De novo structural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families.

Each human genome includes de novo mutations that arose during gametogenesis. While these germline mutations represent a fundamental source of new genetic diversity, they can also create deleterious alleles that impact fitness. Whereas the rate and patterns of point mutations in the human germline are now well understood, far less is known about the frequency and features that impact de novo structural variants (dnSVs). We report a family-based study of germline mutations among 9,599 human genomes from 33 multigenerational CEPH-Utah families and 2,384 families from the Simons Foundation Autism Research Initiative. We find that de novo structural mutations detected by alignment-based, short-read WGS occur at an overall rate of at least 0.160 events per genome in unaffected individuals, and we observe a significantly higher rate (0.206 per genome) in ASD-affected individuals. In both probands and unaffected samples, nearly 73% of de novo structural mutations arose in paternal gametes, and we predict most de novo structural mutations to be caused by mutational mechanisms that do not require sequence homology. After multiple testing correction, we did not observe a statistically significant correlation between parental age and the rate of de novo structural variation in offspring. These results highlight that a spectrum of mutational mechanisms contribute to germline structural mutations and that these mechanisms most likely have markedly different rates and selective pressures than those leading to point mutations.

Familial aggregation of stillbirth: A pedigree analysis of a matched case-control study.

OBJECTIVE: To determine whether stillbirth aggregates in families and quantify its familial risk using extended pedigrees. DESIGN: State-wide matched case-control study. SETTING: Utah, United States. POPULATION: Stillbirth cases (n = 9404) and live birth controls (18 808) between 1978 and 2019. METHODS: Using the Utah Population Database, a population-based genealogical resource linked with state fetal death and birth records, we identified high-risk pedigrees with excess familial aggregation of stillbirth using the Familial Standardised Incidence Ratio (FSIR). Stillbirth odds ratio (OR) for first-degree relatives (FDR), second-degree relatives (SDR) and third-degree relatives (TDR) of parents with a stillbirth (affected) and live birth (unaffected) were estimated using logistic regression models. MAIN OUTCOME MEASURES: Familial aggregation estimated using FSIR, and stillbirth OR estimated for FDR, SDR and TDR of affected and unaffected parents using logistic regression models. RESULTS: We identified 390 high-risk pedigrees with evidence for excess familial aggregation (FSIR ≥2.00; P-value <0.05). FDRs, SDRs and TDRs of affected parents had 1.14-fold (95% confidence interval [CI]: 1.04-1.26), 1.22-fold (95% CI 1.11-1.33) and 1.15-fold (95% CI 1.08-1.21) higher stillbirth odds compared with FDRs, SDRs and TDRs of unaffected parents, respectively. Parental sex-specific analyses showed male FDRs, SDRs and TDRs of affected fathers had 1.22-fold (95% CI 1.02-1.47), 1.38-fold (95% CI 1.17-1.62) and 1.17-fold (95% CI 1.05-1.30) higher stillbirth odds compared with those of unaffected fathers, respectively. FDRs, SDRs and TDRs of affected mothers had 1.12-fold (95% CI 0.98-1.28), 1.09-fold (95% CI 0.96-1.24) and 1.15-fold (95% CI 1.06-1.24) higher stillbirth odds compared with those of unaffected mothers, respectively. CONCLUSIONS: We provide evidence for familial aggregation of stillbirth. Our findings warrant investigation into genes associated with stillbirth and underscore the need to design large-scale studies to determine the genetic architecture of stillbirth.

Pedigree-based estimation of human mobile element retrotransposition rates.

Germline mutation rates in humans have been estimated for a variety of mutation types, including single-nucleotide and large structural variants. Here, we directly measure the germline retrotransposition rate for the three active retrotransposon elements: L1, Alu, and SVA. We used three tools for calling mobile element insertions (MEIs) (MELT, RUFUS, and TranSurVeyor) on blood-derived whole-genome sequence (WGS) data from 599 CEPH individuals, comprising 33 three-generation pedigrees. We identified 26 de novo MEIs in 437 births. The retrotransposition rate estimates for Alu elements, one in 40 births, is roughly half the rate estimated using phylogenetic analyses, a difference in magnitude similar to that observed for single-nucleotide variants. The L1 retrotransposition rate is one in 63 births and is within range of previous estimates (1:20-1:200 births). The SVA retrotransposition rate, one in 63 births, is much higher than the previous estimate of one in 900 births. Our large, three-generation pedigrees allowed us to assess parent-of-origin effects and the timing of insertion events in either gametogenesis or early embryonic development. We find a statistically significant paternal bias in Alu retrotransposition. Our study represents the first in-depth analysis of the rate and dynamics of human retrotransposition from WGS data in three-generation human pedigrees.

TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data.

Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alus are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alus and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline - TypeTE - which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a high-quality set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

Overlooked roles of DNA damage and maternal age in generating human germline mutations.

The textbook view that most germline mutations in mammals arise from replication errors is indirectly supported by the fact that there are both more mutations and more cell divisions in the male than in the female germline. When analyzing large de novo mutation datasets in humans, we find multiple lines of evidence that call that view into question. Notably, despite the drastic increase in the ratio of male to female germ cell divisions after the onset of spermatogenesis, even young fathers contribute three times more mutations than young mothers, and this ratio barely increases with parental age. This surprising finding points to a substantial contribution of damage-induced mutations. Indeed, C-to-G transversions and CpG transitions, which together constitute over one-fourth of all base substitution mutations, show genomic distributions and sex-specific age dependencies indicative of double-strand break repair and methylation-associated damage, respectively. Moreover, we find evidence that maternal age at conception influences the mutation rate both because of the accumulation of damage in oocytes and potentially through an influence on the number of postzygotic mutations in the embryo. These findings reveal underappreciated roles of DNA damage and maternal age in the genesis of human germline mutations.

Evolutionary history of Tibetans inferred from whole-genome sequencing.

The indigenous people of the Tibetan Plateau have been the subject of much recent interest because of their unique genetic adaptations to high altitude. Recent studies have demonstrated that the Tibetan EPAS1 haplotype is involved in high altitude-adaptation and originated in an archaic Denisovan-related population. We sequenced the whole-genomes of 27 Tibetans and conducted analyses to infer a detailed history of demography and natural selection of this population. We detected evidence of population structure between the ancestral Han and Tibetan subpopulations as early as 44 to 58 thousand years ago, but with high rates of gene flow until approximately 9 thousand years ago. The CMS test ranked EPAS1 and EGLN1 as the top two positive selection candidates, and in addition identified PTGIS, VDR, and KCTD12 as new candidate genes. The advantageous Tibetan EPAS1 haplotype shared many variants with the Denisovan genome, with an ancient gene tree divergence between the Tibetan and Denisovan haplotypes of about 1 million years ago. With the exception of EPAS1, we observed no evidence of positive selection on Denisovan-like haplotypes.

PADRE: Pedigree-Aware Distant-Relationship Estimation.

Accurate estimation of shared ancestry is an important component of many genetic studies; current prediction tools accurately estimate pairwise genetic relationships up to the ninth degree. Pedigree-aware distant-relationship estimation (PADRE) combines relationship likelihoods generated by estimation of recent shared ancestry (ERSA) with likelihoods from family networks reconstructed by pedigree reconstruction and identification of a maximum unrelated set (PRIMUS), improving the power to detect distant relationships between pedigrees. Using PADRE, we estimated relationships from simulated pedigrees and three extended pedigrees, correctly predicting 20% more fourth- through ninth-degree simulated relationships than when using ERSA alone. By leveraging pedigree information, PADRE can even identify genealogical relationships between individuals who are genetically unrelated. For example, although 95% of 13(th)-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50% of 13(th)-degree relationships to within one degree of relatedness. The improvement in prediction accuracy was consistent between simulated and actual pedigrees. We also applied PADRE to the HapMap3 CEU samples and report new cryptic relationships and validation of previously described relationships between families. PADRE greatly expands the range of relationships that can be estimated by using genetic data in pedigrees.

Extremely low-coverage whole genome sequencing in South Asians captures population genomics information.

BACKGROUND: The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined. RESULTS: South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples. CONCLUSIONS: Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies.

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

Relationship estimation from whole-genome sequence data.

The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.

The Y-chromosome tree bursts into leaf: 13,000 high-confidence SNPs covering the majority of known clades.

