A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds.

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

High polygenic risk score is a risk factor associated with colorectal cancer based on data from the UK Biobank.

Colorectal cancer (CRC) is a common cancer among both men and women and is one of the leading causes of cancer death worldwide. It is important to identify risk factors that may be used to help reduce morbidity and mortality of the disease. We used a case-control study design to explore the association between CRC, polygenic risk scores (PRS), and other factors. We extracted data about 2,585 CRC cases and 9,362 controls from the UK Biobank, calculated the PRS for these cases and controls based on 140 single nucleotide polymorphisms, and performed logistic regression analyses for the 11,947 cases and controls, for an older group (ages 50+), and for a younger group (younger than 50). Five significant risk factors were identified when all 11,947 cases and controls were considered. These factors were, in descending order of the values of the adjusted odds ratios (aOR), high PRS (aOR: 2.70, CI: 2.27-3.19), male sex (aOR: 1.52, CI: 1.39-1.66), unemployment (aOR: 1.47, CI: 1.17-1.85), family history of CRC (aOR: 1.44, CI: 1.28-1.62), and age (aOR: 1.01, CI: 1.01-1.02). These five risk factors also remained significant in the older group. For the younger group, only high PRS (aOR: 2.87, CI: 1.65-5.00) and family history of CRC (aOR: 1.73, CI: 1.12-2.67) were significant risk factors. These findings indicate that genetic risk for the disease is a significant risk factor for CRC even after adjusting for family history. Additional studies are needed to examine this association using larger samples and different population groups.

Deep learning based phenotyping of medical images improves power for gene discovery of complex disease.

Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.

The genetic architecture and evolution of the human skeletal form.

The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions. Polygenic score analysis identified specific associations between osteoarthritis and hip and knee SPs. In contrast to other traits, SP loci were enriched in human accelerated regions and in regulatory elements of genes that are differentially expressed between humans and great apes. Combined, our work identifies specific genetic variants that affect the skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

Understanding natural selection in Holocene Europe using multi-locus genotype identity scans.

Ancient DNA (aDNA) has been a revolutionary technology in understanding human history but has not been used extensively to study natural selection as large sample sizes to study allele frequency changes over time have thus far not been available. Here, we examined a time transect of 708 published samples over the past 7,000 years of European history using multi-locus genotype-based selection scans. As aDNA data is affected by high missingness, ascertainment bias, DNA damage, random allele calling, and is unphased, we first validated our selection scan, G12ancient, on simulated data resembling aDNA under a demographic model that captures broad features of the allele frequency spectrum of European genomes as well as positive controls that have been previously identified and functionally validated in modern European datasets on data from ancient individuals from time periods very close to the present time. We then applied our statistic to the aDNA time transect to detect and resolve the timing of natural selection occurring genome wide and found several candidates of selection across the different time periods that had not been picked up by selection scans using single SNP allele frequency approaches. In addition, enrichment analysis discovered multiple categories of complex traits that might be under adaptation across these periods. Our results demonstrate the utility of applying different types of selection scans to aDNA to uncover putative selection signals at loci in the ancient past that might have been masked in modern samples.

The genetic architecture of the human skeletal form.

The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood. Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height. All skeletal proportions are highly heritable (∻40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions. We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes. Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

1,000 ancient genomes uncover 10,000 years of natural selection in Europe.

Ancient DNA has revolutionized our understanding of human population history. However, its potential to examine how rapid cultural evolution to new lifestyles may have driven biological adaptation has not been met, largely due to limited sample sizes. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, providing a dataset that is large enough to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods. We identified 25 genetic loci with rapid changes in frequency during these periods, a majority of which were previously undetected. Signals specific to the Neolithic transition are associated with body weight, diet, and lipid metabolism-related phenotypes. They also include immune phenotypes, most notably a locus that confers immunity to Salmonella infection at a time when ancient Salmonella genomes have been shown to adapt to human hosts, thus providing a possible example of human-pathogen co-evolution. In the Bronze Age, selection signals are enriched near genes involved in pigmentation and immune-related traits, including at a key human protein interactor of SARS-CoV-2. Only in the Historical period do the selection candidates we detect largely mirror previously-reported signals, highlighting how the statistical power of previous studies was limited to the last few millennia. The Historical period also has multiple signals associated with vitamin D binding, providing evidence that lactase persistence may have been part of an oligogenic adaptation for efficient calcium uptake and challenging the theory that its adaptive value lies only in facilitating caloric supplementation during times of scarcity. Finally, we detect selection on complex traits in all three periods, including selection favoring variants that reduce body weight in the Neolithic. In the Historical period, we detect selection favoring variants that increase risk for cardiovascular disease plausibly reflecting selection for a more active inflammatory response that would have been adaptive in the face of increased infectious disease exposure. Our results provide an evolutionary rationale for the high prevalence of these deadly diseases in modern societies today and highlight the unique power of ancient DNA in elucidating biological change that accompanied the profound cultural transformations of recent human history.

Millennia-old coral holobiont DNA provides insight into future adaptive trajectories.

Ancient DNA (aDNA) has been applied to evolutionary questions across a wide variety of taxa. Here, for the first time, we utilized aDNA from millennia-old fossil coral fragments to gain new insights into a rapidly declining western Atlantic reef ecosystem. We sampled four Acropora palmata fragments (dated 4215 BCE to 1099 CE) obtained from two Florida Keys reef cores. From these samples, we established that it is possible both to sequence aDNA from reef cores and place the data in the context of modern-day genetic variation. We recovered varying amounts of nuclear DNA exhibiting the characteristic signatures of aDNA from the A. palmata fragments. To describe the holobiont sensu lato, which plays a crucial role in reef health, we utilized metagenome-assembled genomes as a reference to identify a large additional proportion of ancient microbial DNA from the samples. The samples shared many common microbes with modern-day coral holobionts from the same region, suggesting remarkable holobiont stability over time. Despite efforts, we were unable to recover ancient Symbiodiniaceae reads from the samples. Comparing the ancient A. palmata data to whole-genome sequencing data from living acroporids, we found that while slightly distinct, ancient samples were most closely related to individuals of their own species. Together, these results provide a proof-of-principle showing that it is possible to carry out direct analysis of coral holobiont change over time, which lays a foundation for studying the impacts of environmental stress and evolutionary constraints.

miqoGraph: fitting admixture graphs using mixed-integer quadratic optimization.

SUMMARY: Admixture graphs represent the genetic relationship between a set of populations through splits, drift and admixture. In this article, we present the Julia package miqoGraph, which uses mixed-integer quadratic optimization to fit topology, drift lengths and admixture proportions simultaneously. Through applications of miqoGraph to both simulated and real data, we show that integer optimization can greatly speed up and automate what is usually an arduous manual process. AVAILABILITY AND IMPLEMENTATION: https://github.com/juliayyan/PhylogeneticTrees.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction: A restricted spectrum of missense KMT2D variants cause a multiple malformations disorder distinct from Kabuki syndrome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A restricted spectrum of missense KMT2D variants cause a multiple malformations disorder distinct from Kabuki syndrome.

PURPOSE: To investigate if specific exon 38 or 39 KMT2D missense variants (MVs) cause a condition distinct from Kabuki syndrome type 1 (KS1). METHODS: Multiple individuals, with MVs in exons 38 or 39 of KMT2D that encode a highly conserved region of 54 amino acids flanked by Val3527 and Lys3583, were identified and phenotyped. Functional tests were performed to study their pathogenicity and understand the disease mechanism. RESULTS: The consistent clinical features of the affected individuals, from seven unrelated families, included choanal atresia, athelia or hypoplastic nipples, branchial sinus abnormalities, neck pits, lacrimal duct anomalies, hearing loss, external ear malformations, and thyroid abnormalities. None of the individuals had intellectual disability. The frequency of clinical features, objective software-based facial analysis metrics, and genome-wide peripheral blood DNA methylation patterns in these patients were significantly different from that of KS1. Circular dichroism spectroscopy indicated that these MVs perturb KMT2D secondary structure through an increased disordered to ɑ-helical transition. CONCLUSION: KMT2D MVs located in a specific region spanning exons 38 and 39 and affecting highly conserved residues cause a novel multiple malformations syndrome distinct from KS1. Unlike KMT2D haploinsufficiency in KS1, these MVs likely result in disease through a dominant negative mechanism.

