Structural variation (copy number variation [CNV] including deletion and duplication, translocation, inversion) of chromosomes has been identified in some individuals with autism spectrum disorder (ASD), but the full etiologic role is unknown. We performed genome-wide assessment for structural abnormalities in 427 unrelated ASD cases via single-nucleotide polymorphism microarrays and karyotyping. With microarrays, we discovered 277 unbalanced CNVs in 44% of ASD families not present in 500 controls (and re-examined in another 1152 controls). Karyotyping detected additional balanced changes. Although most variants were inherited, we found a total of 27 cases with de novo alterations, and in three (11%) of these individuals, two or more new variants were observed. De novo CNVs were found in approximately 7% and approximately 2% of idiopathic families having one child, or two or more ASD siblings, respectively. We also detected 13 loci with recurrent/overlapping CNV in unrelated cases, and at these sites, deletions and duplications affecting the same gene(s) in different individuals and sometimes in asymptomatic carriers were also found. Notwithstanding complexities, our results further implicate the SHANK3-NLGN4-NRXN1 postsynaptic density genes and also identify novel loci at DPP6-DPP10-PCDH9 (synapse complex), ANKRD11, DPYD, PTCHD1, 15q24, among others, for a role in ASD susceptibility. Our most compelling result discovered CNV at 16p11.2 (p = 0.002) (with characteristics of a genomic disorder) at approximately 1% frequency. Some of the ASD regions were also common to mental retardation loci. Structural variants were found in sufficiently high frequency influencing ASD to suggest that cytogenetic and microarray analyses be considered in routine clinical workup.

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

Accurate interpretation of DNA sequence variation is a prerequisite for implementing personalized medicine. Discrepancies in interpretation between testing laboratories impede the effective use of genetic test results in clinical medicine. To better understand the underpinnings of these discrepancies, we quantified differences in variant classification internally over time and those between our diagnostic laboratory and other laboratories and resources. We assessed the factors that contribute to these discrepancies and those that facilitate their resolution. Our process resolved 72% of nearly 300 discrepancies between pairs of laboratories to within a one-step classification difference and identified key sources of data that facilitate changes in variant interpretation. The identification and harmonization of variant discrepancies will maximize the clinical use of genetic information; these processes will be fostered by the accumulation of additional population data as well as the sharing of data between diagnostic laboratories.

Copy-number variants (CNVs) have been the predominant focus of genetic studies of structural variation, and chromosomal microarray (CMA) for genome-wide CNV detection is the recommended first-tier genetic diagnostic screen in neurodevelopmental disorders. We compared CNVs observed by CMA to the structural variation detected by whole-genome large-insert sequencing in 259 individuals diagnosed with autism spectrum disorder (ASD) from the Simons Simplex Collection. These analyses revealed a diverse landscape of complex duplications in the human genome. One remarkably common class of complex rearrangement, which we term dupINVdup, involves two closely located duplications ("paired duplications") that flank the breakpoints of an inversion. This complex variant class is cryptic to CMA, but we observed it in 8.1% of all subjects. We also detected other paired-duplication signatures and duplication-mediated complex rearrangements in 15.8% of all ASD subjects. Breakpoint analysis showed that the predominant mechanism of formation of these complex duplication-associated variants was microhomology-mediated repair. On the basis of the striking prevalence of dupINVdups in this cohort, we explored the landscape of all inversion variation among the 235 highest-quality libraries and found abundant complexity among these variants: only 39.3% of inversions were canonical, or simple, inversions without additional rearrangement. Collectively, these findings indicate that dupINVdups, as well as other complex duplication-associated rearrangements, represent relatively common sources of genomic variation that is cryptic to population-based microarray and low-depth whole-genome sequencing. They also suggest that paired-duplication signatures detected by CMA warrant further scrutiny in genetic diagnostic testing given that they might mark complex rearrangements of potential clinical relevance.

