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The immune and circulatory systems of animals are functionally integrated. In mammals, the spleen and lymph nodes filter and destroy microbes circulating in the blood and lymph, respectively. In insects, immune cells that surround the heart valves (ostia), called periostial hemocytes, destroy pathogens in the areas of the body that experience the swiftest hemolymph (blood) flow. An infection recruits additional periostial hemocytes, amplifying heart-associated immune responses. Although the structural mechanics of periostial hemocyte aggregation have been defined, the genetic factors that regulate this process remain less understood. Here, we conducted RNAseq in the African malaria mosquito, Anopheles gambiae, and discovered that an infection upregulates multiple components of the IMD and JNK pathways in the heart with periostial hemocytes. This upregulation is greater in the heart with periostial hemocytes than in the circulating hemocytes or the entire abdomen. RNAi-based knockdown then showed that the IMD and JNK pathways drive periostial hemocyte aggregation and alter phagocytosis and melanization on the heart, thereby demonstrating that these pathways regulate the functional integration between the immune and circulatory systems. Understanding how insects fight infection lays the foundation for novel strategies that could protect beneficial insects and harm detrimental ones.

MotivationThousands of human gene regulatory enhancers are composed of sequences with multiple evolutionary origins. These evolutionarily “complex” enhancers consist of older “core” sequences and younger “derived” sequences. However, the functional relationship between the sequences of different evolutionary origins within complex enhancers is poorly understood.ResultsWe evaluated the function, selective pressures, and sequence variation across core and derived components of human complex enhancers. We find that both components are older than expected from the genomic background, and cores are enriched for derived sequences of similar evolutionary ages. Both components show strong evidence of biochemical activity in massively parallel report assays (MPRAs). However, core and derived sequences have distinct transcription factor (TF) binding preferences that are largely stable across evolutionary origins. Given these signatures of function, both core and derived sequences have substantial evidence of purifying selection. Nonetheless, derived sequences exhibit weaker purifying selection than adjacent cores. Derived sequences also tolerate more common genetic variation and are enriched compared to cores for eQTL associated with gene expression variability in human populations.ConclusionsBoth core and derived sequences have strong evidence of gene regulatory function, but derived sequences have distinct constraint profiles, TF binding preferences, and tolerance to variation compared with cores. We propose that the step-wise integration of younger derived and older core sequences has generated regulatory substrates with robust activity and the potential for functional variation. Our analyses demonstrate that synthesizing study of enhancer evolution and function can aid interpretation of regulatory sequence activity and functional variation across human populations.Significance StatementThousands of human gene regulatory enhancers are mosaics of sequences from multiple evolutionary origins, yet how these different segments contribute to enhancer function is poorly understood. By dissecting their regulatory functions, transcription factor binding, constraint, and human genetic variation, we show that both older “core” and younger “derived” sequences in complex enhancers have strong evidence of gene regulatory function, but derived sequences are more likely to harbor genetic variants that influence function. Together, our results support a model in which the integration of sequences of different origins generates regulatory substrates with robust activity and the potential for functional variation.

Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. Their potential impact is particularly germane to the biology of pregnancy and perinatal medicine, where limited improvement in health outcomes for women and children has remained a global challenge. We assembled a group of experts to establish a Pregnancy Think Tank to discuss a broad spectrum of major gestational disorders and adverse pregnancy outcomes that affect maternal-infant lifelong health and should serve as targets for leveraging the many recent advances. This report reflects avenues for future effects that hold great potential in 3 major areas: developmental genomics, including the application of methodologies designed to bridge genotypes, physiology, and diseases, addressing vexing questions in early human development; gestational physiology, from immune tolerance to growth and the timing of parturition; and personalized and population medicine, focusing on amalgamating health record data and deep phenotypes to create broad knowledge that can be integrated into healthcare systems and drive discovery to address pregnancy-related disease and promote general health. We propose a series of questions reflecting development, systems biology, diseases, clinical approaches and tools, and population health, and a call for scientific action. Clearly, transdisciplinary science must advance and accelerate to address adverse pregnancy outcomes. Disciplines not traditionally involved in the reproductive sciences, such as computer science, engineering, mathematics, and pharmacology, should be engaged at the study design phase to optimize the information gathered and to identify and further evaluate potentially actionable therapeutic targets. Information sources should include noninvasive personalized sensors and monitors, alongside instructive “liquid biopsies” for noninvasive pregnancy assessment. Future research should also address the diversity of human cohorts in terms of geography, racial and ethnic distributions, and social and health disparities. Modern technologies, for both data-gathering and data-analyzing, make this possible at a scale that was previously unachievable. Finally, the psychosocial and economic environment in which pregnancy takes place must be considered to promote the health and wellness of communities worldwide.

