To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

Objective

To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses.

Materials and methods

We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results.

Results

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

Discussion

Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals.

Conclusion

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

Defining the progeria phenome

Progeroid disorders are a heterogenous group of rare and complex hereditary syndromes presenting with pleiotropic phenotypes associated with normal aging. Due to the large variation in clinical presentation the diseases pose a diagnostic challenge for clinicians which consequently restricts medical research. To accommodate the challenge, we compiled a list of known progeroid syndromes and calculated the mean prevalence of their associated phenotypes, defining what we term the 'progeria phenome'. The data were used to train a support vector machine that is available at https://www.mitodb.com and able to classify progerias based on phenotypes. Furthermore, this allowed us to investigate the correlation of progeroid syndromes and syndromes with various pathogenesis using hierarchical clustering algorithms and disease networks. We detected that ataxia-telangiectasia like disorder 2, spastic paraplegia 49 and Meier-Gorlin syndrome display strong association to progeroid syndromes, thereby implying that the syndromes are previously unrecognized progerias. In conclusion, our study has provided tools to evaluate the likelihood of a syndrome or patient being progeroid. This is a considerable step forward in our understanding of what constitutes a premature aging disorder and how to diagnose them.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

BACKGROUND

Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics.

RESULTS

Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype-phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories.

CONCLUSIONS

Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

Stress combinations and their interactions in plants database: a one-stop resource on combined stress responses in plants

We have developed a compendium and interactive platform, named Stress Combinations and their Interactions in Plants Database (SCIPDb; http://www.nipgr.ac.in/scipdb.php), which offers information on morpho-physio-biochemical (phenome) and molecular (transcriptome and metabolome) responses of plants to different stress combinations. SCIPDb is a plant stress informatics hub for data mining on phenome, transcriptome, trait-gene ontology, and data-driven research for advancing mechanistic understanding of combined stress biology. We analyzed global phenome data from 939 studies to delineate the effects of various stress combinations on yield in major crops and found that yield was substantially affected under abiotic-abiotic stresses. Transcriptome datasets from 36 studies hosted in SCIPDb identified novel genes, whose roles have not been earlier established in combined stress. Integretome analysis under combined drought-heat stress pinpointed carbohydrate, amino acid, and energy metabolism pathways as the crucial metabolic, proteomic, and transcriptional components in plant tolerance to combined stress. These examples illustrate the application of SCIPDb in identifying novel genes and pathways involved in combined stress tolerance. Further, we showed the application of this database in identifying novel candidate genes and pathways for combined drought and pathogen stress tolerance. To our knowledge, SCIPDb is the only publicly available platform offering combined stress-specific omics big data visualization tools, such as an interactive scrollbar, stress matrix, radial tree, global distribution map, meta-phenome analysis, search, BLAST, transcript expression pattern table, Manhattan plot, and co-expression network. These tools facilitate a better understanding of the mechanisms underlying plant responses to combined stresses.

Life is pain: Fibromyalgia as a nexus of multiple liability distributions

Fibromyalgia is a complex disease of unclear etiology that is complicated by difficulties in diagnosis, treatment, and clinical heterogeneity. To clarify this etiology, healthcare-based data are leveraged to assess the influences on fibromyalgia in several domains. Prevalence is less than 1% of females in our population register data, and about 1/10th that in males. Fibromyalgia often presents with co-occurring conditions including back pain, rheumatoid arthritis, and anxiety. More comorbidities are identified with hospital-associated biobank data, falling into three broad categories of pain-related, autoimmune, and psychiatric disorders. Selecting representative phenotypes with published genome-wide association results for polygenic scoring, we confirm genetic predispositions to psychiatric, pain sensitivity, and autoimmune conditions show associations with fibromyalgia, although these may differ by ancestry group. We conduct a genome-wide association analysis of fibromyalgia in biobank samples, which did not result in any genome-wide significant loci; further studies with increased sample size are necessary to identify specific genetic effects on fibromyalgia. Overall, fibromyalgia appears to have strong clinical and likely genetic links to several disease categories, and could usefully be understood as a composite manifestation of these etiological sources.