Reduced FCGR3B copy number is associated with increased risk of systemic lupus erythematosus (SLE). The five FCGR2/FCGR3 genes are arranged across two highly paralogous genomic segments on chromosome 1q23. Previous studies have suggested mechanisms for structural rearrangements at the FCGR2/FCGR3 locus and have proposed mechanisms whereby altered FCGR3B copy number predisposes to autoimmunity, but the high degree of sequence similarity between paralogous segments has prevented precise definition of the molecular events and their functional consequences. To pursue the genomic pathology associated with FCGR3B copy-number variation, we integrated sequencing data from fosmid and bacterial artificial chromosome clones and sequence-captured DNA from FCGR3B-deleted genomes to establish a detailed map of allelic and paralogous sequence variation across the FCGR2/FCGR3 locus. This analysis identified two highly paralogous 24.5 kb blocks within the FCGR2C/FCGR3B/FCGR2B locus that are devoid of nonpolymorphic paralogous sequence variations and that define the limits of the genomic regions in which nonallelic homologous recombination leads to FCGR2C/FCGR3B copy-number variation. Further, the data showed evidence of swapping of haplotype blocks between these highly paralogous blocks that most likely arose from sequential ancestral recombination events across the region. Functionally, we found by flow cytometry, immunoblotting and cDNA sequencing that individuals with FCGR3B-deleted alleles show ectopic presence of FcγRIIb on natural killer (NK) cells. We conclude that FCGR3B deletion juxtaposes the 5'-regulatory sequences of FCGR2C with the coding sequence of FCGR2B, creating a chimeric gene that results in an ectopic accumulation of FcγRIIb on NK cells and provides an explanation for SLE risk associated with reduced FCGR3B gene copy number.

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and medical genetic initiatives are reliant upon short-read whole-genome sequencing (srWGS), which presents challenges for the detection of structural variants (SVs) relative to emerging long-read WGS (lrWGS) technologies. Given this ubiquity of srWGS in large-scale genomics initiatives, we sought to establish expectations for routine SV detection from this data type by comparison with lrWGS assembly, as well as to quantify the genomic properties and added value of SVs uniquely accessible to each technology. Analyses from the Human Genome Structural Variation Consortium (HGSVC) of three families captured ~11,000 SVs per genome from srWGS and ~25,000 SVs per genome from lrWGS assembly. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplication (SD) and simple repeat (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of reference sequence, we observed extremely high (93.8%) concordance between technologies for deletions in these datasets. In contrast, lrWGS was superior for detection of insertions across all genomic contexts. Given that non-SD/SR sequences encompass 95.9% of currently annotated disease-associated exons, improved sensitivity from lrWGS to discover novel pathogenic deletions in these currently interpretable genomic regions is likely to be incremental. However, these analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.

Height is a model polygenic trait that is highly heritable. Genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with stature, but the role of structural variation in determining height is largely unknown. We performed a genome-wide association study of copy-number variation and stature in a clinical cohort of children who had undergone comparative genomic hybridization (CGH) microarray analysis for clinical indications. We found that subjects with short stature had a greater global burden of copy-number variants (CNVs) and a greater average CNV length than did controls (p < 0.002). These associations were present for lower-frequency (<5%) and rare (<1%) deletions, but there were no significant associations seen for duplications. Known gene-deletion syndromes did not account for our findings, and we saw no significant associations with tall stature. We then extended our findings into a population-based cohort and found that, in agreement with the clinical cohort study, an increased burden of lower-frequency deletions was associated with shorter stature (p = 0.015). Our results suggest that in individuals undergoing copy-number analysis for clinical indications, short stature increases the odds that a low-frequency deletion will be found. Additionally, copy-number variation might contribute to genetic variation in stature in the general population.

