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Human relationships, as documented by family trees, can elucidate the heritability of a host of medical and biological parameters. Kaplanis et al. collected 86 million publicly available profiles from a crowd-sourced genealogy website and used them to examine the genetic architecture of human longevity and migration patterns (see the Perspective by Lussier and Keinan). Various models of inheritance suggested that life span is predominantly attributable to additive genetic effects, with a smaller component from dominant genetic inheritance. The data also suggested that relatedness between individuals is less attributable to advances in human transportation than to cultural changes. Science , this issue p. 171 ; see also p. 153 Crowdsourced genealogy data are useful for testing genetic hypotheses. Family trees have vast applications in fields as diverse as genetics, anthropology, and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. We collected 86 million profiles from publicly available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of human longevity and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other data sets with our resource.

Whole genome sequencing (WGS) studies have estimated the human germline mutation rate per basepair per generation (~1.2 × 10 −8 ) to be higher than in mice (3.5–5.4 × 10 −9 ). In humans, most germline mutations are paternal in origin and numbers of mutations per offspring increase with paternal and maternal age. Here we estimate germline mutation rates and spectra in six multi-sibling mouse pedigrees and compare to three multi-sibling human pedigrees. In both species we observe a paternal mutation bias, a parental age effect, and a highly mutagenic first cell division contributing to the embryo. We also observe differences between species in mutation spectra, in mutation rates per cell division, and in the parental bias of mutations in early embryogenesis. These differences between species likely result from both species-specific differences in cellular genealogies of the germline, as well as biological differences within the same stage of embryogenesis or gametogenesis. Estimates of mutation rates differ between species. Here, Lindsay et al. perform side-by-side analyses of germline mutation rates using multi-sibling mouse and human pedigrees and find different mutation rates between species, also stratified by sex and temporal stage of mutation acquisition.

De novo germline mutation is an important factor in the evolution of allelic diversity and disease predisposition in a population. Here, we study the influence of genetically-inferred ancestry and environmental factors on de novo mutation rates and spectra. Using a genetically diverse sample of ~10 K whole-genome sequenced trios, one of the largest de novo mutation catalogues to date, we found that genetically-inferred ancestry is associated with modest but significant changes in both germline mutation rate and spectra across continental populations. These effects may be due to genetic or environmental factors correlated with ancestry. We find epidemiological evidence that cigarette smoking is significantly associated with increased de novo mutation rate, but it does not mediate the observed ancestry effects. Investigation of several other potential mutagenic factors using Mendelian randomisation showed no consistent effects, except for age at  menopause, where factors increasing this corresponded to a reduction in de novo mutation rate. Overall, our study sheds light on factors influencing de novo mutation rates and spectra. Here the authors analyze de novo mutations in >10,000 parent-offspring trios and find that ancestry and smoking independently associate with mutation rate, but that common genetic variants likely contribute little to mutation rate variation.

Mutations in the germline generates all evolutionary genetic variation and is a cause of genetic disease. Parental age is the primary determinant of the number of new germline mutations in an individual’s genome 1 , 2 . Here we analysed the genome-wide sequences of 21,879 families with rare genetic diseases and identified 12 individuals with a hypermutated genome with between two and seven times more de novo single-nucleotide variants than expected. In most families (9 out of 12), the excess mutations came from the father. Two families had genetic drivers of germline hypermutation, with fathers carrying damaging genetic variation in DNA-repair genes. For five of the families, paternal exposure to chemotherapeutic agents before conception was probably a key driver of hypermutation. Our results suggest that the germline is well protected from mutagenic effects, hypermutation is rare, the number of excess mutations is relatively modest and most individuals with a hypermutated genome will not have a genetic disease. A study of 21,879 families with rare genetic diseases identifies 12 with 2- to 7-fold excess of germline mutations, most of which are due to DNA repair defects or exposure to mutagenic chemotherapy, although most individuals with a hypermutated genome will not have a genetic disease.

The DDD study recruited more than 13,500 families with probands with severe, probably monogenic disorders in the United Kingdom and Ireland and obtained a genetic diagnosis in approximately 41% of probands.

During activation, T cells undergo extensive gene expression changes that shape the properties of cells to exert their effector function. Understanding the regulation of this process could help explain how genetic variants predispose to immune diseases. Here, we mapped genetic effects on gene expression (expression quantitative trait loci (eQTLs)) using single-cell transcriptomics. We profiled 655,349 CD4 + T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. This identified 38 cell clusters, including transient clusters that were only present at individual time points of activation. We found 6,407 genes whose expression was correlated with genetic variation, of which 2,265 (35%) were dynamically regulated during activation. Furthermore, 127 genes were regulated by variants associated with immune-mediated diseases, with significant enrichment for dynamic effects. Our results emphasize the importance of studying context-specific gene expression regulation and provide insights into the mechanisms underlying genetic susceptibility to immune-mediated diseases. Single-cell RNA sequencing of CD4 + naive and memory T cells from 119 individuals generates an expression quantitative trait locus (eQTL) map during T cell activation, identifying 6,407 eQTL genes, including 2,265 that are dynamically regulated.

The genetics of developmental disorders (DDs) is complex. Martin et al. wanted to determine the degree of recessive inheritance of DDs in protein-coding genes. They examined the exomes of more than 6000 families in populations with high and low proportions of consanguineous marriages. They found that 3.6% of DDs in individuals of European ancestry involved recessive coding disorders, less than a tenth of the levels previously estimated. Furthermore, among South Asians with high parental relatedness, rather than most of the disorders arising from inherited variants, fewer than half had a recessive coding diagnosis. Science , this issue p. 1161 Exome sequencing of more than 6000 families identifies a lower rate of recessive inheritance than previously estimated. We estimated the genome-wide contribution of recessive coding variation in 6040 families from the Deciphering Developmental Disorders study. The proportion of cases attributable to recessive coding variants was 3.6% in patients of European ancestry, compared with 50% explained by de novo coding mutations. It was higher (31%) in patients with Pakistani ancestry, owing to elevated autozygosity. Half of this recessive burden is attributable to known genes. We identified two genes not previously associated with recessive developmental disorders, KDM5B and EIF3F , and functionally validated them with mouse and cellular models. Our results suggest that recessive coding variants account for a small fraction of currently undiagnosed nonconsanguineous individuals, and that the role of noncoding variants, incomplete penetrance, and polygenic mechanisms need further exploration.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders.sup.1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations.sup.1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders.sup.1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations.sup.1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders.sup.1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations.sup.1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.