Getting genetic ancestry right for science and society

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

Background

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations.

Methods

We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry.

Results

Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU.

Conclusion

Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

Germline variation contributes to false negatives in CRISPR-based experiments with varying burden across ancestries

Reducing disparities is vital for equitable access to precision treatments in cancer. Socioenvironmental factors are a major driver of disparities, but differences in genetic variation likely also contribute. The impact of genetic ancestry on prioritization of cancer targets in drug discovery pipelines has not been systematically explored due to the absence of pre-clinical data at the appropriate scale. Here, we analyze data from 611 genome-scale CRISPR/Cas9 viability experiments in human cell line models to identify ancestry-associated genetic dependencies essential for cell survival. Surprisingly, we find that most putative associations between ancestry and dependency arise from artifacts related to germline variants. Our analysis suggests that for 1.2-2.5% of guides, germline variants in sgRNA targeting sequences reduce cutting by the CRISPR/Cas9 nuclease, disproportionately affecting cell models derived from individuals of recent African descent. We propose three approaches to mitigate this experimental bias, enabling the scientific community to address these disparities.

Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.

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.

Towards fair and clinically relevant polygenic predictions

Lennon et al. recently proposed a clinical polygenic score (PGS) pipeline as part of the Electronic Medical Records and Genomics (eMERGE) network initiative. In this spotlight article we discuss the broader context for the use of PGS in preventive medicine and highlight key limitations and challenges facing their inclusion in prediction models.

Association between MCU Gene Polymorphisms with Obesity: Findings from the All of Us Research Program

Obesity is a public health crisis, and its prevalence disproportionately affects African Americans in the United States. Dysregulation of organelle calcium homeostasis is associated with obesity. The mitochondrial calcium uniporter (MCU) complex is primarily responsible for mitochondrial calcium homeostasis. Obesity is a multifactorial disease in which genetic underpinnings such as single-nucleotide polymorphisms (SNPs) may contribute to disease progression. The objective of this study was to identify genetic variations of MCU with anthropometric measurements and obesity in the All of Us Research Program.

METHODS

We used an additive genetic model to assess the association between obesity traits (body mass index (BMI), waist and hip circumference) and selected MCU SNPs in 19,325 participants (3221 normal weight and 16,104 obese). Eleven common MCU SNPs with a minor allele frequency ≥ 5% were used for analysis.

RESULTS

We observed three MCU SNPs in self-reported Black/African American (B/AA) men, and six MCU SNPs in B/AA women associated with increased risk of obesity, whereas six MCU SNPs in White men, and nine MCU SNPs in White women were protective against obesity development.

CONCLUSIONS

This study found associations of MCU SNPs with obesity, providing evidence of a potential predictor of obesity susceptibility in B/AA adults.

[Taking origins into account in medical reasoning in infectious and tropical diseases? A critical look]

Healthcare discriminations based on one's ethnic background is increasingly being studied in medicine. The scale of the Covid-19 pandemic has played an important role in bringing them to light. Data, although scarce, exist in France. These discriminations have an impact on the care pathway and contribute to the renunciation of care by the most affected populations. The issue of discrimination is particularly relevant in infectious diseases. Although the epidemiology of infectious diseases is unevenly distributed worldwide, erroneous social representations are prevalent and expose to a harmful prejudice against migrants with regard to infectious diseases. The transmissible nature of some infectious diseases reinforces their stigmatizing potential. In this context, it seems important to discuss the dimension to be given to social determinants, geographical origin, phenotype, and ethnicity in teaching and medical reasoning. The English-speaking world uses the concept of "race" in a structural way, whereas this "international standard" has not been applied in France until now. To improve the care of people from minority groups, it seems important to better document and teach a more nuanced clinical reasoning based on origin, without neglecting the importance of collecting and taking into account social determinants of health and environmental factors.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Genetic and molecular architecture of complex traits

Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.

The sociopolitical in human genetics education

Education must go beyond only countering essentialist and deterministic views of genetics.

