Counter the weaponization of genetics research by extremists

A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size

Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume either panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population genetics that can accommodate continuous geographic structure, and that ideally use georeferenced individuals as the unit of analysis, rather than populations or subpopulations. In addition, researchers are often interested in describing the diversity of a population distributed continuously in space; this diversity is intimately linked to both the dispersal potential and the population density of the organism. A statistical model that leverages information from patterns of isolation by distance to jointly infer parameters that control local demography (such as Wright's neighborhood size), and the long-term effective size (Ne) of a population would be useful. Here, we introduce such a model that uses individual-level pairwise genetic and geographic distances to infer Wright's neighborhood size and long-term Ne. We demonstrate the utility of our model by applying it to complex, forward-time demographic simulations as well as an empirical dataset of the two-form bumblebee (Bombus bifarius). The model performed well on simulated data relative to alternative approaches and produced reasonable empirical results given the natural history of bumblebees. The resulting inferences provide important insights into the population genetic dynamics of spatially structured populations.

Sources of gene expression variation in a globally diverse human cohort

Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity1-5. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project6, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-expression quantitative trait loci (eQTLs) and cis-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent 'population-specific' effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.

Increasing equity in science requires better ethics training: A course by trainees, for trainees

Despite the profound impacts of scientific research, few scientists have received the necessary training to productively discuss the ethical and societal implications of their work. To address this critical gap, we-a group of predominantly human genetics trainees-developed a course on genetics, ethics, and society. We intend for this course to serve as a template for other institutions and scientific disciplines. Our curriculum positions human genetics within its historical and societal context and encourages students to evaluate how societal norms and structures impact the conduct of scientific research. We demonstrate the utility of this course via surveys of enrolled students and provide resources and strategies for others hoping to teach a similar course. We conclude by arguing that if we are to work toward rectifying the inequities and injustices produced by our field, we must first learn to view our own research as impacting and being impacted by society.

Potential corporate uses of polygenic indexes: Starting a conversation about the associated ethics and policy issues

Some commercial firms currently sell polygenic indexes (PGIs) to individual consumers, despite their relatively low predictive power. It might be tempting to assume that because the predictive power of many PGIs is so modest, other sorts of firms-such as those selling insurance and financial services-will not be interested in using PGIs for their own purposes. We argue to the contrary. We build this argument in two ways. First, we offer a very simple model, rooted in economic theory, of a profit-maximizing firm that can gain information about a single consumer's genome. We use the model to show that, depending on the specific economic environment, a firm would be willing to pay for statistically noisy PGIs, even if they allow for only a small reduction in uncertainty. Second, we describe two plausible scenarios in which these different kinds of firms could conceivably use PGIs to maximize profits. Finally, we briefly discuss some of the associated ethics and policy issues. They deserve more attention, which is unlikely to be given until it is first recognized that firms whose services affect a large swath of the public will indeed have incentives to use PGIs.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

A geographic history of human genetic ancestry

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multifaceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

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.

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.

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.

Trust, but verify

In this editorial, the editor considers issues of trust, accountability, and verification in the work of scholars, institutions, and journals, and challenges readers to examine the interdependencies of trust, accountability, and verification in shaping the field of health professions education.

Getting it right: Teaching undergraduate biology to undermine racial essentialism

How we teach human genetics matters for social equity. The biology curriculum appears to be a crucial locus of intervention for either reinforcing or undermining students' racial essentialist views. The Mendelian genetic models dominating textbooks, particularly in combination with racially inflected language sometimes used when teaching about monogenic disorders, can increase middle and high school students' racial essentialism and opposition to policies to increase equity. These findings are of particular concern given the increasing spread of racist misinformation online and the misappropriation of human genomics research by white supremacists, who take advantage of low levels of genetics literacy in the general public. Encouragingly, however, teaching updated information about the geographical distribution of human genetic variation and the complex, multifactorial basis of most human traits, reduces students' endorsement of racial essentialism. The genetics curriculum is therefore a key tool in combating misinformation and scientific racism. Here, we describe a framework and example teaching materials for teaching students key concepts in genetics, human evolutionary history, and human phenotypic variation at the undergraduate level. This framework can be flexibly applied in biology and anthropology classes and adjusted based on time availability. Our goal is to provide undergraduate-level instructors with varying levels of expertise with a set of evidence-informed tools for teaching human genetics to combat scientific racism, including an evolving set of instructional resources, as well as learning goals and pedagogical approaches. Resources can be found at https://noto.li/YIlhZ5. Additionally, we hope to generate conversation about integrating modern genetics into the undergraduate curriculum, in light of recent findings about the risks and opportunities associated with teaching genetics.

