Search

Many regions in the human genome vary in length among individuals due to variable numbers of tandem repeats (VNTRs). We recently showed that protein-coding VNTRs underlie some of the strongest known genetic associations with diverse phenotypes. Here, we assessed the phenotypic impact of VNTRs genome-wide, 99% of which lie in non-coding regions. We applied a statistical imputation approach to estimate the lengths of 9,561 autosomal VNTR loci in 418,136 unrelated UK Biobank participants. Association and statistical fine-mapping analyses identified 107 VNTR-phenotype associations (involving 58 VNTRs) that were assigned a high probability of VNTR causality (PIP≥0.5). Non-coding VNTRs atTMCO1andEIF3Happeared to generate the largest known contributions of common human genetic variation to risk of glaucoma and colorectal cancer, respectively. Each of these two VNTRs associated with a >2- fold risk range across individuals. These results reveal a substantial and previously unappreciated role of non-coding VNTRs in human health.

Non-invasive prenatal testing (NIPT) to detect fetal aneuploidy by sequencing the cell-free DNA (cfDNA) in maternal plasma is being broadly adopted. To detect fetal aneuploidies from maternal plasma, where fetal DNA is mixed with far-larger amounts of maternal DNA, NIPT requires a minimum fraction of the circulating cfDNA to be of placental origin, a level which is usually attained beginning at 10 weeks gestational age. We present an approach that leverages the arrangement of alleles along homologous chromosomes—also known as chromosomal phase—to make NIPT analyses more conclusive. We validate our approach with in silico simulations, then re-analyze data from a pregnant mother who, due to a fetal DNA fraction of 3.4%, received an inconclusive aneuploidy determination through NIPT. We find that the presence of a trisomy 18 fetus can be conclusively inferred from the patient’s same molecular data when chromosomal phase is incorporated into the analysis. Key to the effectiveness of our approach is the ability of homologous chromosomes to act as natural controls for each other and the ability of chromosomal phase to integrate subtle quantitative signals across very many sequence variants. These results show that chromosomal phase increases the sensitivity of a common laboratory test, an idea that could also advance cfDNA analyses for cancer detection.

Despite their widespread use in research, there has not yet been a systematic genomic analysis of human embryonic stem cell (hESC) lines at a single-nucleotide resolution. We therefore performed whole-genome sequencing (WGS) of 143 hESC lines and annotated their single-nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer-scale structural and single-nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESC genomes and human blood-derived genomes at similar frequencies. Moreover, WGS allowed us to identify SNVs associated with cancer and other diseases that could alter cellular phenotypes and compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection.

There has not yet been a systematic analysis of hESC whole genomes at a single nucleotide resolution. We therefore performed whole genome sequencing (WGS) of 143 hESC lines and annotated their single nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer scale structural and single nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESCs genomes and human blood-derived genomes at similar frequencies. However, WGS did identify SNVs associated with cancer or other diseases that will likely alter cellular phenotypes and may compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection.GRAPHICAL ABSTRACTIN BRIEFMerkle and Ghosh et al. describe insights from the whole genome sequences of commonly used human embryonic stem cell (hESC) lines. Analyses of these sequences show that while hESC genomes had more large structural variants than humans do from genetic inheritance, hESCs did not have an observable excess of finer-scale variants. However, many hESC lines contained rare loss-of-function variants and combinations of common variants that may profoundly shape their biological phenotypes. Thus, genome sequencing data can be valuable to those selecting cell lines for a given biological or clinical application, and the sequences and analysis reported here should facilitate such choices.HIGHLIGHTSOne third of hESCs we analysed are siblings, and almost all are of European ancestryLarge structural variants are common in hESCs, but finer-scale variation is similar to that human populationsMany strong-effect loss-of-function mutations and cancer-associated mutations are present in specific hESC linesWe provide user-friendly resources for rational hESC line selection based on genome sequence