Typological thinking in human genomics research contributes to the production and prominence of scientific racism

Public genomic datasets like the 1000 Genomes project (1KGP), Human Genome Diversity Project (HGDP), and the Adolescent Brain Cognitive Development (ABCD) study are valuable public resources that facilitate scientific advancements in biology and enhance the scientific and economic impact of federally funded research projects. Regrettably, these datasets have often been developed and studied in ways that propagate outdated racialized and typological thinking, leading to fallacious reasoning among some readers that social and health disparities among the so-called races are due in part to innate biological differences between them. We highlight how this framing has set the stage for the racist exploitation of these datasets in two ways: First, we discuss the use of public biomedical datasets in studies that claim support for innate genetic differences in intelligence and other social outcomes between the groups identified as races. We further highlight recent instances of this which involve unauthorized access, use, and dissemination of public datasets. Second, we discuss the memification, use of simple figures meant for quick dissemination among lay audiences, of population genetic data to argue for a biological basis for purported human racial groups. We close with recommendations for scientists, to preempt the exploitation and misuse of their data, and for funding agencies, to better enforce violations of data use agreements.

Hunter-gatherer genetics research: Importance and avenues

Major developments in the field of genetics in the past few decades have revolutionised notions of what it means to be human. Although currently only a few populations around the world practise a hunting and gathering lifestyle, this mode of subsistence has characterised members of our species since its very origins and allowed us to migrate across the planet. Therefore, the geographical distribution of hunter-gatherer populations, dependence on local ecosystems and connections to past populations and neighbouring groups have provided unique insights into our evolutionary origins. However, given the vulnerable status of hunter-gatherers worldwide, the development of the field of anthropological genetics requires that we reevaluate how we conduct research with these communities. Here, we review how the inclusion of hunter-gatherer populations in genetics studies has advanced our understanding of human origins, ancient population migrations and interactions as well as phenotypic adaptations and adaptability to different environments, and the important scientific and medical applications of these advancements. At the same time, we highlight the necessity to address yet unresolved questions and identify areas in which the field may benefit from improvements.

The use of race terms in epigenetics research: considerations moving forward

The field of environmental epigenetics is uniquely suited to investigate biologic mechanisms that have the potential to link stressors to health disparities. However, it is common practice in basic epigenetic research to treat race as a covariable in large data analyses in a way that can perpetuate harmful biases without providing any biologic insight. In this article, we i) propose that epigenetic researchers open a dialogue about how and why race is employed in study designs and think critically about how this might perpetuate harmful biases; ii) call for interdisciplinary conversation and collaboration between epigeneticists and social scientists to promote the collection of more detailed social metrics, particularly institutional and structural metrics such as levels of discrimination that could improve our understanding of individual health outcomes; iii) encourage the development of standards and practices that promote full transparency about data collection methods, particularly with regard to race; and iv) encourage the field of epigenetics to continue to investigate how social structures contribute to biological health disparities, with a particular focus on the influence that structural racism may have in driving these health disparities.

Titin-Truncating variants Predispose to Dilated Cardiomyopathy in Diverse Populations

Importance

The effect of high percentage spliced in (hiPSI) TTN truncating variants (TTNtvs) on risk of dilated cardiomyopathy (DCM) has historically been studied among population subgroups defined by genetic similarity to European reference populations. This has raised questions about the effect of TTNtvs in diverse populations, especially among individuals genetically similar to African reference populations.

Objective

To determine the effect of TTNtvs on risk of DCM in diverse population as measured by genetic distance (GD) in principal component (PC) space.

Design

Cohort study.

Setting

Penn Medicine Biobank (PMBB) is a large, diverse biobank.

Participants

Participants were recruited from across the Penn Medicine healthcare system and volunteered to have their electronic health records linked to biospecimen data including DNA which has undergone whole exome sequencing.

Main Outcomes and Measures

Risk of DCM among individuals carrying a hiPSI TTNtv.

Results

Carrying a hiPSI TTNtv was associated with DCM among PMBB participants across a range of GD deciles from the 1000G European centroid; the effect estimates ranged from odds ratio (OR) = 3.29 (95% confidence interval [CI] 1.26 to 8.56) to OR = 9.39 (95% CI 3.82 to 23.13). When individuals were assigned to population subgroups based on genetic similarity to the 1000G reference populations, hiPSI TTNtvs conferred significant risk of DCM among those genetically similar to the 1000G European reference population (OR = 7.55, 95% CI 4.99 to 11.42, P<0.001) and individuals genetically similar to the 1000G African reference population (OR 3.50, 95% CI 1.48 to 8.24, P=0.004).

Conclusions and Relevance

TTNtvs are associated with increased risk of DCM among a diverse cohort. There is no significant difference in effect of TTNtvs on DCM risk across deciles of GD from the 1000G European centroid, suggesting genetic background should not be considered when screening individuals for titin-related DCM.