Neanderthal ancestry remains across modern Eurasian genomes, and introgressed sequences influence diverse phenotypes. Here we demonstrate that introgressed sequences reintroduced thousands of ancestral alleles that were lost in Eurasian populations prior to introgression. Our simulations and variant effect predictions argue that these reintroduced alleles (RAs) are more likely to be tolerated by modern humans than introgressed Neanderthal-derived alleles (NDAs) due to their distinct evolutionary histories. Consistent with this, we show enrichment for RAs and depletion for NDAs on introgressed haplotypes with expression quantitative trait loci (eQTL) and phenotype associations. Analysis of available cross-population eQTLs and massively parallel reporter assay (MPRA) data show that RAs commonly influence gene expression independent of linked NDAs. We further validate these independent effects for one RA in vitro. Finally, we demonstrate that NDAs are depleted for regulatory activity compared to RAs, while RAs have activity levels similar to non-introgressed variants. In summary, our study reveals that Neanderthal introgression reintroduced thousands of lost ancestral variants with gene regulatory activity and that these RAs were more tolerated than NDAs. Thus, RAs and their distinct evolutionary histories must be considered when evaluating the effects of introgression. ONE SENTENCE SUMMARY Neanderthal interbreeding with anatomically modern humans restored thousands of ancient alleles that were previously lost in Eurasian populations.

Currently, there is no comprehensive framework to evaluate the evolutionary forces acting on genomic regions associated with human complex traits and contextualize the relationship between evolution and molecular function. Here, we develop an approach to test for signatures of diverse evolutionary forces on trait-associated genomic regions. We apply our method to regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including conservation, excess population differentiation, accelerated evolution, and balanced polymorphism. Furthermore, we integrate evolutionary context with molecular evidence to hypothesize how these regions contribute to sPTB risk. Finally, we observe enrichment in signatures of diverse evolutionary forces in sPTB-associated regions compared to genomic background. By quantifying multiple evolutionary forces acting on sPTB-associated regions, our approach improves understanding of both functional roles and the mosaic of evolutionary forces acting on loci. Our work provides a blueprint for investigating evolutionary pressures on complex traits., There is a need for analytical frameworks to investigate the evolution of complex genetic traits. Here, the authors develop an approach to simultaneously evaluate different evolutionary signatures on trait-associated genetic variants for complex traits and apply it to study spontaneous preterm birth.

R-loops are involved in transcriptional regulation, DNA and histone post-translational modifications, genome replication and genome stability. To what extent R-loop abundance and genome-wide localization is actively regulated during metazoan embryogenesis is unknown. Drosophila embryogenesis provides a powerful system to address these questions due to its well-characterized developmental program, the sudden onset of zygotic transcription and available genome-wide ChIP and transcription data sets. Here, we measure the overall abundance and genome localization of R-loops in early and late-stage embryos relative to Drosophila cultured cells. We demonstrate that absolute R-loop levels change during embryogenesis and that resolution of R-loops is critical for embryonic development. R-loop mapping by strand-specific DRIP-seq reveals that R-loop localization is plastic across development, both in the genes which form R-loops and where they localize relative to gene bodies. Importantly, these changes are not driven by changes in the transcriptional program. Negative GC skew and absolute changes in AT skew are associated with R-loop formation in Drosophila. Furthermore, we demonstrate that while some chromatin binding proteins and histone modification such as H3K27me3 are associated with R-loops throughout development, other chromatin factors associated with R-loops in a developmental specific manner. Our findings highlight the importance and developmental plasticity of R-loops during Drosophila embryogenesis.

As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1, GPX1, and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits.

Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which was most dominant in reproductive traits. Many traits also exhibited substantial enrichment for population differentiation and recent positive selection, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for balancing selection (51% GWASs) and no evidence of enrichment for selection signals in regions associated with late-onset Alzheimer’s disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate where diverse modes of selection have shaped trait-associated loci. This atlas of signatures of different modes of natural selection across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and selection in the human genome.

SUMMARYThe origin recognition complex (ORC) binds throughout the genome to initiate DNA replication. In metazoans, it is still unclear how ORC is targeted to specific loci to facilitate helicase loading and replication initiation. Here, we performed immunoprecipitations coupled with mass spectrometry for ORC2 in Drosophila embryos. Surprisingly, we found that ORC2 associates with multiple subunits of the Nup107-160 subcomplex of the nuclear pore. Bioinformatic analysis revealed that, relative to all modENCODE factors, nucleoporins are among the most enriched factors at ORC2 binding sites. Critically, depletion of the nucleoporin Elys, a member of the Nup107-160 complex, results in decreased ORC2 loading onto chromatin. Depleting Elys also sensitized cells to replication fork stalling, which could reflect a defect in establishing dormant replication origins. Our work reveals a new connection between ORC, replication initiation and nucleoporins, highlighting a previously unrecognized function of nucleoporins in metazoan replication initiation.

Quantification of the tolerance of protein-coding sites to genetic variation within human populations has become a cornerstone of the prediction of the function of genomic variants. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct 3D interactions with neighboring sites. To quantify the constraint on protein-coding genetic variation in 3D spatial neighborhoods, we introduce a new framework called COntact Set MISsense tolerance (or COSMIS) for estimating constraint. Leveraging recent advances in computational structure prediction, large-scale sequencing data from gnomAD, and a mutation-spectrum-aware statistical model, we comprehensively map the landscape of 3D spatial constraint on 6.1 amino acid sites covering >80% (16,533) of human proteins. We show that the human proteome is broadly under 3D spatial constraint and that the level of spatial constraint is strongly associated with disease relevance both at the individual site level and the protein level. We demonstrate that COSMIS performs significantly better at a range of variant interpretation tasks than other population-based constraint metrics while also providing biophysical insight into the potential functional roles of constrained sites. We make our constraint maps freely available and anticipate that the structural landscape of constrained sites identified by COSMIS will facilitate interpretation of protein-coding variation in human evolution and prioritization of sites for mechanistic or functional investigation.