Despite widespread clinical genetic testing, many individuals with suspected genetic conditions lack a precise diagnosis, limiting their opportunity to take advantage of state-of-the-art treatments. In some cases, testing reveals difficult-to-evaluate structural differences, candidate variants that do not fully explain the phenotype, single pathogenic variants in recessive disorders, or no variants in genes of interest. Thus, there is a need for better tools to identify a precise genetic diagnosis in individuals when conventional testing approaches have been exhausted. We performed targeted long-read sequencing (T-LRS) using adaptive sampling on the Oxford Nanopore platform on 40 individuals, 10 of whom lacked a complete molecular diagnosis. We computationally targeted up to 151 Mbp of sequence per individual and searched for pathogenic substitutions, structural variants, and methylation differences using a single data source. We detected all genomic aberrations-including single-nucleotide variants, copy number changes, repeat expansions, and methylation differences-identified by prior clinical testing. In 8/8 individuals with complex structural rearrangements, T-LRS enabled more precise resolution of the mutation, leading to changes in clinical management in one case. In ten individuals with suspected Mendelian conditions lacking a precise genetic diagnosis, T-LRS identified pathogenic or likely pathogenic variants in six and variants of uncertain significance in two others. T-LRS accurately identifies pathogenic structural variants, resolves complex rearrangements, and identifies Mendelian variants not detected by other technologies. T-LRS represents an efficient and cost-effective strategy to evaluate high-priority genes and regions or complex clinical testing results.

Despite the discovery of copy-number variation (CNV) across the genome nearly 10 years ago, current SNP-based analysis methodologies continue to collapse the homozygous (i.e., A/A), hemizygous (i.e., A/0), and duplicative (i.e., A/A/A) genotype states, treating the genotype variable as irreducible or unaltered by other colocalizing forms of genetic (e.g., structural) variation. Our understanding of common, genome-wide CNVs suggests that the canonical genotype construct might belie the enormous complexity of the genome. Here we present multiple analyses of several phenotypes and provide methods supporting a conceptual shift that embraces the structural dimension of genotype. We comprehensively investigate the impact of the structural dimension of genotype on (1) GWAS methods, (2) interpretation of rare LOF variants, (3) characterization of genomic architecture, and (4) implications for mapping loci involved in complex disease. Taken together, these results argue for the inclusion of a structural dimension and suggest that some portion of the "missing" heritability might be recovered through integration of the structural dimension of SNP effects on complex traits.

Whole-genome and exome data sets continue to be produced at a frenetic pace, resulting in massively large catalogs of human genomic variation. However, a clear picture of the characteristics and patterns of neutral and deleterious variation within and between populations has yet to emerge, given that recent large-scale sequencing studies have often emphasized different aspects of the data and sometimes appear to have conflicting conclusions. Here, we comprehensively studied characteristics of protein-coding variation in high-coverage exome sequence data from 6,515 European American (EA) and African American (AA) individuals. We developed an unbiased approach to identify putatively deleterious variants and investigated patterns of neutral and deleterious single-nucleotide variants and alleles between individuals and populations. We show that there are substantial differences in the composition of genotypes between EA and AA populations and that small but statistically significant differences exist in the average number of deleterious alleles carried by EA and AA individuals. Furthermore, we performed extensive simulations to delineate the temporal dynamics of deleterious alleles for a broad range of demographic models and use these data to inform the interpretation of empirical patterns of deleterious variation. Finally, we illustrate that the effects of demographic perturbations, such as bottlenecks and expansions, often manifest in opposing patterns of neutral and deleterious variation depending on whether the focus is on populations or individuals. Our results clarify seemingly disparate empirical characteristics of protein-coding variation and provide substantial insights into how natural selection and demographic history have patterned neutral and deleterious variation within and between populations.