Conflating race and ancestry: Tracing decision points about population descriptors over the precision medicine research life course

Responding to calls for human genomics to shift away from the use of race, genomic investigators are coalescing around the possibility of using genetic ancestry. This shift has renewed questions about the use of social and genetic concepts of difference in precision medicine research (PMR). Drawing from qualitative data on five PMR projects, we illustrate negotiations within and between research teams as genomic investigators deliberate on the relevance of race and genetic ancestry for different analyses and contexts. We highlight how concepts of both social and genetic difference are embedded within and travel through research practices, and identify multiple points across the research life course at which conceptual slippage and conflation between race and genetic ancestry occur. We argue that moving beyond race will require PMR investigators to confront the entrenched ways in which race is built into research practices and biomedical infrastructures.

Principles and methods for transferring polygenic risk scores across global populations

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.

Open Science Practices in Psychiatric Genetics: A Primer

Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.

The genomic footprint of social stratification in admixing American populations

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European, and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification, shaped by socially constructed racial and gender hierarchies, has constrained the admixture processes in the Americas since the European colonization and the subsequent Atlantic slave trade.

Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments

Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.

The application of epiphenotyping approaches to DNA methylation array studies of the human placenta

BACKGROUND

Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied.

RESULTS

Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry-informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, as well as over very long placental processing times. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component.

CONCLUSIONS

This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. We further demonstrate the specific utility of epiphenotyping tools developed for use with placental DNAme data, and show that these variables (i) provide an independent check of clinically obtained data and (ii) provide a robust approach to compare variables across different datasets. Finally, we present a general framework for the processing and analysis of placental DNAme data, integrating the epiphenotype variables discussed here.

Riding the merry-go-round of racial disparities in ADRD research

INTRODUCTION

With the rapid expansion of the aging population, the burden of Alzheimer's disease related dementias (ADRD) is anticipated to increase in racialized and minoritized groups who are at disproportionately higher risk. To date, research emphasis has been on further characterizing the existence of racial disparities in ADRD through comparisons to groups racialized as White that are assumed to be normative. Much of the literature on this comparison insinuates that racialized and minoritized groups experience poorer outcomes due to genetics, culture, and/or health behaviors.

METHODS

This perspective shines a light on a category of ADRD research that employs ahistorical methodological approaches to describe racial disparities in ADRD that puts us on a merry-go-round of research with no benefits to society.

METHODS

This commentary provides historical context for the use of race in ADRD research and justification for the study of structural racism. The commentary concludes with recommendations to guide future research.

Mexican Biobank advances population and medical genomics of diverse ancestries

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

Population Pharmacogenomics for Health Equity

Health equity means the opportunity for all people and populations to attain optimal health, and it requires intentional efforts to promote fairness in patient treatments and outcomes. Pharmacogenomic variants are genetic differences associated with how patients respond to medications, and their presence can inform treatment decisions. In this perspective, we contend that the study of pharmacogenomic variation within and between human populations-population pharmacogenomics-can and should be leveraged in support of health equity. The key observation in support of this contention is that racial and ethnic groups exhibit pronounced differences in the frequencies of numerous pharmacogenomic variants, with direct implications for clinical practice. The use of race and ethnicity to stratify pharmacogenomic risk provides a means to avoid potential harm caused by biases introduced when treatment regimens do not consider genetic differences between population groups, particularly when majority group genetic profiles are assumed to hold for minority groups. We focus on the mitigation of adverse drug reactions as an area where population pharmacogenomics can have a direct and immediate impact on public health.

Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity

Forensic investigation of DNA samples from multiple contributors has become commonplace. These complex analyses use statistical frameworks accounting for multiple levels of uncertainty in allelic contributions from different individuals, particularly for samples containing few molecules of DNA. These methods have been thoroughly tested along some axes of variation, but less attention has been paid to accuracy across human genetic variation. Here, we quantify the accuracy of DNA mixture analysis over 244 human groups. We find higher false inclusion rates for mixtures with more contributors, and for groups with lower genetic diversity. Even for two-contributor mixtures where one contributor is known and the reference group is correctly specified, false inclusion rates are 1e-5 or higher for 56 out of 244 groups. This means that, depending on multiple testing, some false inclusions may be expected. These false positives could be lessened with more selective and conservative use of DNA mixture analysis.

Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.

The Pitfalls of Genomic Data Diversity

Biomedical research recruitment today focuses on including participants representative of global genetic variation-rightfully so. But ethnographic attention to practices of inclusion highlights how this agenda often transforms into "predatory inclusion," simplistic pushes to get Black and brown people into genomic databases. As anthropologists of medicine, we argue that the question of how to get from diverse data to concrete benefit for people who are marginalized cannot be presumed to work itself out as a byproduct of diverse datasets. To actualize the equitable translation of genomics, practitioners need to place the impacts of ancestral genetic difference in the scope of much more impactful social determinants. For this to happen, multidisciplinary expertise needs to be leveraged, and current, structurally unequal health care systems ultimately need to transform. As modest steps toward this goal, new models for benefit-sharing must be developed and implemented to mitigate existing inequality between data donors and the entities profiting from that data.

Analysis of Sex-Specific Gene-by-Cohort and Genetic Correlation-by-Cohort Interaction in Educational and Reproductive Outcomes Using the UK Biobank Data

Synthesizing prior gene-by-cohort (G×C) interaction studies, we theorize that changes in genetic effects by social conditions depend on the level of resource constraints, the distribution and use of resources, structural constraints, and constraints on individual choice. Motivated by the theory, we explored several sex-specific G×C trends across a set of outcomes using 30 birth cohorts of UK Biobank data (N = 400,000). We find that genetic coefficients on years of schooling and secondary educational attainment substantially decrease, but genetic coefficients on college attainments only moderately increase. On the other hand, genetic coefficients for education ranks are stable. Genetic coefficients on reproductive behavior increase for younger cohorts. Additional genetic-correlation-by-cohort analysis shows shifting genetic correlations between education and reproductive behavior. Our results suggest that the G×C patterns are highly heterogenous and that social and genetic factors jointly shape the diversity of human phenotypes.

Genetics of Alzheimer's Disease in the African American Population

Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.

The genetic legacy of African Americans from Catoctin Furnace

Few African Americans have been able to trace family lineages back to ancestors who died before the 1870 United States Census, the first in which all Black people were listed by name. We analyzed 27 individuals from Maryland's Catoctin Furnace African American Cemetery (1774-1850), identifying 41,799 genetic relatives among consenting research participants in 23andMe, Inc.'s genetic database. One of the highest concentrations of close relatives is in Maryland, suggesting that descendants of the Catoctin individuals remain in the area. We find that many of the Catoctin individuals derived African ancestry from the Wolof or Kongo groups and European ancestry from Great Britain and Ireland. This study demonstrates the power of joint analysis of historical DNA and large datasets generated through direct-to-consumer ancestry testing.

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1-8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address this significant gap, we present PhyloFrame, the first-ever machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating big data tissue-specific functional interaction networks, global population variation data, and disease-relevant transcriptomic data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. These results demonstrate how AI can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Race, Ethnicity, and Pharmacogenomic Variation in the United States and the United Kingdom

The relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation-6311 drug response-associated variants for All of Us and 5966 variants for UKB-were analyzed with machine learning classifiers to predict participants' self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

Disease risk and healthcare utilization among ancestrally diverse groups in the Los Angeles region

An individual's disease risk is affected by the populations that they belong to, due to shared genetics and environmental factors. The study of fine-scale populations in clinical care is important for identifying and reducing health disparities and for developing personalized interventions. To assess patterns of clinical diagnoses and healthcare utilization by fine-scale populations, we leveraged genetic data and electronic medical records from 35,968 patients as part of the UCLA ATLAS Community Health Initiative. We defined clusters of individuals using identity by descent, a form of genetic relatedness that utilizes shared genomic segments arising due to a common ancestor. In total, we identified 376 clusters, including clusters with patients of Afro-Caribbean, Puerto Rican, Lebanese Christian, Iranian Jewish and Gujarati ancestry. Our analysis uncovered 1,218 significant associations between disease diagnoses and clusters and 124 significant associations with specialty visits. We also examined the distribution of pathogenic alleles and found 189 significant alleles at elevated frequency in particular clusters, including many that are not regularly included in population screening efforts. Overall, this work progresses the understanding of health in understudied communities and can provide the foundation for further study into health inequities.

The application of epiphenotyping approaches to DNA methylation array studies of the human placenta

Background : Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied. Results : Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, but reassuringly were robust to placental processing time. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component. Conclusions : This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. Further, we demonstrate that estimating epiphenotype variables from the DNAme data itself, when possible, provides both an independent check of clinically-obtained data and can provide a robust approach to compare variables across different datasets.

Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program

Importance

Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation.

Objective

To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population.

Design, Setting, and Participants

This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023.

Exposures

PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status.

Main Outcomes and Measures

Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events.

Results

A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%).

Conclusions and Relevance

Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.

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.

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits

Genome-wide association studies have led to the identification of robust statistical associations of genetic variants with numerous brain-related traits, including neurological and psychiatric conditions, and psychological and behavioral measures. These results may provide insight into the biology underlying these traits and may facilitate clinically useful predictions. However, these results also carry the risk of harm, including possible negative effects of inaccurate predictions, violations of privacy, stigma and genomic discrimination, raising serious ethical and legal implications. Here, we discuss ethical concerns surrounding the results of genome-wide association studies for individuals, society and researchers. Given the success of genome-wide association studies and the increasing availability of nonclinical genomic prediction technologies, better laws and guidelines are urgently needed to regulate the storage, processing and responsible use of genetic data. Also, researchers should be aware of possible misuse of their results, and we provide guidance to help avoid such negative impacts on individuals and society.

Effect of XRCC1 Arg399Gln Gene Polymorphism on Survival in Lymphoblastic Leukemia

INTRODUCTION

The relevance of the research of the article is conditioned upon the problem of the development of molecular genetic diagnostics to determine the effectiveness of treatment for acute lymphoblastic leukemia in children. The purpose of the article is to identify the polymorphism parameters of the P53 Arg72Pro and XRCC1 Arg399Gln genes in acute lymphoblastic leukemia with criteria for determining the survival rates of sick children.

MATERIALS AND METHODS

Methods for the study of the identified problem are the study of the medical histories of children with acute leukemia, which allowed selection of the necessary contingent of patients for further genetic study of their frozen blood, where the genomic part of deoxyribonucleic acid was isolated from the frozen blood in a standard way using molecular biological research when performing a polymerase chain reaction.

RESULTS

The article presents the results of a study that shows that in children with acute lymphoblastic leukemia, the frequency of genotypes of the XRCC1 Arg399Gln gene is variable. The most common genotypes are Arg/Gln and Arg/Arg, approximately 48% each. The Gln/Gln genotype is less common. Relapse-free survival of children with the Arg/Gln and Gln/Gln genotypes was the highest, slightly lower rates were noted with the Arg/Arg genotype.

CONCLUSION

It was identified that the frequency of genotypes of the XRCC1 Arg399Gln gene can be a predictor of prognosis in acute lymphocytic leukemia in children, which can be considered when choosing treatment tactics, and this has practical significance for the field of medicine.

Guidelines for genetic ancestry inference created through roundtable discussions

The use of genetic and genomic technology to infer ancestry is commonplace in a variety of contexts, particularly in biomedical research and for direct-to-consumer genetic testing. In 2013 and 2015, two roundtables engaged a diverse group of stakeholders toward the development of guidelines for inferring genetic ancestry in academia and industry. This report shares the stakeholder groups' work and provides an analysis of, commentary on, and views from the groundbreaking and sustained dialogue. We describe the engagement processes and the stakeholder groups' resulting statements and proposed guidelines. The guidelines focus on five key areas: application of genetic ancestry inference, assumptions and confidence/laboratory and statistical methods, terminology and population identifiers, impact on individuals and groups, and communication or translation of genetic ancestry inferences. We delineate the terms and limitations of the guidelines and discuss their critical role in advancing the development and implementation of best practices for inferring genetic ancestry and reporting the results. These efforts should inform both governmental regulation and self-regulation.

Genetic Ancestry of 1127 Brazilian Breast Cancer Patients and Its Correlation With Molecular Subtype and Geographic Region

PURPOSE

Breast cancer molecular subtypes show significant differences in different ethnic groups in the United States, but no study has evaluated genetic ancestry in breast cancer in Brazilian women.

METHODS

Breast cancer patients from distinct parts of Brazil were evaluated. Molecular subtypes were determined by immunohistochemistry. Genetic ancestry was evaluated using a panel of 46 AIMs (ancestry informative markers), which classified genetic ancestry as European, African, Asian, and Amerindian. PCR products were subjected to capillary electrophoresis and analyzed using GeneMapper 4.0 software. Ancestry was evaluated with Structure v.2.3.3 software. Ancestry was tested for correlations with geographic region and molecular subtype. The chi-square test and ANOVA with Bonferroni adjustment were applied.