Two intriguing forms of genome structural variation (SV) – dispersed duplications, and de novo rearrangements of complex, multi-allelic loci – have long escaped genomic analysis. We describe a new way to find and characterize such variation by utilizing identity-by-descent (IBD) relationships between siblings together with high-precision measurements of segmental copy number. Analyzing whole-genome sequence data from 706 families, we find hundreds of “IBD-discordant” (IBDD) CNVs: loci at which siblings’ CNV measurements and IBD states are mathematically inconsistent. We found that commonly-IBDD CNVs identify dispersed duplications; we mapped 95 of these common dispersed duplications to their true genomic locations through family-based linkage and population linkage disequilibrium (LD), and found several to be in strong LD with genome-wide association (GWAS) signals for common diseases or gene expression variation at their revealed genomic locations. Other CNVs that were IBDD in a single family appear to involve de novo mutations in complex and multi-allelic loci; we identified 26 de novo structural mutations that had not been previously detected in earlier analyses of the same families by diverse SV analysis methods. These included a de novo mutation of the amylase gene locus and multiple de novo mutations at chromosome 15q14. Combining these complex mutations with more-conventional CNVs, we estimate that segmental mutations larger than 1kb arise in about one per 22 human meioses. These methods are complementary to previous techniques in that they interrogate genomic regions that are home to segmental duplication, high CNV allele frequencies, and multi-allelic CNVs.Author SummaryCopy number variation is an important form of genetic variation in which individuals differ in the number of copies of segments of their genomes. Certain aspects of copy number variation have traditionally been difficult to study using short-read sequencing data. For example, standard analyses often cannot tell whether the duplicated copies of a segment are located near the original copy or are dispersed to other regions of the genome. Another aspect of copy number variation that has been difficult to study is the detection of mutations in the copy number of DNA segments passed down from parents to their children, particularly when the mutations affect genome segments which already display common copy number variation in the population. We develop an analytical approach to solving these problems when sequencing data is available for all members of families with at least two children. This method is based on determining the number of parental haplotypes the two siblings share at each location in their genome, and using that information to determine the possible inheritance patterns that might explain the copy numbers we observe in each family member. We show that dispersed duplications and mutations can be identified by looking for copy number variants that do not follow these expected inheritance patterns. We use this approach to determine the location of 95 common duplications which are dispersed to distant regions of the genome, and demonstrate that these duplications are linked to genetic variants that affect disease risk or gene expression levels. We also identify a set of copy number mutations not detected by previous analyses of sequencing data from a large cohort of families, and show that repetitive and complex regions of the genome undergo frequent mutations in copy number.

SummaryTens of thousands of genetic variants shape human phenotypes, mostly by unknown cellular mechanisms. Here we describe Census-seq, a way to measure cellular phenotypes in cells from many people simultaneously. Analogous to pooled CRISPR screens but for natural variation, Census-seq associates cellular phenotypes to donors’ genotypes by quantifying the presence of each donor’s DNA in cell “villages” before and after sorting or selection for cellular traits of interest. Census-seq enables population-scale cell-biological phenotyping with low cost and high internal control. We demonstrate Census-seq through investigation of genetic effects on the SMN protein whose deficiency underlies spinal muscular atrophy (SMA). Census-seq quantified and mapped effects of many common alleles on SMN protein levels and response to SMN-targeted therapeutics, including a common, cryptic non-responder allele. We provide tools enabling population-scale cell experiments and explain how Census-seq can be used to map genetic effects on diverse cell phenotypes.Abstract FigureHighlightsCensus-seq reveals how inherited genetic variation affects cell phenotypesGenetic analysis of cellular traits in cell villages of >100 donorsCharacterizing human alleles that shape SMN protein expression and drug responsesDevelopment of protocols and software to enable cellular population genetics

DNA replication follows a strict spatiotemporal program that intersects with chromatin structure and gene regulation. However, the genetic basis of the mammalian DNA replication timing program is poorly understood1–3. To systematically identify genetic regulators of DNA replication timing, we exploited inter-individual variation in 457 human pluripotent stem cell lines from 349 individuals. We show that the human genome’s replication program is broadly encoded in DNA and identify 1,617 cis-acting replication timing quantitative trait loci (rtQTLs4) – base-pair-resolution sequence determinants of replication initiation. rtQTLs function individually, or in combinations of proximal and distal regulators, to affect replication timing. Analysis of rtQTL locations reveals a histone code for replication initiation, composed of bivalent histone H3 trimethylation marks on a background of histone hyperacetylation. The H3 trimethylation marks are individually repressive yet synergize to promote early replication. We further identify novel positive and negative regulators of DNA replication timing, the former comprised of pluripotency-related transcription factors while the latter involve boundary elements. Human replication timing is controlled by a multi-layered mechanism that operates on target DNA sequences, is composed of dozens of effectors working combinatorially, and follows principles analogous to transcription regulation: a histone code, activators and repressors, and a promoter-enhancer logic.

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.