Identifying genomic variation is a crucial step for unraveling the relationship between genotype and phenotype and can yield important insights into human diseases. Prevailing methods rely on cost-intensive whole-genome sequencing (WGS) or whole-exome sequencing (WES) approaches while the identification of genomic variants from often existing RNA sequencing (RNA-seq) data remains a challenge because of the intrinsic complexity in the transcriptome. Here, we present a highly accurate approach termed SNPiR to identify SNPs in RNA-seq data. We applied SNPiR to RNA-seq data of samples for which WGS and WES data are also available and achieved high specificity and sensitivity. Of the SNPs called from the RNA-seq data, >98% were also identified by WGS or WES. Over 70% of all expressed coding variants were identified from RNA-seq, and comparable numbers of exonic variants were identified in RNA-seq and WES. Despite our method's limitation in detecting variants in expressed regions only, our results demonstrate that SNPiR outperforms current state-of-the-art approaches for variant detection from RNA-seq data and offers a cost-effective and reliable alternative for SNP discovery.

Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene.

We conducted a genome-wide association study (GWAS) and a follow-up study of bipolar disorder (BD), a common neuropsychiatric disorder. In the GWAS, we investigated 499,494 autosomal and 12,484 X-chromosomal SNPs in 682 patients with BD and in 1300 controls. In the first follow-up step, we tested the most significant 48 SNPs in 1729 patients with BD and in 2313 controls. Eight SNPs showed nominally significant association with BD and were introduced to a meta-analysis of the GWAS and the first follow-up samples. Genetic variation in the neurocan gene (NCAN) showed genome-wide significant association with BD in 2411 patients and 3613 controls (rs1064395, p = 3.02 × 10(-8); odds ratio = 1.31). In a second follow-up step, we replicated this finding in independent samples of BD, totaling 6030 patients and 31,749 controls (p = 2.74 × 10(-4); odds ratio = 1.12). The combined analysis of all study samples yielded a p value of 2.14 × 10(-9) (odds ratio = 1.17). Our results provide evidence that rs1064395 is a common risk factor for BD. NCAN encodes neurocan, an extracellular matrix glycoprotein, which is thought to be involved in cell adhesion and migration. We found that expression in mice is localized within cortical and hippocampal areas. These areas are involved in cognition and emotion regulation and have previously been implicated in BD by neuropsychological, neuroimaging, and postmortem studies.

Chimeric genes can be caused by structural genomic rearrangements that fuse together portions of two different genes to create a novel gene. We hypothesize that brain-expressed chimeras may contribute to schizophrenia. Individuals with schizophrenia and control individuals were screened genome wide for copy-number variants (CNVs) that disrupted two genes on the same DNA strand. Candidate events were filtered for predicted brain expression and for frequency < 0.001 in an independent series of 20,000 controls. Four of 124 affected individuals and zero of 290 control individuals harbored such events (p = 0.002); a 47 kb duplication disrupted MATK and ZFR2, a 58 kb duplication disrupted PLEKHD1 and SLC39A9, a 121 kb duplication disrupted DNAJA2 and NETO2, and a 150 kb deletion disrupted MAP3K3 and DDX42. Each fusion produced a stable protein when exogenously expressed in cultured cells. We examined whether these chimeras differed from their parent genes in localization, regulation, or function. Subcellular localizations of DNAJA2-NETO2 and MAP3K3-DDX42 differed from their parent genes. On the basis of the expression profile of the MATK promoter, MATK-ZFR2 is likely to be far more highly expressed in the brain during development than the ZFR2 parent gene. MATK-ZFR2 includes a ZFR2-derived isoform that we demonstrate localizes preferentially to neuronal dendritic branch sites. These results suggest that the formation of chimeric genes is a mechanism by which CNVs contribute to schizophrenia and that, by interfering with parent gene function, chimeras may disrupt critical brain processes, including neurogenesis, neuronal differentiation, and dendritic arborization.

