A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity.

Glioblastomas exhibit vast inter- and intra-tumoral heterogeneity, complicating the development of effective therapeutic strategies. Current in vitro models are limited in preserving the cellular and mutational diversity of parental tumors and require a prolonged generation time. Here, we report methods for generating and biobanking patient-derived glioblastoma organoids (GBOs) that recapitulate the histological features, cellular diversity, gene expression, and mutational profiles of their corresponding parental tumors. GBOs can be generated quickly with high reliability and exhibit rapid, aggressive infiltration when transplanted into adult rodent brains. We further demonstrate the utility of GBOs to test personalized therapies by correlating GBO mutational profiles with responses to specific drugs and by modeling chimeric antigen receptor T cell immunotherapy. Our studies show that GBOs maintain many key features of glioblastomas and can be rapidly deployed to investigate patient-specific treatment strategies. Additionally, our live biobank establishes a rich resource for basic and translational glioblastoma research.

Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

Screening Human Embryos for Polygenic Traits Has Limited Utility.

The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.

A Platform for Generation of Chamber-Specific Cardiac Tissues and Disease Modeling.

Tissue engineering using cardiomyocytes derived from human pluripotent stem cells holds a promise to revolutionize drug discovery, but only if limitations related to cardiac chamber specification and platform versatility can be overcome. We describe here a scalable tissue-cultivation platform that is cell source agnostic and enables drug testing under electrical pacing. The plastic platform enabled on-line noninvasive recording of passive tension, active force, contractile dynamics, and Ca2+ transients, as well as endpoint assessments of action potentials and conduction velocity. By combining directed cell differentiation with electrical field conditioning, we engineered electrophysiologically distinct atrial and ventricular tissues with chamber-specific drug responses and gene expression. We report, for the first time, engineering of heteropolar cardiac tissues containing distinct atrial and ventricular ends, and we demonstrate their spatially confined responses to serotonin and ranolazine. Uniquely, electrical conditioning for up to 8 months enabled modeling of polygenic left ventricular hypertrophy starting from patient cells.

Genomic Analyses from Non-invasive Prenatal Testing Reveal Genetic Associations, Patterns of Viral Infections, and Chinese Population History.

We analyze whole-genome sequencing data from 141,431 Chinese women generated for non-invasive prenatal testing (NIPT). We use these data to characterize the population genetic structure and to investigate genetic associations with maternal and infectious traits. We show that the present day distribution of alleles is a function of both ancient migration and very recent population movements. We reveal novel phenotype-genotype associations, including several replicated associations with height and BMI, an association between maternal age and EMB, and between twin pregnancy and NRG1. Finally, we identify a unique pattern of circulating viral DNA in plasma with high prevalence of hepatitis B and other clinically relevant maternal infections. A GWAS for viral infections identifies an exceptionally strong association between integrated herpesvirus 6 and MOV10L1, which affects piwi-interacting RNA (piRNA) processing and PIWI protein function. These findings demonstrate the great value and potential of accumulating NIPT data for worldwide medical and genetic analyses.

Genetic testing in prostate cancer management: Considerations informing primary care.

Inherited genetic mutations can significantly increase the risk for prostate cancer (PC), may be associated with aggressive disease and poorer outcomes, and can have hereditary cancer implications for men and their families. Germline genetic testing (hereditary cancer genetic testing) is now strongly recommended for patients with advanced/metastatic PC, particularly given the impact on targeted therapy selection or clinical trial options, with expanded National Comprehensive Cancer Network guidelines and endorsement from multiple professional societies. Furthermore, National Comprehensive Cancer Network guidelines recommend genetic testing for men with PC across the stage and risk spectrum and for unaffected men at high risk for PC based on family history to identify hereditary cancer risk. Primary care is a critical field in which providers evaluate men at an elevated risk for PC, men living with PC, and PC survivors for whom germline testing may be indicated. Therefore, there is a critical need to engage and educate primary care providers regarding the role of genetic testing and the impact of results on PC screening, treatment, and cascade testing for family members of affected men. This review highlights key aspects of genetic testing in PC, the role of clinicians, with a focus on primary care, the importance of obtaining a comprehensive family history, current germline testing guidelines, and the impact on precision PC care. With emerging evidence and guidelines, clinical pathways are needed to facilitate integrated genetic education, testing, and counseling services in appropriately selected patients. There is also a need for providers to understand the field of genetic counseling and how best to collaborate to enhance multidisciplinary patient care.

Trials and tribulations of statistical significance in biochemistry and omics.

