The genetic history of Scandinavia from the Roman Iron Age to the present.

We investigate a 2,000-year genetic transect through Scandinavia spanning the Iron Age to the present, based on 48 new and 249 published ancient genomes and genotypes from 16,638 modern individuals. We find regional variation in the timing and magnitude of gene flow from three sources: the eastern Baltic, the British-Irish Isles, and southern Europe. British-Irish ancestry was widespread in Scandinavia from the Viking period, whereas eastern Baltic ancestry is more localized to Gotland and central Sweden. In some regions, a drop in current levels of external ancestry suggests that ancient immigrants contributed proportionately less to the modern Scandinavian gene pool than indicated by the ancestry of genomes from the Viking and Medieval periods. Finally, we show that a north-south genetic cline that characterizes modern Scandinavians is mainly due to the differential levels of Uralic ancestry and that this cline existed in the Viking Age and possibly earlier.

Influence of autozygosity on common disease risk across the phenotypic spectrum.

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.

Generation and transmission of interlineage recombinants in the SARS-CoV-2 pandemic.

We present evidence for multiple independent origins of recombinant SARS-CoV-2 viruses sampled from late 2020 and early 2021 in the United Kingdom. Their genomes carry single-nucleotide polymorphisms and deletions that are characteristic of the B.1.1.7 variant of concern but lack the full complement of lineage-defining mutations. Instead, the remainder of their genomes share contiguous genetic variation with non-B.1.1.7 viruses circulating in the same geographic area at the same time as the recombinants. In four instances, there was evidence for onward transmission of a recombinant-origin virus, including one transmission cluster of 45 sequenced cases over the course of 2 months. The inferred genomic locations of recombination breakpoints suggest that every community-transmitted recombinant virus inherited its spike region from a B.1.1.7 parental virus, consistent with a transmission advantage for B.1.1.7's set of mutations.

Public health actions to control new SARS-CoV-2 variants.

Recent reports suggest that some SARS-CoV-2 genetic variants, such as B.1.1.7, might be more transmissible and are quickly spreading around the world. As the emergence of more transmissible variants could exacerbate the pandemic, we provide public health guidance for increased surveillance and measures to reduce community transmission.

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant.

Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19.

The development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and therapeutics will depend on understanding viral immunity. We studied T cell memory in 42 patients following recovery from COVID-19 (28 with mild disease and 14 with severe disease) and 16 unexposed donors, using interferon-γ-based assays with peptides spanning SARS-CoV-2 except ORF1. The breadth and magnitude of T cell responses were significantly higher in severe as compared with mild cases. Total and spike-specific T cell responses correlated with spike-specific antibody responses. We identified 41 peptides containing CD4+ and/or CD8+ epitopes, including six immunodominant regions. Six optimized CD8+ epitopes were defined, with peptide-MHC pentamer-positive cells displaying the central and effector memory phenotype. In mild cases, higher proportions of SARS-CoV-2-specific CD8+ T cells were observed. The identification of T cell responses associated with milder disease will support an understanding of protective immunity and highlights the potential of including non-spike proteins within future COVID-19 vaccine design.

Going Germline: Mitochondrial Replacement as a Guide to Genome Editing.

Mitochondrial replacement (MR) serves as a crucial test case and learning guide for the scientific, ethical, and regulatory challenges of future reproductive breakthroughs. The lessons learned from the regulatory review process of MR over the last decade promise to enrich the emerging dialog over genome editing.

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome.

FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

Polygenic architecture of rare coding variation across 394,783 exomes.

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes1-3. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear4. Here we quantify the heritability explained by the gene-wise burden of rare coding variants across 22 common traits and diseases in 394,783 UK Biobank exomes5. Rare coding variants (allele frequency < 1 × 10-3) explain 1.3% (s.e. = 0.03%) of phenotypic variance on average-much less than common variants-and most burden heritability is explained by ultrarare loss-of-function variants (allele frequency < 1 × 10-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in significant genes, while common-variant heritability is more polygenic, and burden heritability is also more strongly concentrated in constrained genes. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results indicate that rare coding variants will implicate a tractable number of large-effect genes, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.

Polygenic scoring accuracy varies across the genetic ancestry continuum.

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

Rare variant associations with plasma protein levels in the UK Biobank.

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Large-scale plasma proteomics comparisons through genetics and disease associations.

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

Mexican Biobank advances population and medical genomics of diverse ancestries.

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

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Meet the author: Alice Clare Newman.

We talk to Alice Clare Newman, new mother, postdoc, and first author of "Immune-regulated IDO1-dependent tryptophan metabolism is source of one-carbon units for pancreatic cancer and stellate cells," about her paper, juggling family and career, and life-changing moments that led to the realization that life doesn't wait for science.

Large-scale migration into Britain during the Middle to Late Bronze Age.

Present-day people from England and Wales have more ancestry derived from early European farmers (EEF) than did people of the Early Bronze Age1. To understand this, here we generated genome-wide data from 793 individuals, increasing data from the Middle to the Late Bronze Age and Iron Age in Britain by 12-fold, and western and central Europe by 3.5-fold. Between 1000 and 875 BC, EEF ancestry increased in southern Britain (England and Wales) but not northern Britain (Scotland) due to incorporation of migrants who arrived at this time and over previous centuries, and who were genetically most similar to ancient individuals from France. These migrants contributed about half the ancestry of people of England and Wales from the Iron Age, thereby creating a plausible vector for the spread of early Celtic languages into Britain. These patterns are part of a broader trend of EEF ancestry becoming more similar across central and western Europe in the Middle to the Late Bronze Age, coincident with archaeological evidence of intensified cultural exchange2-6. There was comparatively less gene flow from continental Europe during the Iron Age, and the independent genetic trajectory in Britain is also reflected in the rise of the allele conferring lactase persistence to approximately 50% by this time compared to approximately 7% in central Europe where it rose rapidly in frequency only a millennium later. This suggests that dairy products were used in qualitatively different ways in Britain and in central Europe over this period.

The sequences of 150,119 genomes in the UK Biobank.

Detailed knowledge of how diversity in the sequence of the human genome affects phenotypic diversity depends on a comprehensive and reliable characterization of both sequences and phenotypic variation. Over the past decade, insights into this relationship have been obtained from whole-exome sequencing or whole-genome sequencing of large cohorts with rich phenotypic data1,2. Here we describe the analysis of whole-genome sequencing of 150,119 individuals from the UK Biobank3. This constitutes a set of high-quality variants, including 585,040,410 single-nucleotide polymorphisms, representing 7.0% of all possible human single-nucleotide polymorphisms, and 58,707,036 indels. This large set of variants allows us to characterize selection based on sequence variation within a population through a depletion rank score of windows along the genome. Depletion rank analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UK Biobank: a large British Irish cohort, a smaller African cohort and a South Asian cohort. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large-scale whole-genome sequencing studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on whole-exome sequencing and/or imputation.

SARS-CoV-2 is associated with changes in brain structure in UK Biobank.

There is strong evidence of brain-related abnormalities in COVID-191-13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51-81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans-with 141 days on average separating their diagnosis and the second scan-as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.