Rare variants affecting phenotype pose a unique challenge for human genetics. Although genome-wide association studies have successfully detected many common causal variants, they are underpowered in identifying disease variants that are too rare or population-specific to be imputed from a general reference panel and thus are poorly represented on commercial SNP arrays. We set out to overcome these challenges and detect association between disease and rare alleles using SNP arrays by relying on long stretches of genomic sharing that are identical by descent. We have developed an algorithm, DASH, which builds upon pairwise identical-by-descent shared segments to infer clusters of individuals likely to be sharing a single haplotype. DASH constructs a graph with nodes representing individuals and links on the basis of such segments spanning a locus and uses an iterative minimum cut algorithm to identify densely connected components. We have applied DASH to simulated data and diverse GWAS data sets by constructing haplotype clusters and testing them for association. In simulations we show this approach to be significantly more powerful than single-marker testing in an isolated population that is from Kosrae, Federated States of Micronesia and has abundant IBD, and we provide orthogonal information for rare, recent variants in the outbred Wellcome Trust Case-Control Consortium (WTCCC) data. In both cohorts, we identified a number of haplotype associations, five such loci in the WTCCC data and ten in the isolated, that were conditionally significant beyond any individual nearby markers. We have replicated one of these loci in an independent European cohort and identified putative structural changes in low-pass whole-genome sequence of the cluster carriers.

The well-established manifestation of mitochondrial mutations in functional cardiac disease (e.g., mitochondrial cardiomyopathy) prompted the hypothesis that mitochondrial DNA (mtDNA) sequence and/or copy number (mtDNAcn) variation contribute to cardiac defects in congenital heart disease (CHD). MtDNAcns were calculated and rare, non-synonymous mtDNA mutations were identified in 1,837 CHD-affected proband-parent trios, 116 CHD-affected singletons, and 114 paired cardiovascular tissue/blood samples. The variant allele fraction (VAF) of heteroplasmic variants in mitochondrial RNA from 257 CHD cardiovascular tissue samples was also calculated. On average, mtDNA from blood had 0.14 rare variants and 52.9 mtDNA copies per nuclear genome per proband. No variation with parental age at proband birth or CHD-affected proband age was seen. mtDNAcns in valve/vessel tissue (320 ± 70) were lower than in atrial tissue (1,080 ± 320, p = 6.8E-21), which were lower than in ventricle tissue (1,340 ± 280, p = 1.4E-4). The frequency of rare variants in CHD-affected individual DNA was indistinguishable from the frequency in an unaffected cohort, and proband mtDNAcns did not vary from those of CHD cohort parents. In both the CHD and the comparison cohorts, mtDNAcns were significantly correlated between mother-child, father-child, and mother-father. mtDNAcns among people with European (mean = 52.0), African (53.0), and Asian haplogroups (53.5) were calculated and were significantly different for European and Asian haplogroups (p = 2.6E-3). Variant heteroplasmic fraction (HF) in blood correlated well with paired cardiovascular tissue HF (r = 0.975) and RNA VAF (r = 0.953), which suggests blood HF is a reasonable proxy for HF in heart tissue. We conclude that mtDNA mutations and mtDNAcns are unlikely to contribute significantly to CHD risk.

Papillary thyroid carcinoma (PTC) has a wide geographic variation in incidence; it is most common in Saudi Arabia, where it is only second to breast cancer as the most common cancer among females. Genomic profiling of PTC from Saudi Arabia has not been attempted previously. We performed whole-exome sequencing of 101 PTC samples and the corresponding genomic DNA to identify genes with recurrent somatic mutations, then sequenced these genes by using a next-generation gene-panel approach in an additional 785 samples. In addition to BRAF, N-RAS, and H-RAS, which have previously been shown to be recurrently mutated in PTC, our analysis highlights additional genes, including thyroglobulin (TG), which harbored somatic mutations in 3% of the entire cohort. Surprisingly, although TG mutations were not exclusive to mutations in the RAS-MAP kinase pathway, their presence was associated with a significantly worse clinical outcome, which suggests a pathogenic role beyond driving initial oncogenesis. Analysis of metastatic PTC tissue revealed significant enrichment for TG mutations (p < 0.001), including events of apparent clonal expansion. Our results suggest a previously unknown role of TG somatic mutations in the pathogenesis of PTC and its malignant evolution.