INTRODUCTION AND OBJECTIVE:To enable earlier preventative interventions or guide empiric therapy for kidney stone disease, our objective was to demonstrate feasibility of predicting 24-hour urine a...

Over 90% of variants are rare, and 50% of them are singletons in the Alzheimer’s Disease Sequencing Project Whole Exome Sequencing (ADSP WES) data. However, either single variant tests or unit-based tests are limited in the statistical power to detect the association between rare variants and phenotypes. To best utilize rare variants and investigate their biological effect, we exam their association with phenotypes in the context of protein. We developed a protein structure-based approach, POKEMON (Protein Optimized Kernel Evaluation of Missense Nucleotides), which evaluates rare missense variants based on their spatial distribution on the protein rather than allele frequency. The hypothesis behind this is that the three-dimensional spatial distribution of variants within a protein structure provides functional context and improves the power of association tests. POKEMON identified four candidate genes from the ADSP WES data, namely two known Alzheimer’s disease (AD) genes (TREM2 and SORL) and two novel genes (DUSP18 and CSF1R). For known AD genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low frequency risk variants within these genes. DUSP18 has a cluster of variants primarily shared by case subjects around the ligand-binding domain, and this cluster is further validated in a replication dataset with a larger sample size. POKEMON is an open-source tool available at https://github.com/bushlab-genomics/POKEMON.

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure–based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

Eurasians have ~2% Neanderthal ancestry, but we lack a comprehensive understanding of the genome-wide influence of Neanderthal introgression on modern human diseases and traits. Here, we quantify the contribution of introgressed alleles to the heritability of more than 400 diverse traits. We show that genomic regions in which detectable Neanderthal ancestry remains are depleted of heritability for all traits considered, except those related to skin and hair. Introgressed variants themselves are also depleted for contributions to the heritability of most traits. However, introgressed variants shared across multiple Neanderthal populations are enriched for heritability and have consistent directions of effect on several traits with potential relevance to human adaptation to non-African environments, including hair and skin traits, autoimmunity, chronotype, bone density, lung capacity, and menopause age. Integrating our results, we propose a model in which selection against introgressed functional variation was the dominant trend (especially for cognitive traits); however, for a few traits, introgressed variants provided beneficial variation via uni-directional (e.g., lightening skin color) or bi-directional (e.g., modulating immune response) effects., We lack a comprehensive understanding of how Neanderthal ancestry influences human traits. This study finds that regions with Neanderthal ancestry are broadly depleted of trait-associated variation; yet, introgressed variants likely contributed to human adaptation in a few traits, like skin color and immune response modulation.

As humans spread throughout the world, they adapted to variation in many environmental factors, including climate, diet, and pathogens. Because many of these adaptations were likely mediated by multiple non-coding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting gene regulatory prediction to increase robustness to incomplete aDNA data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes previously proposed to be involved in diet-related local adaptation, and in many cases, the predicted effects on regulation provide explanations for previously observed signals of selection, e.g., atFADS1,GPX1, andLEPR. For skin pigmentation, we applied new models trained in melanocytes to a time series of 2999 ancient Europeans spanning ~38,000 years BP. In contrast to diet, skin pigmentation genes show little regulatory change over time, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates how aDNA can be combined with present-day genomes to shed light on the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between ancient genetic diversity and the present-day distribution of complex traits.

Background: Long-term balancing selection (LTBS) can maintain allelic variation at a locus over millions of years and through speciation events. Variants shared between species, hereafter “trans-species polymorphisms” (TSPs), often result from LTBS due to host-pathogen interactions. For instance, the major histocompatibility complex (MHC) locus contains TSPs present across primates. Several hundred candidate TSPs have been identified in humans and chimpanzees; however, because many are in non-coding regions of the genome, the functions and adaptive roles for most TSPs remain unknown. Results: We integrated diverse genomic annotations, with a focus on non-coding regions, to explore the functions of 125 previously identified regions containing multiple TSPs in humans and chimpanzees. We analyzed genome-wide functional assays, expression quantitative trait loci (eQTL), genome-wide association studies (GWAS), and phenome-wide association studies (PheWAS). We identify functional annotations for 119 TSP regions, including 71 with evidence of gene regulatory function from GTEx or genome-wide functional genomics data and 21 with evidence of trait association from GWAS and PheWAS. TSPs in humans associate with many immune system phenotypes, including response to pathogens, but we also find associations with a range of other phenotypes, including body mass, alcohol intake, urate levels, chronotype, and risk-taking behavior. Conclusions: The diversity of traits associated with non-coding human TSPs further support previous hypotheses that functions beyond the immune system are subject to LTBS. Furthermore, several of these trait associations provide support and candidate genetic loci for previous hypothesis about behavioral diversity in great ape populations, such as the importance of variation in sleep cycles and risk sensitivity.