Human pluripotent stem cells (hPS cells) can self-renew indefinitely, making them an attractive source for regenerative therapies. This expansion potential has been linked with the acquisition of large copy number variants that provide mutated cells with a growth advantage in culture. The nature, extent and functional effects of other acquired genome sequence mutations in cultured hPS cells are not known. Here we sequence the protein-coding genes (exomes) of 140 independent human embryonic stem cell (hES cell) lines, including 26 lines prepared for potential clinical use. We then apply computational strategies for identifying mutations present in a subset of cells in each hES cell line. Although such mosaic mutations were generally rare, we identified five unrelated hES cell lines that carried six mutations in the TP53 gene that encodes the tumour suppressor P53. The TP53 mutations we observed are dominant negative and are the mutations most commonly seen in human cancers. We found that the TP53 mutant allelic fraction increased with passage number under standard culture conditions, suggesting that the P53 mutations confer selective advantage. We then mined published RNA sequencing data from 117 hPS cell lines, and observed another nine TP53 mutations, all resulting in coding changes in the DNA-binding domain of P53. In three lines, the allelic fraction exceeded 50%, suggesting additional selective advantage resulting from the loss of heterozygosity at the TP53 locus. As the acquisition and expansion of cancer-associated mutations in hPS cells may go unnoticed during most applications, we suggest that careful genetic characterization of hPS cells and their differentiated derivatives be carried out before clinical use.

Many common illnesses differentially affect men and women for unknown reasons. The autoimmune diseases lupus and Sjögren’s syndrome affect nine times more women than men1,2, whereas schizophrenia affects men more frequently and severely3–5. All three illnesses have their strongest common-genetic associations in the Major Histocompatibility Complex (MHC) locus, an association that in lupus and Sjögren’s syndrome has long been thought to arise from HLA alleles6–13. Here we show that the complement component 4 (C4) genes in the MHC locus, recently found to increase risk for schizophrenia14, generate 7-fold variation in risk for lupus (95% CI: 5.88-8.61; p < 10−117 in total) and 16-fold variation in risk for Sjögren’s syndrome (95% CI: 8.59-30.89; p < 10−23 in total), with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia, greatly reduced risk for lupus and Sjögren’s syndrome. In all three illnesses, C4 alleles acted more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for lupus and 31-fold variation in risk for Sjögren’s syndrome in men (vs. 6-fold and 15-fold among women respectively) and affected schizophrenia risk about twice as strongly in men as in women. At a protein level, both C4 and its effector (C3) were present at greater levels in men than women in cerebrospinal fluid (p < 10−5 for both C4 and C3) and plasma among adults ages 20-5015–17, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help explain the larger effects of C4 alleles in men, women’s greater risk of SLE and Sjögren’s, and men’s greater vulnerability in schizophrenia. These results nominate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (, Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6–9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3–TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5–50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.

Neuromyelitis optica (NMO) is a rare autoimmune disease that affects the optic nerve and spinal cord. Most NMO patients ( > 70%) are seropositive for circulating autoantibodies against aquaporin 4 (NMO-IgG+). Here, we meta-analyze whole-genome sequences from 86 NMO cases and 460 controls with genome-wide SNP array from 129 NMO cases and 784 controls to test for association with SNPs and copy number variation (total N = 215 NMO cases, 1244 controls). We identify two independent signals in the major histocompatibility complex (MHC) region associated with NMO-IgG+, one of which may be explained by structural variation in the complement component 4 genes. Mendelian Randomization analysis reveals a significant causal effect of known systemic lupus erythematosus (SLE), but not multiple sclerosis (MS), risk variants in NMO-IgG+. Our results suggest that genetic variants in the MHC region contribute to the etiology of NMO-IgG+ and that NMO-IgG+ is genetically more similar to SLE than MS., Neuromyelitis optica (NMO) is a rare autoimmune condition characterized by inflammation and demyelination of the optic nerve and the spinal cord. Here, Estrada et al. identify NMO susceptibility variants in the MHC region and find that autoantibody-positive NMO genetically overlaps with lupus.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Hundreds of genes reside in structurally complex, poorly understood regions of the human genome. One such region contains the three amylase genes (AMY2B, AMY2A and AMY1) responsible for digesting starch into sugar. Copy number of AMY1 is reported to be the largest genomic influence on obesity, although genome-wide association studies for obesity have found this locus unremarkable. Using whole-genome sequence analysis, droplet digital PCR and genome mapping, we identified eight common structural haplotypes of the amylase locus that suggest its mutational history. We found that the AMY1 copy number in an individual's genome is generally even (rather than odd) and partially correlates with nearby SNPs, which do not associate with body mass index (BMI). We measured amylase gene copy number in 1,000 obese or lean Estonians and in 2 other cohorts totaling ∼3,500 individuals. We had 99% power to detect the lower bound of the reported effects on BMI, yet found no association.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle where apolipoprotein(a) (protein product of theLPAgene) is covalently attached to apolipoprotein B. Lp(a) is a highly heritable, causal risk factor for cardiovascular diseases and varies in concentrations across ancestries. To comprehensively delineate the inherited basis for plasma Lp(a), we performed deep-coverage whole genome sequencing in 8,392 individuals of European and African American ancestries. Through whole genome variant discovery and direct genotyping of all structural variants overlappingLPA, we quantified the 5.5kb kringle IV-2 copy number (KIV2-CN), a knownLPAstructural polymorphism, and developed a model for its imputation. Through common variant analysis, we discovered a novel locus (SORT1) associated with Lp(a)-cholesterol, and also genetic modifiers of KIV2-CN. Furthermore, in contrast to previous GWAS studies, we explain most of the heritability of Lp(a), observing Lp(a) to be 85% heritable among African Americans and 75% among Europeans, yet with notable inter-ethnic heterogeneity. Through analyses of aggregates of rare coding and non-coding variants with Lp(a)-cholesterol, we found the only genome-wide significant signal to be at a non-codingSLC22A3intronic window also previously described to be associated with Lp(a); however, this association was mitigated by adjustment with KIV2-CN. Finally, using an additional imputation dataset (N=27,344), we performed Mendelian randomization ofLPAvariant classes, finding that genetically regulated Lp(a) is more strongly associated with incident cardiovascular diseases than directly measured Lp(a), and is significantly associated with measures of subclinical atherosclerosis in African Americans.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