Phenomics: conceptualization and importance for plant physiology

Phenomics is a relatively new discipline of biology that has been widely applied in several fields, mainly in crop sciences. We reviewed the concepts used in this discipline (particularly for plants) and found a lack of consensus on what defines a phenomic study. Furthermore, phenomics has been primarily developed around its technical aspects (operationalization), while the conceptual framework of the actual research lags behind. Each research group has given its own interpretation of this 'omic' and thus unwittingly created a 'conceptual controversy'. Addressing this issue is of particular importance, as the experimental designs and concepts of phenomics are so diverse that it is difficult to compare studies. In this opinion article, we evaluate the conceptual framework of phenomics.

Comprehensive Evaluation and Transcriptome Analysis Reveal the Salt Tolerance Mechanism in Semi-Wild Cotton (Gossypium purpurascens)

Elevated salinity significantly threatens cotton growth, particularly during the germination and seedling stages. The utilization of primitive species of Gossypium hirsutum, specifically Gossypium purpurascens, has the potential to facilitate the restoration of genetic diversity that has been depleted due to selective breeding in modern cultivars. This investigation evaluated 45 G. purpurascens varieties and a salt-tolerant cotton variety based on 34 morphological, physiological, and biochemical indicators and comprehensive salt tolerance index values. This study effectively identified a total of 19 salt-tolerant and two salt-resistant varieties. Furthermore, transcriptome sequencing of a salt-tolerant genotype (Nayanmian-2; NY2) and a salt-sensitive genotype (Sanshagaopao-2; GP2) revealed 2776, 6680, 4660, and 4174 differentially expressed genes (DEGs) under 0.5, 3, 12, and 24 h of salt stress. Gene ontology enrichment analysis indicated that the DEGs exhibited significant enrichment in biological processes like metabolic (GO:0008152) and cellular (GO:0009987) processes. MAPK signaling, plant-pathogen interaction, starch and sucrose metabolism, plant hormone signaling, photosynthesis, and fatty acid metabolism were identified as key KEGG pathways involved in salinity stress. Among the DEGs, including NAC, MYB, WRKY, ERF, bHLH, and bZIP, transcription factors, receptor-like kinases, and carbohydrate-active enzymes were crucial in salinity tolerance. Weighted gene co-expression network analysis (WGCNA) unveiled associations of salt-tolerant genotypes with flavonoid metabolism, carbon metabolism, and MAPK signaling pathways. Identifying nine hub genes (MYB4, MYB105, MYB36, bZIP19, bZIP43, FRS2 SMARCAL1, BBX21, F-box) across various intervals offered insights into the transcriptional regulation mechanism of salt tolerance in G. purpurascens. This study lays the groundwork for understanding the important pathways and gene networks in response to salt stress, thereby providing a foundation for enhancing salt tolerance in upland cotton.

The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases

The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e., matrisome genes) in various disease states. A significant contribution on the part of ECM polymorphisms is evident in various types of disease, particularly those in the core-matrisome genes. Our results confirm previous links to connective-tissue disorders but also unearth new and underexplored relationships with neurological, psychiatric, and age-related disease states. Through our analysis of the drug indications for gene-disease relationships, we identify numerous targets that may be repurposed for age-related pathologies. The identification of ECM polymorphisms and their contributions to disease will play an integral role in future therapeutic developments, drug repurposing, precision medicine, and personalized care.