An Ancient Harappan Genome Lacks Ancestry from Steppe Pastoralists or Iranian Farmers.

We report an ancient genome from the Indus Valley Civilization (IVC). The individual we sequenced fits as a mixture of people related to ancient Iranians (the largest component) and Southeast Asian hunter-gatherers, a unique profile that matches ancient DNA from 11 genetic outliers from sites in Iran and Turkmenistan in cultural communication with the IVC. These individuals had little if any Steppe pastoralist-derived ancestry, showing that it was not ubiquitous in northwest South Asia during the IVC as it is today. The Iranian-related ancestry in the IVC derives from a lineage leading to early Iranian farmers, herders, and hunter-gatherers before their ancestors separated, contradicting the hypothesis that the shared ancestry between early Iranians and South Asians reflects a large-scale spread of western Iranian farmers east. Instead, sampled ancient genomes from the Iranian plateau and IVC descend from different groups of hunter-gatherers who began farming without being connected by substantial movement of people.

The formation of human populations in South and Central Asia.

By sequencing 523 ancient humans, we show that the primary source of ancestry in modern South Asians is a prehistoric genetic gradient between people related to early hunter-gatherers of Iran and Southeast Asia. After the Indus Valley Civilization's decline, its people mixed with individuals in the southeast to form one of the two main ancestral populations of South Asia, whose direct descendants live in southern India. Simultaneously, they mixed with descendants of Steppe pastoralists who, starting around 4000 years ago, spread via Central Asia to form the other main ancestral population. The Steppe ancestry in South Asia has the same profile as that in Bronze Age Eastern Europe, tracking a movement of people that affected both regions and that likely spread the distinctive features shared between Indo-Iranian and Balto-Slavic languages.

Estimating the human mutation rate from autozygous segments reveals population differences in human mutational processes.

Heterozygous mutations within homozygous sequences descended from a recent common ancestor offer a way to ascertain de novo mutations across multiple generations. Using exome sequences from 3222 British-Pakistani individuals with high parental relatedness, we estimate a mutation rate of 1.45 ± 0.05 × 10-8 per base pair per generation in autosomal coding sequence, with a corresponding non-crossover gene conversion rate of 8.75 ± 0.05 × 10-6 per base pair per generation. This is at the lower end of exome mutation rates previously estimated in parent-offspring trios, suggesting that post-zygotic mutations contribute little to the human germ-line mutation rate. We find frequent recurrence of mutations at polymorphic CpG sites, and an increase in C to T mutations in a 5' CCG 3' to 5' CTG 3' context in the Pakistani population compared to Europeans, suggesting that mutational processes have evolved rapidly between human populations.Estimates of human mutation rates differ substantially based on the approach. Here, the authors present a multi-generational estimate from the autozygous segment in a non-European population that gives insight into the contribution of post-zygotic mutations and population-specific mutational processes.

Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations.

The genetic features of isolated populations can boost power in complex-trait association studies, and an in-depth understanding of how their genetic variation has been shaped by their demographic history can help leverage these advantageous characteristics. Here, we perform a comprehensive investigation using 3,059 newly generated low-depth whole-genome sequences from eight European isolates and two matched general populations, together with published data from the 1000 Genomes Project and UK10K. Sequencing data give deeper and richer insights into population demography and genetic characteristics than genotype-chip data, distinguishing related populations more effectively and allowing their functional variants to be studied more fully. We demonstrate relaxation of purifying selection in the isolates, leading to enrichment of rare and low-frequency functional variants, using novel statistics, DVxy and SVxy. We also develop an isolation-index (Isx) that predicts the overall level of such key genetic characteristics and can thus help guide population choice in future complex-trait association studies.