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential., Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.

Background Two major forms of inherited J-wave syndrome (JWS) are recognized: early repolarization syndrome (ERS) and Brugada syndrome (BrS). Objectives This study sought to assess the distinct features between patients with ERS and BrS carrying pathogenic variants in SCN5A. Methods Clinical evaluation and next-generation sequencing were performed in 262 probands with BrS and 104 with ERS. Nav1.5 and Kv4.3 channels were studied with the use of patch-clamp techniques. A computational model was used to investigate the protein structure. Results The SCN5A+ yield in ERS was significantly lower than in BrS (9.62% vs 22.90%; P = 0.004). Patients diagnosed with ERS displayed shorter QRS and QTc than patients with BrS. More than 2 pathogenic SCN5A variants were found in 5 probands. These patients displayed longer PR intervals and QRS duration and experienced more major arrhythmia events (MAE) compared with those carrying only a single pathogenic variant. SCN5A-L1412F, detected in a fever-induced ERS patient, led to total loss of function, destabilized the Nav1.5 structure, and showed a dominant-negative effect, which was accentuated during a febrile state. ERS-related SCN5A-G452C did not alter the inward sodium current (INa) when SCN5A was expressed alone, but when coexpressed with KCND3 it reduced peak INa by 44.52% and increased the transient outward potassium current (Ito) by 106.81%. Conclusions These findings point to SCN5A as a major susceptibility gene in ERS as much as it is in BrS, whereas the lower SCN5A+ ratio in ERS indicates the difference in underlying electrophysiology. These findings also identify the first case of fever-induced ERS and demonstrate a critical role of Ito in JWS and a higher risk for MAE in JWS probands carrying multiple pathogenic variants in SCN5A.

Evolutionary changes in enhancers are widely associated with variation in human traits and diseases. However, studies comprehensively quantifying levels of selection on enhancers at multiple evolutionary time points during recent human evolution and how enhancer evolution varies across human tissues are lacking. To address these questions, we integrated a dataset of 41,561 transcribed enhancers active in 41 different human tissues (FANTOM Consortium) with whole genome sequences of 1,668 individuals from the African, Asian, and European populations (1000 Genomes Project). Our analyses based on four different metrics (Tajima’s D, FST, H12, nSL) showed that ~5.90% of enhancers considered showed evidence of recent positive selection and that genes associated with enhancers under positive selection are enriched for diverse immune-related functions. The distributions of these metrics for brain and testis enhancers were often statistically significantly different compared to those of other tissues; the same was true for brain and testis enhancers that are tissue-specific compared to those that are tissue-broad and for testis enhancers associated with tissue-enriched and non-tissue-enriched genes. These differences varied considerably across metrics and tissues and were generally due to changes in distributions’ shapes rather than shifts in their values. These results suggest that many human enhancers experienced recent positive selection throughout multiple time periods in human evolutionary history, that this selection occurred in a tissue-dependent and immune-related functional context, and that much like the evolution of their coding counterparts, the evolution of brain and testis enhancers has been markedly different from that of enhancers in other tissues.

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers., Graphical abstract Unlabelled Image

[Image: see text] Advances over the past 25 years have revealed much about how the structural properties of membranes and associated proteins are linked to the thermodynamics and kinetics of membrane protein (MP) folding. At the same time biochemical progress has outlined how cellular proteostasis networks mediate MP folding and manage misfolding in the cell. When combined with results from genomic sequencing, these studies have established paradigms for how MP folding and misfolding are linked to the molecular etiologies of a variety of diseases. This emerging framework has paved the way for the development of a new class of small molecule “pharmacological chaperones” that bind to and stabilize misfolded MP variants, some of which are now in clinical use. In this review, we comprehensively outline current perspectives on the folding and misfolding of integral MPs as well as the mechanisms of cellular MP quality control. Based on these perspectives, we highlight new opportunities for innovations that bridge our molecular understanding of the energetics of MP folding with the nuanced complexity of biological systems. Given the many linkages between MP misfolding and human disease, we also examine some of the exciting opportunities to leverage these advances to address emerging challenges in the development of therapeutics and precision medicine.

Background and objectives The diversity of eutherian reproductive strategies has led to variation in many traits, such as number of offspring, age of reproductive maturity and gestation length. While reproductive trait variation has been extensively investigated and is well established in mammals, the genetic loci contributing to this variation remain largely unknown. The domestic dog, Canis lupus familiaris is a powerful model for studies of the genetics of inherited disease due to its unique history of domestication. To gain insight into the genetic basis of reproductive traits across domestic dog breeds, we collected phenotypic data for four traits, cesarean section rate, litter size, stillbirth rate and gestation length, from primary literature and breeders' handbooks. Methodology By matching our phenotypic data to genomic data from the Cornell Veterinary Biobank, we performed genome-wide association analyses for these four reproductive traits, using body mass and kinship among breeds as covariates. Results We identified 12 genome-wide significant associations between these traits and genetic loci, including variants near CACNA2D3 with gestation length, MSRB3 and MSANTD1 with litter size, SMOC2 with cesarean section rate and UFM1 with stillbirth rate. A few of these loci, such as CACNA2D3 and MSRB3, have been previously implicated in human reproductive pathologies, whereas others have been associated with domestication-related traits, including brachycephaly (SMOC2) and coat curl (KRT71). Conclusions and implications We hypothesize that the artificial selection that gave rise to dog breeds also influenced the observed variation in their reproductive traits. Overall, our work establishes the domestic dog as a system for studying the genetics of reproductive biology and disease. LAY SUMMARY The genetic contributors to variation in mammalian reproductive traits remain largely unknown. We took advantage of the domestic dog, a powerful model system, to test for associations between genome-wide variants and four reproductive traits (cesarean section rate, litter size, stillbirth rate and gestation length) that vary extensively across breeds. We identified associations at a dozen loci, including ones previously associated with domestication-related traits, suggesting that selection on dog breeds also influenced their reproductive traits.

Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome's functional landscape and enabling variant interpretation that takes 3D structure into account.

Nucleotide excision repair (NER) is an essential DNA damage repair pathway that removes bulky DNA lesions formed by exposure to ultraviolet light, environmental toxins, and platinum (Pt)-based chemotherapeutic drugs that are a standard of care for many cancer types. Mutation or decreased NER gene expression in cancer correlates with improved patient survival after Pt-based chemotherapy. However, the impact of most missense mutations in NER genes is unknown, and few approaches exist to reliably identify nonrecurrent passenger mutations with functional consequences. In this study, a multidisciplinary strategy will be developed to predict, validate, and characterize NER-defective mutations in the essential NER scaffold protein Xeroderma Pigmentosum Complementation Group A (XPA). Computational analyses were used to score NER-deficient versus NER-proficient mutations for further study. Predicted NER-deficient XPA mutants are being expressed in human XPA-deficient cells and screened for both NER activity and cisplatin sensitivity. In-depth biophysical and structural studies are being implemented to elucidate mechanisms of dysfunction. Identifying NER-deficient mutations that may sensitize tumors to Pt-based chemotherapies represents a promising strategy to stratify patients for optimal treatment strategies.

Long-term balancing selection (LTBS) can maintain allelic variation at a locus over millions of years and through speciation events. Variants shared between species, hereafter “trans-species polymorphisms” (TSPs), often result from LTBS due to host-pathogen interactions. For instance, the major histocompatibility complex (MHC) locus contains TSPs present across primates. Several hundred TSPs have been identified in humans and chimpanzees; however, because many are in non-coding regions of the genome, the functions and adaptive roles for most TSPs remain unknown. We integrated diverse genomic annotations to explore the functions of 125 previously identified non-coding TSPs that are likely under LTBS since the common ancestor of humans and chimpanzees. We analyzed genome-wide functional assays, expression quantitative trait loci (eQTL), genome-wide association studies (GWAS), and phenome-wide association studies (PheWAS). We identify functional annotations for 119 TSP regions, including 71 with evidence of gene regulatory function from GTEx or genome-wide functional genomics data and 21 with evidence of trait association from GWAS and PheWAS. TSPs in humans associate with many immune system phenotypes, including response to pathogens, but we also find associations with a range of other phenotypes, including body mass, alcohol intake, urate levels, chronotype, and risk-taking behavior. The diversity of traits associated with non-coding human TSPs suggest that functions beyond the immune system are often subject to LTBS. Furthermore, several of these trait associations provide support and candidate genetic loci for previous hypothesis about behavioral diversity in great ape populations, such as the importance of variation in sleep cycles and risk sensitivity.Significance statementMost genetic variants present in human populations are young (

Background: Brugada syndrome (BrS) is a severe inherited arrhythmia syndrome that can be unmasked by fever. Methods: A multicentre clinical analysis was performed in 261 patients diagnosed with fever-induced BrS, including 198 (75.9%) and 27 (10.3%) patients who received next-generation genetic sequencing and epicardial arrhythmogenic substrate (AS) mapping, respectively. Findings: In fever-induced BrS patients, pathogenic or likely pathogenic (P/LP) SCN5A variant carriers developed fever-induced BrS at a younger age, and more often in females and those of Caucasian descent. They exhibited significant electrophysical abnormalities, including a larger epicardial AS area, and more prolonged abnormal epicardial electrograms. During a median follow-up of 50.5 months (quartiles 32.5–81.5 months) after the diagnosis, major cardiac events (MCE) occurred in 27 (14.4%) patients. Patients with P/LP SCN5A variants had a higher ratio of MCE compared with the rest. Additionally, history of syncope, QRS duration, and Tpe interval could also predict an increased risk for future MCE according to univariate analysis. Multivariate analysis indicated that only P/LP SCN5A variants were independent significant predictors of MCE. Computational structural modelling showed that most variants are destabilizing, suggesting that Nav1.5 structure destabilization caused by SCN5A missense variants may contribute to fever-induced BrS. Interpretation: In our cohort, P/LP SCN5A variant carriers with fever-induced BrS are more prevalent among patients of Caucasian descent, females, and younger patients. These patients exhibit aggressive electrophysiological abnormalities and worse outcome, which warrants closer monitoring and more urgent management of fever. Funding: None.