Maternal Folate Status and the Relation between Gestational Arsenic Exposure and Child Health Outcomes

Gestational arsenic exposure adversely impacts child health. Folate-mediated 1-carbon metabolism facilitates urinary excretion of arsenic and may prevent arsenic-related adverse health outcomes. We investigated the potential for maternal folate status to modify associations between gestational arsenic exposure and child health. We used data from 364 mother-child pairs in the MIREC study, a prospective pan-Canadian cohort. During pregnancy, we measured first trimester urinary arsenic concentrations, plasma folate biomarkers, and folic acid supplementation intake. At age 3 years, we evaluated twelve neurodevelopmental and anthropometric features. Using latent profile analysis and multinomial regression, we developed phenotypic profiles of child health, estimated covariate-adjusted associations between arsenic and these phenotypic profiles, and evaluated whether folate status modified these associations. We identified three phenotypic profiles of neurodevelopment and three of anthropometry, ranging from less to more optimal child health. Gestational arsenic was associated with decreased odds of optimal neurodevelopment. Maternal folate status did not modify associations of arsenic with neurodevelopmental phenotypic profiles, but gestational arsenic was associated with increased odds of excess adiposity among those who exceed recommendations for folic acid (>1000 μg/day). However, arsenic exposure was low and folate status was high. Gestational arsenic exposure may adversely impact child neurodevelopment and anthropometry, and maternal folate status may not modify these associations; however, future work should examine these associations in more arsenic-exposed or lower folate-status populations.

Explaining pre-emptive acclimation by linking information to plant phenotype

We review mechanisms for pre-emptive acclimation in plants and propose a conceptual model linking developmental and evolutionary ecology with the acquisition of information through sensing of cues and signals. The idea is that plants acquire much of the information in the environment not from individual cues and signals but instead from their joint multivariate properties such as correlations. If molecular signalling has evolved to extract such information, the joint multivariate properties of the environment must be encoded in the genome, epigenome, and phenome. We contend that multivariate complexity explains why extrapolating from experiments done in artificial contexts into natural or agricultural systems almost never works for characters under complex environmental regulation: biased relationships among the state variables in both time and space create a mismatch between the evolutionary history reflected in the genotype and the artificial growing conditions in which the phenotype is expressed. Our model can generate testable hypotheses bridging levels of organization. We describe the model and its theoretical bases, and discuss its implications. We illustrate the hypotheses that can be derived from the model in two cases of pre-emptive acclimation based on correlations in the environment: the shade avoidance response and acclimation to drought.

Syndromic male subfertility: a network view of genome-phenome associations

Background

Male infertility is a disorder of the reproductive system with a highly complex genetic landscape. In most cases, the reason for male infertility remains unknown; however, the importance of genetic abnormalities in the diagnosis of subfertility/infertility is becoming increasingly recognized. Several syndromes include impaired male fertility in the clinical picture, although a comprehensive analysis of genetic causes of the syndromology perspective of male reproduction is not yet available.

Objectives

(1) To develop a catalog of syndromes and corresponding genes associated with impaired male fertility and (2) to visualize an up‐to‐date genome–phenome network of syndromic male subfertility.

Materials and methods

Published literature was retrieved from the Online Mendelian Inheritance in Man, Orphanet, Human Phenotype Ontology and PubMed databases using keywords “male infertility,” “syndrome,” “gene,” and “case report”; time period from 1980 to September, 2021. Retrieved data were organized as a catalog and complemented with identification numbers of syndromes (MIM ID) and genes (Gene ID). The genome–phenome network and the phenome network were visualized using Cytoscape and Gephi software platforms. Protein–protein interaction analysis was performed using STRING tool.

Results

Retrieved syndromes were presented as (1) a catalog containing 63 syndromes and 93 associated genes, (2) a genome–phenome network including CHD7 and WT1 genes and Noonan and Kartagener syndromes, and (3) a phenome network including 63 syndromes, and 25 categories of clinical features.

Discussion

The developed catalog will contribute to the advances and translational impact toward understanding the factors of syndromic male infertility. Visualized networks provide simple, flexible tools for clinicians and researchers to quickly generate hypotheses and gain a deeper understanding of underlying mechanisms affecting male reproduction.

Conclusion

Recognition of the significance of genome–phenome visualization as part of network medicine can help expedite efforts toward unravelling molecular mechanisms and enable advances personal/precision medicine of male reproduction and other complex traits.

Ibero-American Endometriosis Patient Phenome: Demographics, Obstetric-Gynecologic Traits, and Symptomatology

Background: An international collaborative study was conducted to determine the demographic and clinical profiles of Hispanic/Latinx endometriosis patients from Latin America and Spain using the Minimal Clinical Questionnaire developed by the World Endometriosis Research Foundation (WERF) Endometriosis Phenome and Biobanking Harmonization Project (EPHect). Methods: This is a cross-sectional study to collect self-reported data on demographics, lifestyle, and endometriosis symptoms of Hispanic/Latinx endometriosis patients from April 2019 to February 2020. The EPHect Minimal Clinical Questionnaire (EPQ-M) was translated into Spanish. Comprehension and length of the translated survey were assessed by Spanish-speaking women. An electronic link was distributed via social media of endometriosis patient associations from 11 Latin American countries and Spain. Descriptive statistics (frequency, means and SD, percentages, and proportions) and correlations were conducted using SPSSv26. Results: The questionnaire was completed by 1,378 participants from 23 countries; 94.6% had self-reported diagnosis of endometriosis. Diagnostic delay was 6.6 years. Most participants had higher education, private health insurance, and were employed. The most common symptoms were back/leg pain (85.4%) and fatigue (80.7%). The mean number of children was 1.5; 34.4% had miscarriages; the mean length of infertility was 3.7 years; 47.2% reported pregnancy complications. The most common hormone treatment was oral contraceptives (47.0%). The most common comorbidities were migraines (24.1%), polycystic ovary syndrome (PCOS) (22.2%), and irritable bowel syndrome (21.1%). Most participants (97.0%) experienced pelvic pain during menses; for 78.7%, pain was severe; 86.4% reported dyspareunia. The mean age of dysmenorrhea onset was 16.2 years (SD ± 6.1). Hormone treatments were underutilized, while impact was substantial. Pain catastrophizing scores were significantly correlated with pain intensity (p < 0.001). Conclusion: This is the first comprehensive effort to generate a clinical-demographic profile of Hispanic/Latinx endometriosis patients. Differences in clinical presentation compared to other cohorts included higher prevalence and severity of dysmenorrhea and dyspareunia and high levels of pain catastrophizing. Though future studies are needed to dissect the impact of race and ethnicity on pain and impact, this profile is the first step to facilitate the recognition of risk factors and diagnostic features and promote improved clinical management of this patient population. The EPHect questionnaire is an efficient tool to capture data to allow comparisons across ethnicities and geographic regions and tackle disparities in endometriosis research.

The Role of Electronic Health Records in Advancing Genomic Medicine

Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.

Unique Attributes of the Laurel Wilt Fungal Pathogen, Raffaelea lauricola, as Revealed by Metabolic Profiling

Raffaelea lauricola is the causative agent of laurel wilt, a devastating disease of lauraceous trees. R. lauricola is also an obligate nutritional symbiont of several ambrosia beetle species who act as vectors for the pathogen. Here, we sought to establish the baseline “phenome” of R. lauricola with knowledge concerning its metabolic capability, expanding our understanding of how these processes are impacted by environmental and host nutrients. Phenotypic screening using a microarray of over one thousand compounds was used to generate a detailed profile of R. lauricola substrate utilization and chemical sensitivity. These data revealed (i) relatively restricted carbon utilization, (ii) broad sulfur and phosphate utilization, and (iii) pH and osmotic sensitivities that could be rescued by specific compounds. Additional growth profiling on fatty acids revealed toxicity on C10 substrates and lower, with robust growth on C12–C18 fatty acids. Conditions for lipid droplet (LD) visualization and LD dynamics were examined using a series of lipid dyes. These data provide unique insights regarding R. lauricola metabolism and physiology, and identify distinct patterns of substrate usage and sensitivity which likely reflect important aspects of the host-microbe interface and can be exploited for the development of strategies for mitigating the spread of laurel wilt.