Many studies of human populations have used the male-specific region of the Y chromosome (MSY) as a marker, but MSY sequence variants have traditionally been subject to ascertainment bias. Also, dating of haplogroups has relied on Y-specific short tandem repeats (STRs), involving problems of mutation rate choice, and possible long-term mutation saturation. Next-generation sequencing can ascertain single nucleotide polymorphisms (SNPs) in an unbiased way, leading to phylogenies in which branch-lengths are proportional to time, and allowing the times-to-most-recent-common-ancestor (TMRCAs) of nodes to be estimated directly. Here we describe the sequencing of 3.7 Mb of MSY in each of 448 human males at a mean coverage of 51×, yielding 13,261 high-confidence SNPs, 65.9% of which are previously unreported. The resulting phylogeny covers the majority of the known clades, provides date estimates of nodes, and constitutes a robust evolutionary framework for analyzing the history of other classes of mutation. Different clades within the tree show subtle but significant differences in branch lengths to the root. We also apply a set of 23 Y-STRs to the same samples, allowing SNP- and STR-based diversity and TMRCA estimates to be systematically compared. Ongoing purifying selection is suggested by our analysis of the phylogenetic distribution of nonsynonymous variants in 15 MSY single-copy genes.

Mobile element biology: new possibilities with high-throughput sequencing.

Mobile elements comprise more than half of the human genome, but until recently their large-scale detection was time consuming and challenging. With the development of new high-throughput sequencing (HTS) technologies, the complete spectrum of mobile element variation in humans can now be identified and analyzed. Thousands of new mobile element insertions (MEIs) have been discovered, yielding new insights into mobile element biology, evolution, and genomic variation. Here, we review several high-throughput methods, with an emphasis on techniques that specifically target MEIs in humans. We highlight recent applications of these methods in evolutionary studies and in the analysis of somatic alterations in human normal and tumor tissues.

Germline mutations in NFKB2 implicate the noncanonical NF-κB pathway in the pathogenesis of common variable immunodeficiency.

Common variable immunodeficiency (CVID) is a heterogeneous disorder characterized by antibody deficiency, poor humoral response to antigens, and recurrent infections. To investigate the molecular cause of CVID, we carried out exome sequence analysis of a family diagnosed with CVID and identified a heterozygous frameshift mutation, c.2564delA (p.Lys855Serfs(∗)7), in NFKB2 affecting the C terminus of NF-κB2 (also known as p100/p52 or p100/p49). Subsequent screening of NFKB2 in 33 unrelated CVID-affected individuals uncovered a second heterozygous nonsense mutation, c.2557C>T (p.Arg853(∗)), in one simplex case. Affected individuals in both families presented with an unusual combination of childhood-onset hypogammaglobulinemia with recurrent infections, autoimmune features, and adrenal insufficiency. NF-κB2 is the principal protein involved in the noncanonical NF-κB pathway, is evolutionarily conserved, and functions in peripheral lymphoid organ development, B cell development, and antibody production. In addition, Nfkb2 mouse models demonstrate a CVID-like phenotype with hypogammaglobulinemia and poor humoral response to antigens. Immunoblot analysis and immunofluorescence microscopy of transformed B cells from affected individuals show that the NFKB2 mutations affect phosphorylation and proteasomal processing of p100 and, ultimately, p52 nuclear translocation. These findings describe germline mutations in NFKB2 and establish the noncanonical NF-κB signaling pathway as a genetic etiology for this primary immunodeficiency syndrome.

Mobile element scanning (ME-Scan) identifies thousands of novel Alu insertions in diverse human populations.

Alu retrotransposons are the most numerous and active mobile elements in humans, causing genetic disease and creating genomic diversity. Mobile element scanning (ME-Scan) enables comprehensive and affordable identification of mobile element insertions (MEI) using targeted high-throughput sequencing of multiplexed MEI junction libraries. In a single experiment, ME-Scan identifies nearly all AluYb8 and AluYb9 elements, with high sensitivity for both rare and common insertions, in 169 individuals of diverse ancestry. ME-Scan detects heterozygous insertions in single individuals with 91% sensitivity. Insertion presence or absence states determined by ME-Scan are 95% concordant with those determined by locus-specific PCR assays. By sampling diverse populations from Africa, South Asia, and Europe, we are able to identify 5799 Alu insertions, including 2524 novel ones, some of which occur in exons. Sub-Saharan populations and a Pygmy group in particular carry numerous intermediate-frequency Alu insertions that are absent in non-African groups. There is a significant dearth of exon-interrupting insertions among common Alu polymorphisms, but the density of singleton Alu insertions is constant across exonic and nonexonic regions. In one case, a validated novel singleton Alu interrupts a protein-coding exon of FAM187B. This implies that exonic Alu insertions are generally deleterious and thus eliminated by natural selection, but not so quickly that they cannot be observed as extremely rare variants.

Genomic analysis of natural selection and phenotypic variation in high-altitude mongolians.