Extensive Proliferation of a Subset of Differentiated, yet Plastic, Medial Vascular Smooth Muscle Cells Contributes to Neointimal Formation in Mouse Injury and Atherosclerosis Models.

RATIONALE: Vascular smooth muscle cell (VSMC) accumulation is a hallmark of atherosclerosis and vascular injury. However, fundamental aspects of proliferation and the phenotypic changes within individual VSMCs, which underlie vascular disease, remain unresolved. In particular, it is not known whether all VSMCs proliferate and display plasticity or whether individual cells can switch to multiple phenotypes. OBJECTIVE: To assess whether proliferation and plasticity in disease is a general characteristic of VSMCs or a feature of a subset of cells. METHODS AND RESULTS: Using multicolor lineage labeling, we demonstrate that VSMCs in injury-induced neointimal lesions and in atherosclerotic plaques are oligoclonal, derived from few expanding cells. Lineage tracing also revealed that the progeny of individual VSMCs contributes to both alpha smooth muscle actin (aSma)-positive fibrous cap and Mac3-expressing macrophage-like plaque core cells. Costaining for phenotypic markers further identified a double-positive aSma+ Mac3+ cell population, which is specific to VSMC-derived plaque cells. In contrast, VSMC-derived cells generating the neointima after vascular injury generally retained the expression of VSMC markers and the upregulation of Mac3 was less pronounced. Monochromatic regions in atherosclerotic plaques and injury-induced neointima did not contain VSMC-derived cells expressing a different fluorescent reporter protein, suggesting that proliferation-independent VSMC migration does not make a major contribution to VSMC accumulation in vascular disease. CONCLUSIONS: We demonstrate that extensive proliferation of a low proportion of highly plastic VSMCs results in the observed VSMC accumulation after injury and in atherosclerotic plaques. Therapeutic targeting of these hyperproliferating VSMCs might effectively reduce vascular disease without affecting vascular integrity.

Human Knockout Carriers: Dead, Diseased, Healthy, or Improved?

Whole-genome and whole-exome sequence data from large numbers of individuals reveal that we all carry many variants predicted to inactivate genes (knockouts). This discovery raises questions about the phenotypic consequences of these knockouts and potentially allows us to study human gene function through the investigation of homozygous loss-of-function carriers. Here, we discuss strategies, recent results, and future prospects for large-scale human knockout studies. We examine their relevance to studying gene function, population genetics, and importantly, the implications for accurate clinical interpretations.

Health and population effects of rare gene knockouts in adult humans with related parents.

Examining complete gene knockouts within a viable organism can inform on gene function. We sequenced the exomes of 3222 British adults of Pakistani heritage with high parental relatedness, discovering 1111 rare-variant homozygous genotypes with predicted loss of function (knockouts) in 781 genes. We observed 13.7% fewer homozygous knockout genotypes than we expected, implying an average load of 1.6 recessive-lethal-equivalent loss-of-function (LOF) variants per adult. When genetic data were linked to the individuals' lifelong health records, we observed no significant relationship between gene knockouts and clinical consultation or prescription rate. In this data set, we identified a healthy PRDM9-knockout mother and performed phased genome sequencing on her, her child, and control individuals. Our results show that meiotic recombination sites are localized away from PRDM9-dependent hotspots. Thus, natural LOF variants inform on essential genetic loci and demonstrate PRDM9 redundancy in humans.

BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data.

UNLABELLED: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity. AVAILABILITY AND IMPLEMENTATION: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools CONTACT: vn2@sanger.ac.uk or pd3@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.