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

A Phenome-Wide Association Study of the Effects of Fusarium graminearum Transcription Factors on Fusarium Graminearum Virus 1 Infection

The Fusarium graminearum virus 1 (FgV1) causes noticeable phenotypic changes such as reduced mycelial growth, increase pigmentation, and reduced pathogenicity in its host fungi, Fusarium graminearum. Previous study showed that the numerous F. graminearum genes including regulatory factors were differentially expressed upon FgV1 infection, however, we have limited knowledge on the effect(s) of specific transcription factor (TF) during FgV1 infection in host fungus. Using gene-deletion mutant library of 657 putative TFs in F. graminearum, we transferred FgV1 by hyphal anastomosis to screen transcription factors that might be associated with viral replication or symptom induction. FgV1-infected TF deletion mutants were divided into three groups according to the mycelial growth phenotype compare to the FgV1-infected wild-type strain (WT-VI). The FgV1-infected TF deletion mutants in Group 1 exhibited slow or weak mycelial growth compare to that of WT-VI on complete medium at 5 dpi. In contrast, Group 3 consists of virus-infected TF deletion mutants showing faster mycelial growth and mild symptom compared to that of WT-VI. The hyphal growth of FgV1-infected TF deletion mutants in Group 2 was not significantly different from that of WT-VI. We speculated that differences of mycelial growth among the FgV1-infected TF deletion mutant groups might be related with the level of FgV1 RNA accumulations in infected host fungi. By conducting real-time quantitative reverse transcription polymerase chain reaction, we observed close association between FgV1 RNA accumulation and phenotypic differences of FgV1-infected TF deletion mutants in each group, i.e., increased and decreased dsRNA accumulation in Group 1 and Group 3, respectively. Taken together, our analysis provides an opportunity to identify host's regulator(s) of FgV1-triggered signaling and antiviral responses and helps to understand complex regulatory networks between FgV1 and F. graminearum interaction.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

Background:

We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

Methods:

The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.

Results:

Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.

Conclusions:

Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Phenome-wide and genome-wide analyses of quality of life in schizophrenia

BACKGROUND

Schizophrenia negatively affects quality of life (QoL). A handful of variables from small studies have been reported to influence QoL in patients with schizophrenia, but a study comprehensively dissecting the genetic and non-genetic contributing factors to QoL in these patients is currently lacking.

AIMS

We adopted a hypothesis-generating approach to assess the phenotypic and genotypic determinants of QoL in schizophrenia.

METHOD

The study population comprised 1119 patients with a psychotic disorder, 1979 relatives and 586 healthy controls. Using linear regression, we tested >100 independent demographic, cognitive and clinical phenotypes for their association with QoL in patients. We then performed genome-wide association analyses of QoL and examined the association between polygenic risk scores for schizophrenia, major depressive disorder and subjective well-being and QoL.

RESULTS

We found nine phenotypes to be significantly and independently associated with QoL in patients, the most significant ones being negative (β = -1.17; s.e. 0.05; P = 1 × 10-83; r2 = 38%), depressive (β = -1.07; s.e. 0.05; P = 2 × 10-79; r2 = 36%) and emotional distress (β = -0.09; s.e. 0.01; P = 4 × 10-59, r2 = 25%) symptoms. Schizophrenia and subjective well-being polygenic risk scores, using various P-value thresholds, were significantly and consistently associated with QoL (lowest association P-value = 6.8 × 10-6). Several sensitivity analyses confirmed the results.

CONCLUSIONS

Various clinical phenotypes of schizophrenia, as well as schizophrenia and subjective well-being polygenic risk scores, are associated with QoL in patients with schizophrenia and their relatives. These may be targeted by clinicians to more easily identify vulnerable patients with schizophrenia for further social and clinical interventions to improve their QoL.