Background: Brugada syndrome (BrS) is a severe inherited arrhythmia syndrome which can be unmasked by fever. Methods: A clinical analysis was performed in 134 probands diagnosed with fever-induced BrS, including 59 patients who received next-generation genetic sequencing. We employed a biophysics-based computational protocol to investigate whether protein structure destabilization contributes to Nav1.5 variant-mediated fever-induced BrS. Findings: Among all cases enrolled, the symptoms at diagnosis were syncope in 26 (19.40%) and major arrhythmic events (MAE) in 16 (11.94%). SCN5A variant carriers were significantly younger than probands free of SCN5A variants. Compared with a historical cohort of non-fever BrS probands carrying SCN5A variants, carriers with fever-induced BrS were prone to MAE at younger age, and had a higher proportion of variants localizing at the interdomain linkers. Modeling result showed that most variants are destabilizing when temperature increases, suggesting that Nav1.5 structure destabilization is the cause of fever-induced BrS associated with SCN5A variants. Interpretation: In our cohort, SCN5A variant carriers with fever-induced BrS present at a younger age and harbor SCN5A variants predominantly localized to interdomain linker regions of Nav1.5. These SCN5A variants typically induce destabilization of Nav1.5 structure at higher body temperatures. Patients with SCN5A mutations present within interdomain linker regions may warrant more aggressive monitoring and management of fever. Funding Statement: The current work was supported by the National Natural Science Foundation Project of China (Grant No. 81670304); the National Institutes of Health of USA (NIH R56 - HL47678], NIH R01 [HL138103]), NIH R01 [HL152201], the W.W. Smith Charitable Trust and the Wistar and Martha Morris Fund. Declaration of Interests: The authors report no relationships that could be construed as a conflict of interest. Ethics Approval Statement: This study was approved by the Institutional Review Board of each hospital and performed in accordance with the declaration of Helsinki. All participants gave written informed consent.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Accurate prediction of secondary structures and transmembrane segments is often the first step towards modeling the tertiary structure of a protein. Existing methods are either specialized in one class of proteins or developed to predict one type of 1D structural attributes (secondary structure, topology, or transmembrane segment). In this work, we develop a new method for simultaneous prediction of secondary structure, transmembrane segment, and transmembrane topology with no a priori assumption on the class of the input protein sequence. The new method, Membrane Association and Secondary Structures of Proteins (MASSP) predictor, uses multi-tiered neural networks that incorporate recent innovations in machine learning. The first tier is a multi-task multi-layer convolutional neural network (CNN) that learns patterns in image-like input position-specific-scoring matrices (PSSMs) and predicts residue-level 1D structural attributes. The second tier is a long short-term memory (LSTM) neural network that treats the predictions of the first tier from the perspective of natural language processing and predicts the class of the input protein sequence. We curated a non-redundant data set consisting of 54 bitopic, 241 multi-spanning TM-alpha, 77 TM-beta, and 372 soluble proteins, respectively for training and testing MASSP. For secondary structure prediction, the mean three-state accuracy (Q3) of MASSP is 0.830, better than the Q3 of PSIPRED (0.829) and that of SPINE-X (0.813) and substantially better than that of Jufo9D (0.762) and RaptorX-Property (0.741). The mean segment overlap score (SOV) of MASSP is 0.752, gaining at least 7.7% improvement over all the other four methods. For transmembrane topology prediction, MASSP has a performance comparable to OCTOPUS and substantially better than MEMSAT3 and TMHMM2 on TM-alpha proteins, and on TM-beta proteins, MASSP is significantly better than both BOCTOPUS2 and PRED-TMBB2. By integrating prediction of secondary structure and transmembrane segments in a deep-learning framework, MASSP improves performance over previous methods, has broader applicability, and enables proteome scale predictions.

Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features they learn. Several models of how combinatorial binding of transcription factors, i.e. the regulatory grammar, drives enhancer activity have been proposed, ranging from the flexible TF billboard model to the stringent enhanceosome model. However, there is limited knowledge of the prevalence of these (or other) sequence architectures across enhancers. Here we perform several hypothesis-driven analyses to explore the ability of DNNs to learn the regulatory grammar of enhancers. We created synthetic datasets based on existing hypotheses about combinatorial transcription factor binding site (TFBS) patterns, including homotypic clusters, heterotypic clusters, and enhanceosomes, from real TF binding motifs from diverse TF families. We then trained deep residual neural networks (ResNets) to model the sequences under a range of scenarios that reflect real-world multi-label regulatory sequence prediction tasks. We developed a gradient-based unsupervised clustering method to extract the patterns learned by the ResNet models. We demonstrated that simulated regulatory grammars are best learned in the penultimate layer of the ResNets, and the proposed method can accurately retrieve the regulatory grammar even when there is heterogeneity in the enhancer categories and a large fraction of TFBS outside of the regulatory grammar. However, we also identify common scenarios where ResNets fail to learn simulated regulatory grammars. Finally, we applied the proposed method to mouse developmental enhancers and were able to identify the components of a known heterotypic TF cluster. Our results provide a framework for interpreting the regulatory rules learned by ResNets, and they demonstrate that the ability and efficiency of ResNets in learning the regulatory grammar depends on the nature of the prediction task., Author summary Gene regulatory sequences function through the combinatorial binding of transcription factors (TFs). However, the specific binding combinations and patterns that specify regulatory activity in different cellular contexts (“regulatory grammars”) are poorly understood. Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying regulatory DNA sequences active in different contexts, but they have provided only limited biological insight due to the complexity of the statistical patterns they learn. In this study, we explore the power and limitations of DNNs in learning regulatory grammars through biologically motivated simulations. We simulated regulatory sequences based on existing hypotheses about the structure of possible regulatory grammars and trained DNNs to model these sequences under a range of scenarios that reflect real-world regulatory sequence prediction tasks. We developed an unsupervised clustering method to extract learned patterns from the trained DNNs and compare them to the simulated grammars. We found that our method can successfully extract regulatory grammars when they are learned by the DNN, but the ability of DNNs to learn regulatory grammars highly depends on the nature of the prediction task. Finally, we show that our DNN approach highlights a known heart regulatory grammar when applied to real mouse enhancer sequences.