'Dysgenic fertility' is an ideological, not a scientific, concept. A Comment on: 'Stability and change in male fertility patterns by cognitive ability across 32 birth cohorts' (2023), by Bratsberg & Rogeberg

Recently Bratsberg & Rogeberg (2023) presented an analysis in Biology Letters of how cognitive ability is associated with fertility in Norwegian men. Our concern relates to the theoretical framework of this paper. The analysis is framed around the concept of 'dysgenic fertility', which is treated throughout as a scientific theory, but 'dysgenic fertility' is not science, it is an ideological concept.

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.

Methods for Assessing Population Relationships and History Using Genomic Data

Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.

A rarefaction approach for measuring population differences in rare and common variation

In studying allele-frequency variation across populations, it is often convenient to classify an allelic type as "rare," with nonzero frequency less than or equal to a specified threshold, "common," with a frequency above the threshold, or entirely unobserved in a population. When sample sizes differ across populations, however, especially if the threshold separating "rare" and "common" corresponds to a small number of observed copies of an allelic type, discreteness effects can lead a sample from one population to possess substantially more rare allelic types than a sample from another population, even if the two populations have extremely similar underlying allele-frequency distributions across loci. We introduce a rarefaction-based sample-size correction for use in comparing rare and common variation across multiple populations whose sample sizes potentially differ. We use our approach to examine rare and common variation in worldwide human populations, finding that the sample-size correction introduces subtle differences relative to analyses that use the full available sample sizes. We introduce several ways in which the rarefaction approach can be applied: we explore the dependence of allele classifications on subsample sizes, we permit more than two classes of allelic types of nonzero frequency, and we analyze rare and common variation in sliding windows along the genome. The results can assist in clarifying similarities and differences in allele-frequency patterns across populations.

Leveraging individual power to improve racial equity in academia

Academia in the United States continues to grapple with its longstanding history of racial discrimination and its active perpetuation of racial disparities. To this end, universities and academic societies must grow in ways that reduce racial minoritization and foster racial equity. What are the effective and long-lasting approaches we as academics should prioritize to promote racial equity in our academic communities? To address this, the authors held a diversity, equity, and inclusion (DEI) panel during the Society for Behavioral Neuroendocrinology 2022 annual meeting, and in the following commentary synthesize the panelists' recommendations for fostering racial equity in the US academic community.

A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size

Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population genetics that can accommodate continuous geographic structure, and that ideally use georeferenced individuals as the unit of analysis, rather than populations or subpopulations. In addition, researchers are often interested describing the diversity of a population distributed continuously in space, and this diversity is intimately linked to the dispersal potential of the organism. A statistical model that leverages information from patterns of isolation-by-distance to jointly infer parameters that control local demography (such as Wright's neighborhood size), and the long-term effective size (Ne) of a population would be useful. Here, we introduce such a model that uses individual-level pairwise genetic and geographic distances to infer Wright's neighborhood size and long-term Ne. We demonstrate the utility of our model by applying it to complex, forward-time demographic simulations as well as an empirical dataset of the Red Sea clownfish (Amphiprion bicinctus). The model performed well on simulated data relative to alternative approaches and produced reasonable empirical results given the natural history of clownfish. The resulting inferences provide important insights into the population genetic dynamics of spatially structure populations.

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.