New human mutations are thought to originate in germ cells, thus making a recurrence of the same mutation in a sibling exceedingly rare. However, increasing sensitivity of genomic technologies has anecdotally revealed mosaicism for mutations in somatic tissues of apparently healthy parents. Such somatically mosaic parents might also have germline mosaicism that can potentially cause unexpected intergenerational recurrences. Here, we show that somatic mosaicism for transmitted mutations among parents of children with simplex genetic disease is more common than currently appreciated. Using the sensitivity of individual-specific breakpoint PCR, we prospectively screened 100 families with children affected by genomic disorders due to rare deletion copy-number variants (CNVs) determined to be de novo by clinical analysis of parental DNA. Surprisingly, we identified four cases of low-level somatic mosaicism for the transmitted CNV in DNA isolated from parental blood. Integrated probabilistic modeling of gametogenesis developed in response to our observations predicts that mutations in parental blood increase recurrence risk substantially more than parental mutations confined to the germline. Moreover, despite the fact that maternally transmitted mutations are the minority of alleles, our model suggests that sexual dimorphisms in gametogenesis result in a greater proportion of somatically mosaic transmitting mothers who are thus at increased risk of recurrence. Therefore, somatic mosaicism together with sexual differences in gametogenesis might explain a considerable fraction of unexpected recurrences of X-linked recessive disease. Overall, our results underscore an important role for somatic mosaicism and mitotic replicative mutational mechanisms in transmission genetics.

With recent rapid advances in genomic technologies, precise delineation of structural chromosome rearrangements at the nucleotide level is becoming increasingly feasible. In this era of "next-generation cytogenetics" (i.e., an integration of traditional cytogenetic techniques and next-generation sequencing), a consensus nomenclature is essential for accurate communication and data sharing. Currently, nomenclature for describing the sequencing data of these aberrations is lacking. Herein, we present a system called Next-Gen Cytogenetic Nomenclature, which is concordant with the International System for Human Cytogenetic Nomenclature (2013). This system starts with the alignment of rearrangement sequences by BLAT or BLAST (alignment tools) and arrives at a concise and detailed description of chromosomal changes. To facilitate usage and implementation of this nomenclature, we are developing a program designated BLA(S)T Output Sequence Tool of Nomenclature (BOSToN), a demonstrative version of which is accessible online. A standardized characterization of structural chromosomal rearrangements is essential both for research analyses and for application in the clinical setting.

Mosaicism is defined as the coexistence of cells with different genetic composition within an individual, caused by postzygotic somatic mutation. Although somatic mosaicism for chromosomal abnormalities is a well-established cause of developmental and somatic disorders and has also been detected in different tissues, its frequency and extent in the adult normal population are still unknown. We provide here a genome-wide survey of mosaic genomic variation obtained by analyzing Illumina 1M SNP array data from blood or buccal DNA samples of 1991 adult individuals from the Spanish Bladder Cancer/EPICURO genome-wide association study. We found mosaic abnormalities in autosomes in 1.7% of samples, including 23 segmental uniparental disomies, 8 complete trisomies, and 11 large (1.5-37 Mb) copy-number variants. Alterations were observed across the different autosomes with recurrent events in chromosomes 9 and 20. No case-control differences were found in the frequency of events or the percentage of cells affected, thus indicating that most rearrangements found are not central to the development of bladder cancer. However, five out of six events tested were detected in both blood and bladder tissue from the same individual, indicating an early developmental origin. The high cellular frequency of the anomalies detected and their presence in normal adult individuals suggest that this type of mosaicism is a widespread phenomenon in the human genome. Somatic mosaicism should be considered in the expanding repertoire of inter- and intraindividual genetic variation, some of which may cause somatic human diseases but also contribute to modifying inherited disorders and/or late-onset multifactorial traits.