Deedu (DU) Mongolians, who migrated from the Mongolian steppes to the Qinghai-Tibetan Plateau approximately 500 years ago, are challenged by environmental conditions similar to native Tibetan highlanders. Identification of adaptive genetic factors in this population could provide insight into coordinated physiological responses to this environment. Here we examine genomic and phenotypic variation in this unique population and present the first complete analysis of a Mongolian whole-genome sequence. High-density SNP array data demonstrate that DU Mongolians share genetic ancestry with other Mongolian as well as Tibetan populations, specifically in genomic regions related with adaptation to high altitude. Several selection candidate genes identified in DU Mongolians are shared with other Asian groups (e.g., EDAR), neighboring Tibetan populations (including high-altitude candidates EPAS1, PKLR, and CYP2E1), as well as genes previously hypothesized to be associated with metabolic adaptation (e.g., PPARG). Hemoglobin concentration, a trait associated with high-altitude adaptation in Tibetans, is at an intermediate level in DU Mongolians compared to Tibetans and Han Chinese at comparable altitude. Whole-genome sequence from a DU Mongolian (Tianjiao1) shows that about 2% of the genomic variants, including more than 300 protein-coding changes, are specific to this individual. Our analyses of DU Mongolians and the first Mongolian genome provide valuable insight into genetic adaptation to extreme environments.

The heritability of gestational age in a two-million member cohort: implications for spontaneous preterm birth.

Preterm birth (PTB), defined as birth prior to a gestational age (GA) of 37 completed weeks, affects more than 10% of births worldwide. PTB is the leading cause of neonatal mortality and is associated with a broad spectrum of lifelong morbidity in survivors. The etiology of spontaneous PTB (SPTB) is complex and has an important genetic component. Previous studies have compared monozygotic and dizygotic twin mothers and their families to estimate the heritability of SPTB, but these approaches cannot separate the relative contributions of the maternal and the fetal genomes to GA or SPTB. Using the Utah Population Database, we assessed the heritability of GA in more than 2 million post-1945 Utah births, the largest familial GA dataset ever assembled. We estimated a narrow-sense heritability of 13.3% for GA and a broad-sense heritability of 24.5%. A maternal effect (which includes the effect of the maternal genome) accounts for 15.2% of the variance of GA, and the remaining 60.3% is contributed by individual environmental effects. Given the relatively low heritability of GA and SPTB in the general population, multiplex SPTB pedigrees are likely to provide more power for gene detection than will samples of unrelated individuals. Furthermore, nongenetic factors provide important targets for therapeutic intervention.

Mutations in embryonic myosin heavy chain (MYH3) cause Freeman-Sheldon syndrome and Sheldon-Hall syndrome.

The genetic basis of most conditions characterized by congenital contractures is largely unknown. Here we show that mutations in the embryonic myosin heavy chain (MYH3) gene cause Freeman-Sheldon syndrome (FSS), one of the most severe multiple congenital contracture (that is, arthrogryposis) syndromes, and nearly one-third of all cases of Sheldon-Hall syndrome (SHS), the most common distal arthrogryposis. FSS and SHS mutations affect different myosin residues, demonstrating that MYH3 genotype is predictive of phenotype. A structure-function analysis shows that nearly all of the MYH3 mutations are predicted to interfere with myosin's catalytic activity. These results add to the growing body of evidence showing that congenital contractures are a shared outcome of prenatal defects in myofiber force production. Elucidation of the genetic basis of these syndromes redefines congenital contractures as unique defects of the sarcomere and provides insights about what has heretofore been a poorly understood group of disorders.

Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency.

We have identified two families with a previously undescribed lethal X-linked disorder of infancy; the disorder comprises a distinct combination of an aged appearance, craniofacial anomalies, hypotonia, global developmental delays, cryptorchidism, and cardiac arrhythmias. Using X chromosome exon sequencing and a recently developed probabilistic algorithm aimed at discovering disease-causing variants, we identified in one family a c.109T>C (p.Ser37Pro) variant in NAA10, a gene encoding the catalytic subunit of the major human N-terminal acetyltransferase (NAT). A parallel effort on a second unrelated family converged on the same variant. The absence of this variant in controls, the amino acid conservation of this region of the protein, the predicted disruptive change, and the co-occurrence in two unrelated families with the same rare disorder suggest that this is the pathogenic mutation. We confirmed this by demonstrating a significantly impaired biochemical activity of the mutant hNaa10p, and from this we conclude that a reduction in acetylation by hNaa10p causes this disease. Here we provide evidence of a human genetic disorder resulting from direct impairment of N-terminal acetylation, one of the most common protein modifications in humans.

A probabilistic disease-gene finder for personal genomes.

VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases.