Increasing diversity in genomics requires investment in equitable partnerships and capacity building

Recent Advances in Genomic Studies of Gestational Duration and Preterm Birth

Preterm birth (PTB) is the leading cause of infant mortality and morbidity. For several decades, extensive epidemiologic and genetic studies have highlighted the significant contribution of maternal and offspring genetic factors to PTB. This review discusses the challenges inherent in conventional genomic analyses of PTB and underscores the importance of adopting nonconventional approaches, such as analyzing the mother-child pair as a single analytical unit, to disentangle the intertwined maternal and fetal genetic influences. We elaborate on studies investigating PTB phenotypes through 3 levels of genetic analyses: single-variant, multi-variant, and genome-wide variants.

Imputation accuracy across global human populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n = 1,061), Vietnamese (n = 1,264), Thai (n = 2,435), and Papua New Guineans (n = 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.

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.

Childhood maltreatment and health in the UK Biobank: triangulation of outcome-wide and polygenic risk score analyses

BACKGROUND

Childhood maltreatment is common globally and impacts morbidity, mortality, and well-being. Our understanding of its impact is constrained by key substantive and methodological limitations of extant research, including understudied physical health outcomes and bias due to unmeasured confounding. We address these limitations through a large-scale outcome-wide triangulation study.

METHODS

We performed two outcome-wide analyses (OWAs) in the UK Biobank. First, we examined the relationship between self-reported maltreatment exposure (number of maltreatment types, via Childhood Trauma Screener) and 414 outcomes in a sub-sample of 157,316 individuals using generalized linear models ("observational OWA"). Outcomes covered a broad range of health themes including health behaviors, cardiovascular disease, digestive health, socioeconomic status, and pain. Second, we examined the relationship between a polygenic risk score for maltreatment and 298 outcomes in a non-overlapping sample of 243,006 individuals ("genetic OWA"). We triangulated results across OWAs based on differing sources of bias.

RESULTS

Overall, 23.8% of the analytic sample for the observational OWA reported at least one maltreatment type. Of 298 outcomes examined in both OWAs, 25% were significant in both OWAs and concordant in the direction of association. Most of these were considered robust in the observational OWA according to sensitivity analyses and included outcomes such as marital separation (OR from observational OWA, ORo = 1.25 (95% CI: 1.21, 1.29); OR from genetic OWA, ORg = 1.06 (1.03, 1.08)), major diet changes due to illness (ORo = 1.27 (1.24, 1.29); ORg = 1.01 (1.00, 1.03)), certain intestinal diseases (ORo = 1.14 (1.10, 1.18); ORg = 1.03 (1.01, 1.06)), hearing difficulty with background noise (ORo = 1.11 (1.11, 1.12); ORg = 1.01 (1.00, 1.01)), knee arthrosis (ORo = 1.13 (1.09, 1.18); ORg = 1.03 (1.01, 1.05)), frequent sleeplessness (ORo = 1.21 (1.20, 1.23); ORg = 1.02 (1.01, 1.03)), and low household income (ORo = 1.28 (1.26, 1.31); ORg = 1.02 (1.01, 1.03)). Approximately 62% of results were significant in the observational OWA but not the genetic OWA, including numerous cardiovascular outcomes. Only 6 outcomes were significant in the genetic OWA and null in the observational OWA; these included diastolic blood pressure and glaucoma. No outcomes were statistically significant in opposite directions in the two analyses, and 11% were not significant in either OWA.

CONCLUSIONS

Our findings underscore the far-reaching negative effects of childhood maltreatment in later life and the utility of an outcome-wide triangulation design with sensitivity analyses for improving causal inference.

The Challenges and Opportunities for Mental Health Twin Research in Nigeria

The recent interest in increasing diversity in genetic research can be useful in uncovering novel insights into the genetic architecture of mental health disorders - globally and in previously unexplored settings such as low- and middle-income settings like Nigeria. Genetic research into mental health is potentially promising in Nigeria and we reflect on the challenges and opportunities for twin research which may be particularly suited to Nigeria. The higher rates of twinning in Africa and Nigeria specifically, make the twin design an affordable and readily maintainable approach for genetic research in the country. Despite potential challenges with recruitment, data collection, data analysis and dissemination; the success of current efforts suggest that the twin design can tapped even further for greater impact in the country. We highlight some ways in which the scope of twin research can be increased and suggest some ways in which existing challenges can be overcome including recent Patient Participant Involve and Engagement activities.

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits

Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

Genomic Insights into the Shared and Distinct Genetic Architecture of Cognitive Function and Schizophrenia

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, and efforts to understand the biological basis of cognitive dysfunction in the disorder are ongoing. Previous studies have suggested genetic overlap between global cognitive ability and schizophrenia, but further work is needed to delineate the shared genetic architecture. Here, we apply genomic structural equation modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank (UKB). We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). We identified three broad factors (visuo-spatial, verbal analytic and decision/reaction time) that underly the genetic correlations between the UKB cognitive tests. Global genetic correlations showed a significant but moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found evidence of substantial polygenic overlap between each cognitive factor and schizophrenia but show that most loci shared between the latent cognitive factors and schizophrenia have unique patterns of association with the cognitive factors. Biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we find that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptom dimensions. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

Imputation Accuracy Across Global Human Populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.

Causal complexity in human research: On the shared challenges of behavior genetics, medical genetics, and environmentally oriented social science

We received 23 spirited commentaries on our target article from across the disciplines of philosophy, economics, evolutionary genetics, molecular biology, criminology, epidemiology, and law. We organize our reply around three overarching questions: (1) What is a cause? (2) How are randomized controlled trials (RCTs) and within-family genome-wide association studies (GWASs) alike and unalike? (3) Is behavior genetics a qualitatively different enterprise? Throughout our discussion of these questions, we advocate for the idea that behavior genetics shares many of the same pitfalls and promises as environmentally oriented research, medical genetics, and other arenas of the social and behavioral sciences.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Ethical principles, challenges and opportunities when conducting genetic counselling for schizophrenia

Ethical challenges of genetic counselling for schizophrenia include effective communication of critical scientific information in an easily understood manner by patients and relatives, and the ability to ensure communication is unencumbered by medical jargon. Levels of literacy in the target population may limit this process, making it difficult for patients to attain the desired levels of informed consent to make crucial decisions during genetic counselling. Multilingualism in target communities may further complicate such communication. This paper outlines the ethical principles, challenges and opportunities facing clinicians when conducting genetic counselling for schizophrenia and how these might be met, drawing on lessons from South African studies. The paper draws on reflections of clinician and researcher experiences gained from clinical practice or research on the genetics of schizophrenia and psychotic disorders in South Africa. The context of genetic studies in schizophrenia is used to illustrate the ethical challenges in genetic counselling for schizophrenia, both in clinical and research settings. Attention is also drawn to multicultural and multilingual populations, particularly where the preferred language lacks a well-developed scientific language of communication for some of the genetic concepts that have to be presented during the genetic counselling process. The authors describe the ethical challenges and how to address these to empower patients and relatives to make well-informed decisions despite these obstacles. Principles applied by clinicians and researchers during the genetic counselling are described. Potential solutions, including the establishment of community advisory boards to address potential ethical challenges inherent to the genetic counselling process, are also shared. Genetic counselling for schizophrenia still faces ethical challenges which require a balance of principles of beneficence, autonomy, informed consent, confidentiality and distributive justice, while striving to present accuracy in the science that guides the process. Evolution in language and cultural competency therefore needs to occur alongside scientific advances in genetic research. Key stakeholders need to partner and build capacity and expertise in genetic counselling through the provision of funding and resources. The goal of partnerships is to empower patients, relatives, clinicians and researchers to share scientific information in a manner guided by empathy while retaining scientific accuracy.

Polygenic scoring accuracy varies across the genetic ancestry continuum

Polygenic scores (PGSs) have limited portability across different groupings of individuals (for example, by genetic ancestries and/or social determinants of health), preventing their equitable use1-3. PGS portability has typically been assessed using a single aggregate population-level statistic (for example, R2)4, ignoring inter-individual variation within the population. Here, using a large and diverse Los Angeles biobank5 (ATLAS, n = 36,778) along with the UK Biobank6 (UKBB, n = 487,409), we show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries7 in all considered populations, even within traditionally labelled 'homogeneous' genetic ancestries. The decreasing trend is well captured by a continuous measure of genetic distance (GD) from the PGS training data: Pearson correlation of -0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained on individuals labelled as white British in the UKBB to individuals with European ancestries in ATLAS, individuals in the furthest GD decile have 14% lower accuracy relative to the closest decile; notably, the closest GD decile of individuals with Hispanic Latino American ancestries show similar PGS performance to the furthest GD decile of individuals with European ancestries. GD is significantly correlated with PGS estimates themselves for 82 of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestries in PGS interpretation. Our results highlight the need to move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs.

Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility

In this consensus report by a diverse group of academics who conduct and/or are concerned about social and behavioral genomics (SBG) research, the authors recount the often-ugly history of scientific attempts to understand the genetic contributions to human behaviors and social outcomes. They then describe what the current science-including genomewide association studies and polygenic indexes-can and cannot tell us, as well as its risks and potential benefits. They conclude with a discussion of responsible behavior in the context of SBG research. SBG research that compares individuals within a group according to a "sensitive" phenotype requires extra attention to responsible conduct and to responsible communication about the research and its findings. SBG research (1) on sensitive phenotypes that (2) compares two or more groups defined by (a) race, (b) ethnicity, or (c) genetic ancestry (where genetic ancestry could easily be misunderstood as race or ethnicity) requires a compelling justification to be conducted, funded, or published. All authors agree that this justification at least requires a convincing argument that a study's design could yield scientifically valid results; some authors would additionally require the study to have a socially favorable risk-benefit profile.

15 years of GWAS discovery: Realizing the promise

It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.

Addressing diversity and inclusion challenges in global neuro-psychiatric and behavioral genomics research

Advancements in neuro-psychiatric and behavioral genomics offer significant opportunities for better understanding the human brain, behavior and associated disorders. Such advancements may help us prevent, manage and/or cure complex conditions. The serious challenge confronted by these disciplines however is diversity. Both fields lack diversity in terms of genomic reference datasets needed for discovery research, engagement of diverse communities in translational research and in terms of diverse and multidisciplinary scientific teams. This is a challenge because diversity is needed on all levels in order to increase representation and inclusion of all populations across the globe as we move research activities forward. The lack of diversity can translate to an inability to use scientific innovations from these fields for the benefit of all people everywhere and signifies a missed opportunity to address pervasive global health inequities. In this commentary we identify three persistent barriers to reaching diversity targets while focusing on discovery and translational science. Additionally, we propose four suggestions on how to advance efforts and rapidly move towards achieving diversity and inclusion in neuro-psychiatric and behavioral genomics. Without systematically addressing the diversity gap within these fields, the benefits of the science may not be relevant and accessible to all people.

Perceptions, attitudes, and willingness of the public in low- and middle-income countries of the Arab region to participate in biobank research

Population-based genomics studies have proven successful in identifying genetic variants associated with diseases. High-quality biospecimens linked with informative health data from diverse segments of the population have made such research possible. However, the success of biobank research depends on the willingness of the public to participate in this type of research. We aimed to explore the factors associated with the willingness of the public to participate in biobank research from four low- and middle-income countries in the Arab region (Egypt, Jordan, Morocco, and Sudan). We used a previously validated questionnaire to assess several constructs that included the public's perceptions, attitudes, and willingness to participate in biobank research. We recruited 967 participants. More than half did not have prior awareness of biobanks. Participants' willingness to donate biospecimens and health data was less than 10%. Our results also showed that participants harbored concerns with trust, privacy, and with data-sharing involving international researchers. Predictors of willingness to participate in biobank research included no previous involvement in research and positive attitudes toward biobanks. Finally, our study showed several differences between the four countries regarding several of the investigated constructs. We conclude there should be additional efforts to raise public awareness and enhance perceptions of the public in biobanking research to enhance trust. We further recommend qualitative research to explore the underlying factors that contribute to the public's concerns with international data sharing that would enhance global health.