequately investigated. METHODS: Using healthcare, questionnaire, and genetic data from three large population-based biobanks (UK Biobank, EGCUT, and MGI), we surveyed FD comorbidities, heritability, and genetic correlations across a wide spectrum of conditions and traits in 10,078 cases and 351,282 non-FD controls of European ancestry. KEY RESULTS: In UK Biobank, 281 diagnoses were detected at increased prevalence in FD, based on healthcare records. Among these, gastrointestinal conditions (OR = 4.0, p < 1.0 × 10-300 ), anxiety disorders (OR = 2.3, p < 1.4 × 10-27 ), ischemic heart disease (OR = 2.2, p < 2.3 × 10-76 ), and infectious and parasitic diseases (OR = 2.1, p = 1.5 × 10-73 ) showed strongest association with FD. Similar results were obtained in an analysis of self-reported conditions and use of medications from questionnaire data. Based on a genome-wide association meta-analysis of genotypes across all cohorts, FD heritability was estimated close to 5% ( hSNP2  = 0.047, p = 0.014). Genetic correlations indicate FD predisposition is shared with several other diseases and traits (rg  > 0.344), mostly overlapping with those also enriched in FD patients. Suggestive (p < 5.0 × 10-6 ) association with FD risk was detected for 13 loci, with 2 showing nominal replication (p < 0.05) in an independent cohort of 192 FD patients. CONCLUSIONS & INFERENCES: FD has a weak heritable component that shows commonalities with multiple conditions across a wide spectrum of pathophysiological domains. This new knowledge contributes to a better understanding of FD etiology and may have implications for improving its treatment.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

GWAS of stool frequency provides insights into gastrointestinal motility and irritable bowel syndrome.

Gut dysmotility is associated with constipation, diarrhea, and functional gastrointestinal disorders like irritable bowel syndrome (IBS), although its molecular underpinnings are poorly characterized. We studied stool frequency (defined by the number of bowel movements per day, based on questionnaire data) as a proxy for gut motility in a GWAS meta-analysis including 167,875 individuals from UK Biobank and four smaller population-based cohorts. We identify 14 loci associated with stool frequency (p ≤ 5.0 × 10-8). Gene set and pathway analyses detected enrichment for genes involved in neurotransmitter/neuropeptide signaling and preferentially expressed in enteric motor neurons controlling peristalsis. PheWAS identified pleiotropic associations with dysmotility syndromes and the response to their pharmacological treatment. The genetic architecture of stool frequency correlates with that of IBS, and UK Biobank participants from the top 1% of stool frequency polygenic score distribution were associated with 5× higher risk of IBS with diarrhea. These findings pave the way for the identification of actionable pathological mechanisms in IBS and the dysmotility syndromes.

Genome-Wide Association Study of Pelvic Organ Prolapse Using the Michigan Genomics Initiative.

OBJECTIVES: The aim of this study was to (1) replicate previously identified genetic variants significantly associated with pelvic organ prolapse and (2) identify new genetic variants associated with pelvic organ prolapse using a genome-wide association study. METHODS: Using our institution's database linking genetic and clinical data, we identified 1,329 women of European ancestry with an International Classification of Diseases, Ninth Revision (ICD-9)/ICD-10 code for prolapse, 767 of whom also had Current Procedural Terminology (CPT)/ICD-9/ICD-10 procedure codes for prolapse surgery, and 16,383 women of European ancestry older than 40 years without a prolapse diagnosis code as controls. Patients were genotyped using the Illumina HumanCoreExome chip and imputed to the Haplotype Reference Consortium. We tested 20 million single nucleotide polymorphisms (SNPs) for association with pelvic organ prolapse adjusting for relatedness, age, chip version, and 4 principal components. We compared our results with 18 previously identified genome-wide significant SNPs from the UK Biobank, Commun Biol (2020;3:129), and Obstet Gynecol (2011;118:1345-1353). RESULTS: No variants achieved genome-wide significance (P = 5 × 10-8). However, we replicated 4 SNPs with biologic plausibility at nominal significance (P ≤ 0.05): rs12325192 (P = 0.002), rs9306894 (P = 0.05), rs1920568 (P = 0.034), and rs1247943 (P = 0.041), which were all intergenic and nearest the genes SALL1, GDF7, TBX5, and TBX5, respectively. CONCLUSIONS: Our replication of 4 biologically plausible previously reported SNPs provides further evidence for a genetic contribution to prolapse, specifically that rs12325192, rs9306894, rs1920568, and rs1247943 may contribute to susceptibility for prolapse. These and previously reported associations that have not yet been replicated should be further explored in larger, more diverse cohorts, perhaps through meta-analysis.

ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS.

Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.

GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer.

Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

Validating Online Measures of Cognitive Ability in Genes for Good, a Genetic Study of Health and Behavior.

Genetic association studies routinely require many thousands of participants to achieve sufficient power, yet accumulation of large well-assessed samples is costly. We describe here an effort to efficiently measure cognitive ability and personality in an online genetic study, Genes for Good. We report on the first 21,550 participants with relevant phenotypic data, 7,458 of whom have been genotyped genome-wide. Measures of crystallized and fluid intelligence reflected a two-dimensional latent ability space, with items demonstrating adequate item-level characteristics. The Big Five Inventory questionnaire revealed the expected five-factor model of personality. Cognitive measures predicted educational attainment over and above personality characteristics, as expected. We found that a genome-wide polygenic score of educational attainment predicted educational level, accounting for 4%, 4%, and 2.7% of the variance in educational attainment, verbal reasoning, and spatial reasoning, respectively. In summary, the online cognitive measures in Genes for Good appear to perform adequately and demonstrate expected associations with personality, education, and an education-based polygenic score. Results indicate that online cognitive assessment is one avenue to accumulate large samples of individuals for genetic research of cognitive ability.

Genes for Good: Engaging the Public in Genetics Research via Social Media.

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.