PURPOSE: Mayer–Rokitansky–Küster–Hauser (MRKH) syndrome consists of congenital absence of the uterus and vagina and is often associated with renal, skeletal, cardiac, and auditory defects. The genetic basis is largely unknown except for rare variants in several genes. Many candidate genes have been suggested by mouse models and human studies. The purpose of this study was to narrow down the number of candidate genes. METHODS: Whole exome sequencing was performed on 111 unrelated individuals with MRKH; variant analysis focused on 72 genes suggested by mouse models, human studies of physiological candidates, or located near translocation breakpoints in t(3;16). Candidate variants (CV) predicted to be deleterious were confirmed by Sanger sequencing. RESULTS: Sanger sequencing verified 54 heterozygous CV from genes identified through mouse (13 CV in 6 genes), human (22 CV in seven genes), and translocation breakpoint (19 CV in 11 genes) studies. Twelve patients had ≥ 2 CVs, including four patients with two variants in the same gene. One likely digenic combination of LAMC1 and MMP14 was identified. CONCLUSION: We narrowed 72 candidate genes to 10 genes that appear more likely implicated. These candidate genes will require further investigation to elucidate their role in the development of MRKH.

MotivationDespite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive evolutionary model of enhancer sequence architecture and function. This substantially limits our understanding of the genetic basis for divergence between species and our ability to interpret the effects of non-coding variants on human traits.ResultsTo explore enhancer sequence evolution and its relationship to regulatory function, we traced the evolutionary origins of human sequences with enhancer activity defined by eRNA from diverse tissues and cellular contexts. The majority of enhancers are sequences of a single evolutionary age (“simple” enhancer architectures), likely indicating constraint against genomic rearrangements. A minority of enhancers are composites of sequences of multiple evolutionary ages (“complex” enhancer architectures). Compared to simple enhancers, complex enhancers are older, more pleiotropic, and more active across species. Genetic variants within complex enhancers are also less likely to have effects on human traits and biochemical activity. Transposable-element-derived sequences have made diverse contributions to enhancer architectures; some have nucleated enhancers with simple architectures, while others have remodeled older sequences to create complex regulatory architectures.ConclusionsBased on these results, we propose a framework for modeling enhancer sequence architecture and evolution. Applying this framework to human enhancer sequences reveals multiple, distinct trajectories of human regulatory sequence evolution. Considering these evolutionary histories can aid interpretation of the effects of variants on enhancer function.

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is the cause of the global pandemic of coronavirus disease‐2019 (COVID‐19). SARS‐CoV‐2 is a zoonotic disease, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. Certain species, such as domestic cats and tigers, are susceptible to SARS‐CoV‐2 infection, while other species such as mice and chickens are not. Most animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict the infection risk of animal species are urgently needed. SARS‐CoV‐2 spike protein binding to angiotensin‐converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we integrate species differences in susceptibility with multiple in‐depth structural analyses to identify key ACE2 amino acid positions including 30, 83, 90, 322, and 354 that distinguish susceptible from resistant species. Using differences in these residues across species, we developed a susceptibility score that predicts an elevated risk of SARS‐CoV‐2 infection for multiple species including horses and camels. We also demonstrate that SARS‐CoV‐2 is nearly optimal for binding ACE2 of humans compared to other animals, which may underlie the highly contagious transmissibility of this virus among humans. Taken together, our findings define potential ACE2 and SARS‐CoV‐2 residues for therapeutic targeting and identification of animal species on which to focus research and protection measures for environmental and public health.

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC=0.75, PR-AUC=0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC=0.65, PR-AUC=0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth sub-types enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) from a different healthcare system. By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy.

In only a few months, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, leaving physicians, scientists, and public health officials racing to understand, treat, and contain this zoonotic disease. SARS-CoV-2 has made the leap from animals to humans, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. While there is evidence that certain species, such as cats, are susceptible, the vast majority of animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict their infection risk are urgently needed. SARS-CoV-2 spike protein binding to angiotensin converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we identified key ACE2 residues that distinguish susceptible from resistant species using in-depth sequence and structural analyses of ACE2 and its binding to SARS-CoV-2. Our findings have important implications for identification of ACE2 and SARS-CoV-2 residues for therapeutic targeting and identification of animal species with increased susceptibility for infection on which to focus research and protection measures for environmental and public health.