Over recent years many statisticians and researchers have highlighted that statistical inference would benefit from a better use and understanding of hypothesis testing, p-values, and statistical significance. We highlight three recommendations in the context of biochemical sciences. First recommendation: to improve the biological interpretation of biochemical data, do not use p-values (or similar test statistics) as thresholded values to select biomolecules. Second recommendation: to improve comparison among studies and to achieve robust knowledge, perform complete reporting of data. Third recommendation: statistical analyses should be reported completely with exact numbers (not as asterisks or inequalities). Owing to the high number of variables, a better use of statistics is of special importance in omic studies.

The impact of clinical genome sequencing in a global population with suspected rare genetic disease.

There is mounting evidence of the value of clinical genome sequencing (cGS) in individuals with suspected rare genetic disease (RGD), but cGS performance and impact on clinical care in a diverse population drawn from both high-income countries (HICs) and low- and middle-income countries (LMICs) has not been investigated. The iHope program, a philanthropic cGS initiative, established a network of 24 clinical sites in eight countries through which it provided cGS to individuals with signs or symptoms of an RGD and constrained access to molecular testing. A total of 1,004 individuals (median age, 6.5 years; 53.5% male) with diverse ancestral backgrounds (51.8% non-majority European) were assessed from June 2016 to September 2021. The diagnostic yield of cGS was 41.4% (416/1,004), with individuals from LMIC sites 1.7 times more likely to receive a positive test result compared to HIC sites (LMIC 56.5% [195/345] vs. HIC 33.5% [221/659], OR 2.6, 95% CI 1.9-3.4, p < 0.0001). A change in diagnostic evaluation occurred in 76.9% (514/668) of individuals. Change of management, inclusive of specialty referrals, imaging and testing, therapeutic interventions, and palliative care, was reported in 41.4% (285/694) of individuals, which increased to 69.2% (480/694) when genetic counseling and avoidance of additional testing were also included. Individuals from LMIC sites were as likely as their HIC counterparts to experience a change in diagnostic evaluation (OR 6.1, 95% CI 1.1-∞, p = 0.05) and change of management (OR 0.9, 95% CI 0.5-1.3, p = 0.49). Increased access to genomic testing may support diagnostic equity and the reduction of global health care disparities.

Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds.

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.

Inferring COVID-19 testing and vaccination behavior from New Jersey testing data.

Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic's course may feed back to affect one's likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals' test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination.

Model selection over partially ordered sets.

In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.

Identifying parental and cell-division origins of aneuploidy in the human blastocyst.

Preimplantation genetic testing commonly employs simplistic copy-number analyses to screen for aneuploidy in blastocyst trophectoderm biopsies. Interpreting intermediate copy number alone as evidence of mosaicism has led to suboptimal estimation of its prevalence. Because mosaicism originates from mitotic nondisjunction, utilizing SNP microarray technology to identify the cell-division origins of aneuploidy might provide a more accurate estimation of its prevalence. The present study develops and validates a method of determining the cell-division origin of aneuploidy in the human blastocyst by using both genotyping and copy-number data in parallel. The concordance of predicted origins with expected results was demonstrated in a series of truth models (99%-100%). This included determination of X chromosome origins from a subset of normal male embryos, determination of the origins of translocation chromosome-related imbalances via embryos from couples with structural rearrangements, and prediction of either mitotic or meiotic origins via multiple rebiopsies of embryos with aneuploidy. In a cohort of blastocysts with parental DNA (n = 2,277), 71% were euploid, 27% were meiotic aneuploid, and 2% were mitotic aneuploid, indicating a low frequency of bona fide mosaicism in the human blastocyst (mean maternal age: 34.4). Chromosome-specific trisomies in the blastocyst were also consistent with observations previously established in products of conception. The ability to accurately identify mitotic-origin aneuploidy in the blastocyst could benefit and better inform individuals whose IVF cycle results in all aneuploid embryos. Clinical trials with this methodology might also help provide a definitive answer regarding the reproductive potential of bona fide mosaic embryos.

Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon.

The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics.

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic results disclosure in diverse families.

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

An allelic-series rare-variant association test for candidate-gene discovery.

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Identifying risk variants for embryo aneuploidy using ultra-low coverage whole-genome sequencing from preimplantation genetic testing.

Aneuploidy frequently arises during human meiosis and is the primary cause of early miscarriage and in vitro fertilization (IVF) failure. Individuals undergoing IVF exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using next-generation sequencing is a standard test for identifying and selecting IVF-derived euploid embryos. The wealth of embryo aneuploidy data and ultra-low coverage whole-genome sequencing (ulc-WGS) data from PGT-A have the potential to discover variants in parental genomes that are associated with aneuploidy risk in their embryos. Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in embryo genomes. We then used the imputed variants and embryo aneuploidy calls to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system. We identified one locus on chromosome 3 that is significantly associated with meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice. Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that has potential to be leveraged for similar association studies that use ulc-WGS data.

Genetic insights into the age-specific biological mechanisms governing human ovarian aging.

There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.

MoCoLo: a testing framework for motif co-localization.

Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.