A defined human aging phenome

Aging is among the most complex phenotypes that occur in humans. Identifying the interplay between different age-associated features is undoubtedly critical to our understanding of aging and thus age-associated diseases. Nevertheless, what constitutes human aging is not well characterized. Towards this end, we mined millions of PubMed abstracts for age-associated terms, enabling us to generate a detailed description of the human aging phenotype. We discovered age-associated features in clusters that can be broadly associated with previously defined hallmarks of aging, consequently identifying areas where interventions could be pursued. Importantly, we validated the newly discovered features by manually verifying the prevalence of these features in combined cohorts describing 76 million individuals, allowing us to stratify features in aging that appear to be the most prominent. In conclusion, we propose a comprehensive landscape of human aging: the human aging phenome.

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Background

Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation.

Methods

We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107).

Results

Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10⁻⁹) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10⁻¹⁰) and variants in IRF4 (p=2.8×10⁻⁵⁷), SLC45A2 (p=2.2×10⁻¹³⁰), HERC2 (p=2.8×10⁻¹⁷⁶), OCA2 (p=2.4×10⁻¹²¹) and MC1R (p=7.7×10⁻²²) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10⁻⁹) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9.

Conclusion

Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.

Polygenic prediction of the phenome, across ancestry, in emerging adulthood

BACKGROUND

Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on the clinical disorder or cognitive abilities rather than normal-range behavioral outcomes.

METHODS

This study examined a sample of emerging adults 18-22 years of age (N = 5947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. Twenty-eight hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes.

RESULTS

Polygenic risk for schizophrenia (SZ) in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease.

CONCLUSIONS

These results demonstrate the extensive impact of genetic risk for SZ, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.

Mini-Review: The Contribution of Intermediate Phenotypes to GxE Effects on Disorders of Body Composition in the New OMICS Era

Studies of gene-environment (GxE) interactions describe how genetic and environmental factors influence the risk of developing disease. Intermediate (molecular or clinical) phenotypes (IPs) are traits or metabolic biomarkers that mediate the effects of gene-environment influences on risk behaviors. Functional systems genomics discovery offers mechanistic insights into how DNA variations affect IPs in order to detect genetic causality for a given disease. Disorders of body composition include obesity (OB), Type 2 diabetes (T2D), and osteoporosis (OSTP). These pathologies are examples of how a GxE interaction contributes to their development. IPs as surrogates for inherited genotypes play a key role in models of genetic and environmental interactions in health outcomes. Such predictive models may unravel relevant genomic and molecular pathways for preventive and therapeutic interventions for OB, T2D, and OSTP. Annotation strategies for genomes, in contrast to phenomes, are well advanced. They generally do not measure specific aspects of the environment. Therefore, the concepts of deep phenotyping and the exposome generate new avenues to exploit with high-resolution technologies for analyzing this sophisticated phenome. With the successful characterization of phenomes, exposomes, and genomes, environmental and genetic determinants of chronic diseases can be united with multi-OMICS studies that better examine GxE interactions.

Phenome-wide association studies: a new method for functional genomics in humans

In experimental physiological research, a common study design for examining the functional role of a gene or a genetic variant is to introduce that genetic variant into a model organism (such as yeast or mouse) and then to search for phenotypic consequences. The development of DNA biobanks linked to dense phenotypic information enables such an experiment to be applied to human subjects in the form of a phenome-wide association study (PheWAS). The PheWAS paradigm takes advantage of a curated medical phenome, often derived from electronic health records, to search for associations between 'input functions' and phenotypes in an unbiased fashion. The most commonly studied input function to date has been single nucleotide polymorphisms (SNPs), but other inputs, such as sets of SNPs or a disease or drug exposure, are now being explored to probe the genetic and phenotypic architecture of human traits. Potential outcomes of these approaches include defining subsets of complex diseases (that can then be targeted by specific therapies) and drug repurposing.

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach

Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.

Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

OBJECTIVES

Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity.

METHODS

We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present.

RESULTS

Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins.

CONCLUSION

This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.

Visual Analysis for Detection and Quantification of Pseudomonas cichorii Disease Severity in Tomato Plants

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (≥ 10(6) cfu/ml) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (ΦPSII) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII (Fv/Fm) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.

An Individual-Centered Framework For Unravelling Genotype-Phenotype Interactions

A new framework in which the multiple levels of molecular variations contribute to phenotypic variations in a complex, nonlinear and interactive way, challenges the hierarchical nature of the relationships between the genotypic and phenotypic spaces. This individual-centered framework provides new insights on the evolutionary mechanisms involved in the production of phenotypes. We propose to move this research agenda forward by combining selection experiments and functional genetics.

A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Introduction: new tools for enhancing collaborative endometriosis research

This issue of Fertility and Sterility contains four articles by the World Endometriosis Research Foundation whose present objective is global standardization of the collection of phenotypic data and biological samples, designated as the Endometriosis Phenome and Biobanking Harmonisation Project. The aim is to facilitate large-scale international, multicenter trials that are robust, and will result in biomarker and treatment targets to advance research in endometriosis.

Phenotypic profiling of ABC transporter coding genes in Myxococcus xanthus

Information about a gene sometimes can be deduced by examining the impact of its mutation on phenotype. However, the genome-scale utility of the method is limited because, for nearly all model organisms, the majority of mutations result in little or no observable phenotypic impact. The cause of this is often attributed to robustness or redundancy within the genome, but that is only one plausible hypothesis. We examined a standard set of phenotypic traits, and applied statistical methods commonly used in the study of natural variants to an engineered mutant strain collection representing disruptions in 180 of the 192 ABC transporters within the bacterium Myxococcus xanthus. These strains display continuous variation in their phenotypic distributions, with a small number of “outlier” strains at both phenotypic extremes, and the majority within a confidence interval about the mean that always includes wild type. Correlation analysis reveals substantial pleiotropy, indicating that the traits do not represent independent variables. The traits measured in this study co-cluster with expression profiles, thereby demonstrating that these changes in phenotype correspond to changes at the molecular level, and therefore can be indirectly connected to changes in the genome. However, the continuous distributions, the pleiotropy, and the placement of wild type always within the confidence interval all indicate that this standard set of M. xanthus phenotypic assays is measuring a narrow range of partially overlapping traits that do not directly reflect fitness. This is likely a significant cause of the observed small phenotypic impact from mutation, and is unrelated to robustness and redundancy.

New roots for agriculture: exploiting the root phenome

Recent advances in root biology are making it possible to genetically design root systems with enhanced soil exploration and resource capture. These cultivars would have substantial value for improving food security in developing nations, where yields are limited by drought and low soil fertility, and would enhance the sustainability of intensive agriculture. Many of the phenes controlling soil resource capture are related to root architecture. We propose that a better understanding of the root phenome is needed to effectively translate genetic advances into improved crop cultivars. Elementary, unique root phenes need to be identified. We need to understand the 'fitness landscape' for these phenes: how they affect crop performance in an array of environments and phenotypes. Finally, we need to develop methods to measure phene expression rapidly and economically without artefacts. These challenges, especially mapping the fitness landscape, are non-trivial, and may warrant new research and training modalities.

Saturation of the human phenome

The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits.

Phenome analysis in plant species using loss-of-function and gain-of-function mutants

Analysis of genetic mutations is one of the most effective ways to investigate gene function. We now have methods that allow for mass production of mutant lines and cells in a variety of model species. Recently, large numbers of mutant lines have been generated by both 'loss-of-function' and 'gain-of-function' techniques. In parallel, phenotypic information covering various mutant resources has been acquired and released in web-based databases. As a result, significant progress in comprehensive phenotype analysis is being made through the use of these tools. Arabidopsis and rice are two major model plant species in which genome sequencing projects have been completed. Arabidopsis is the most widely used experimental plant, with a large number of mutant resources and several examples of systematic phenotype analysis. Rice is a major crop species and is used as a model plant, with an increasing number of mutant resources. Other plant species are also being employed in functional genetics research. In this review, the present status of mutant resources for large-scale studies of gene function in plant research and the current perspective on using loss-of-function and gain-of-function mutants in phenome research will be discussed.