BackgroundNearly all Eurasians have ∼2% Neanderthal ancestry due to several events of inbreeding between anatomically modern humans and archaic hominins. Previous studies characterizing the legacy of Neanderthal ancestry in modern Eurasians have identified examples of both adaptive and deleterious effects of admixture. However, we lack a comprehensive understanding of the genome-wide influence of Neanderthal introgression on modern human diseases and traits.ResultsWe integrate recent maps of Neanderthal ancestry with well-powered association studies for more than 400 diverse traits to estimate heritability enrichment patterns in regions of the human genome tolerant of Neanderthal ancestry and in introgressed Neanderthal variants themselves. First, we find that variants in regions tolerant of Neanderthal ancestry are depleted of heritability for all traits considered, except skin and hair-related traits. Second, the introgressed variants remaining in modern Europeans are depleted of heritability for most traits; however, we discover that they are enriched for heritability of several traits with potential relevance to human adaptation to non-African environments, including hair and skin traits, autoimmunity, chronotype, bone density, lung capacity, and menopause age. To better understand the phenotypic consequences of these enrichments, we adapt recent methods to test for consistent directional effects of introgressed alleles, and we find directionality for several traits. Finally, we use a direction-of-effect-aware approach to highlight novel candidate introgressed variants that influence risk for disease.ConclusionOur results demonstrate that genomic regions retaining Neanderthal ancestry are not only less functional at the molecular-level, but are also depleted for variation influencing a diverse array of complex traits in modern humans. In spite of this depletion, we identify traits where introgression has an outsized effect. Integrating our results, we propose a framework for using quantification of trait heritability and direction of effect in introgressed regions to understand how Neanderthals were different from modern humans, how selection acted on different traits, and how introgression may have facilitated adaptation to non-African environments.

Rare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Context Hypophosphatasia (HPP) is a syndrome marked by low serum alkaline phosphatase (AlkP) activity as well as musculoskeletal and/or dental disease. While the majority of subjects with HPP carry a pathogenic variant in the ALPL gene or its regulatory regions, individual pathogenic variants are often not tightly correlated with clinical symptomatology. We sought to better understand the genotype/phenotype correlation in HPP by examining the clinical and biochemical data of 37 subjects with 2 rare variants in ALPL. Methods Through BioVU, a DNA biobank that pairs individuals’ genetic information with their de-identified medical records, we identified subjects with 2 rare variants with distinct reported clinical phenotypes (p.D294A and p.T273M). We then performed a manual review of these subjects’ de-identified medical records along with computational modeling of protein structure to construct a genetic, biochemical and clinical phenotype for each subject and variant. Results Twenty subjects with the p.D294A variant and 17 with the p.T273M variant had sufficient data for analysis. Among subjects in our cohort with the p.D294A variant, 6 (30.0%) had both clinical bone disease and serum AlkP activity below 40 IU/L while 4 subjects (23.5%) with the p.T273M variant met the same criteria despite the distinct clinical phenotypes of these variants. Conclusions Given the loose genotype/phenotype correlation in HPP seen in our cohort, clinical context is crucial for the interpretation of genetic test results to guide clinical care in this population. Otherwise, over- or under-diagnosis may occur, resulting in misidentification of those who may benefit from additional screening and perhaps pharmacologic intervention.

Genetic variation in the membrane trafficking adapter protein complex 4 (AP-4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP-4 function arising from genetic variation, because AP-4 remains poorly understood structurally. Here, we analyze patterns of AP-4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation. We map known variants onto an AP-4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP-4. We find significant clustering of likely pathogenic variants located at the interface between the β4 and N-μ4 subunits, as well as throughout the C-μ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP-4 structure and function. As more individuals with uncharacterized AP-4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted.

Sex and sexual differentiation are pervasive across the tree of life. Because females and males often have substantially different functional requirements, we expect selection to differ between the sexes. Recent studies in diverse species, including humans, suggest that sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci. However, theory and population-level simulations indicate that sex-specific differences in viability would need to be very large to produce and maintain reported levels of between-sex allelic differentiation. We address this contradiction between theoretical predictions and empirical observations by evaluating evidence for sexually antagonistic viability selection on autosomal loci in humans using the largest cohort to date (UK Biobank, n = 487,999) along with a second large, independent cohort (BioVU, n = 93,864). We performed association tests between genetically ascertained sex and autosomal loci. Although we found dozens of genome-wide significant associations, none replicated across cohorts. Moreover, closer inspection revealed that all associations are likely due to cross-hybridization with sex chromosome regions during genotyping. We report loci with potential for mis-hybridization found on commonly used genotyping platforms that should be carefully considered in future genetic studies of sex-specific differences. Despite being well powered to detect allele frequency differences of up to 0.8% between the sexes, we do not detect clear evidence for this signature of sexually antagonistic viability selection on autosomal variation. These findings suggest a lack of strong ongoing sexually antagonistic viability selection acting on single locus autosomal variation in humans.