RESULTS

Genetic ancestry and clinical data were evaluated in 1127 patients. Higher rates of self-reported white ethnicity, European ancestry, and HER-2^- luminal tumors were identified in the South region, which may influence age at diagnosis and result in a higher rate of early tumors. Conversely, higher rates of African ancestry in the North and Northeast regions, self-reported nonwhite ethnicity, HER-2⁺ tumors, and triple-negative tumors were noted. Triple-negative and HER-2⁺ tumors were associated with higher advanced and metastatic disease rates at diagnosis, with triple-negative tumors being more frequent in young women.

CONCLUSION

Differences in genetic ancestry, self-reported ethnicity, and molecular subtype were found between Brazilian demographic regions. Knowledge of these features may contribute to a better understanding of age at diagnosis and the molecular distribution of breast cancer in Brazil.

Research Silos in Cancer Disparities: Obstacles to Improving Clinical Outcomes for Underserved Patient Populations

Despite much vaunted progress in cancer therapeutics and diagnostics, outcomes for many groups of non-White patients with cancer remain worse than those for their White compatriots. One reason for this is the lack of inclusion and representation of non-White patients in clinical trials, preclinical datasets, and among researchers, a shortfall that is gaining wide recognition within the cancer research community and the lay public. Several reviews and editorials have commented on the negative impacts of the status quo on progress in cancer research toward medical breakthroughs that help all communities and not just White patients with cancer. In this perspective, we describe the existence of research silos focused either on the impact of socioeconomic factors proceeding from systemic racism on cancer outcomes, or on genetic ancestry as it affects the molecular biology of cancer developing in specific patient populations. While both these research areas are critical for progress toward precision medicine equity, breaking down these silos will help us gain an integrated understanding of how race and racism impact cancer development, progression, and patient outcomes. Bringing this comprehensive approach to cancer disparities research will undoubtedly improve our overall understanding of how stress and environmental factors affect the molecular biology of cancer, which will lead to the development of new diagnostics and therapeutics that are applicable across cancer patient demographics.

The complex genetic architecture of Alzheimer's disease: novel insights and future directions

BACKGROUND

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disorder and the most common form of dementia. AD is highly heritable, with heritability estimates of ∼70% from twin studies. Progressively larger genome-wide association studies (GWAS) have continued to expand our knowledge of AD/dementia genetic architecture. Until recently these efforts had identified 39 disease susceptibility loci in European ancestry populations.

RECENT DEVELOPMENTS

Two new AD/dementia GWAS have dramatically expanded the sample sizes and the number of disease susceptibility loci. The first increased total sample size to 1,126,563-with an effective sample size of 332,376-by predominantly including new biobank and population-based dementia datasets. The second, expands on an earlier GWAS from the International Genomics of Alzheimer's Project (IGAP) by increasing the number of clinically-defined AD cases/controls in addition to incorporating biobank dementia datasets, resulting in a total sample size to 788,989 and an effective sample size of 382,472. Collectively both GWAS identified 90 independent variants across 75 AD/dementia susceptibility loci, including 42 novel loci. Pathway analyses indicate the susceptibility loci are enriched for genes involved in amyloid plaque and neurofibrillary tangle formation, cholesterol metabolism, endocytosis/phagocytosis, and the innate immune system. Gene prioritization efforts for the novel loci identified 62 candidate causal genes. Many of the candidate genes from known and newly discovered loci play key roles in macrophages and highlight phagocytic clearance of cholesterol-rich brain tissue debris by microglia (efferocytosis) as a core pathogenetic hub and putative therapeutic target for AD. WHERE NEXT?: While GWAS in European ancestry populations have substantially enhanced our understanding of AD genetic architecture, heritability estimates from population based GWAS cohorts are markedly smaller than those from twin studies. While this missing heritability is likely due to a combination of factors, it highlights that our understanding of AD genetic architecture and genetic risk mechanisms remains incomplete. These knowledge gaps result from several underexplored areas in AD research. First, rare variants remain understudied due to methodological issues in identifying them and the cost of generating sufficiently powered whole exome/genome sequencing datasets. Second, sample sizes of non-European ancestry populations in AD GWAS remain small. Third, GWAS of AD neuroimaging and cerebrospinal fluid endophenotypes remains limited due to low compliance and high costs associated with measuring amyloid-β and tau levels and other disease-relevant biomarkers. Studies generating sequencing data, including diverse populations, and incorporating blood-based AD biomarkers are set to substantially improve our knowledge of AD genetic architecture.