SummaryGenomic DNA replicates in a choreographed temporal order that impacts the distribution of mutations along the genome. We show here that DNA replication timing is shaped by genetic polymorphisms that act in cis upon megabase-scale DNA segments. In genome sequences from proliferating cells, read depth along chromosomes reflected DNA replication activity in those cells. We used this relationship to analyze variation in replication timing among 161 individuals sequenced by the 1000 Genomes Project. Genome-wide association of replication timing with genetic variation identified 16 loci at which inherited alleles associate with replication timing. We call these “replication timing quantitative trait loci” (rtQTLs). rtQTLs involved the differential use of replication origins, exhibited allele-specific effects on replication timing, and associated with gene expression variation at megabase scales. Our results show replication timing to be shaped by genetic polymorphism and identify a means by which inherited polymorphism regulates the mutability of nearby sequences.

Tens of millions of base pairs of euchromatic human genome sequence, including many protein-coding genes, have no known location in the human genome. We describe an approach for localizing the human genome's missing pieces by utilizing the patterns of genome sequence variation created by population admixture. We mapped the locations of 70 scaffolds spanning four million base pairs of the human genome's unplaced euchromatic sequence, including more than a dozen protein-coding genes, and identified eight large novel inter-chromosomal segmental duplications. We find that most of these sequences are hidden in the genome's heterochromatin, particularly its pericentromeric regions. Many cryptic, pericentromeric genes are expressed in RNA and have been maintained intact for millions of years while their expression patterns diverged from those of paralogous genes elsewhere in the genome. We describe how knowledge of the locations of these sequences can inform disease association and genome biology studies.

Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5–5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing., National Institutes of Health (U.S.) (Intramural Research Program), National Human Genome Research Institute (U.S.), Charles University (program UNCE 204011), Charles University (program PRVOUK-P24/LF1/3), Czech Republic. Ministry of Education, Youth, and Sports (grant NT13116-4/2012), Czech Republic. Ministry of Health (grant NT13116-4/2012), Czech Republic. Ministry of Health (grant LH12015), National Institutes of Health (U.S.) (Harvard Digestive Diseases Center, grant DK34854)

Ultra-rare inherited andde novodisruptive variants in highly constrained (HC) genes are enriched in neurodevelopmental disorders1–5. However, their impact on cognition in the general population has not been explored. We hypothesize that disruptive and damaging ultra-rare variants (URVs) in HC genes not only confer risk to neurodevelopmental disorders, but also influence general cognitive abilities measured indirectly by years of education (YOE). We tested this hypothesis in 14,133 individuals with whole exome or genome sequencing data. The presence of one or more URVs was associated with a decrease in YOE (3.1 months less for each additional mutation; P-value=3.3×10−8) and the effect was stronger in HC genes enriched for brain expression (6.5 months less, P-value=3.4×10−5). The effect of these variants was more pronounced than the estimated effects of runs of homozygosity and pathogenic copy number variation6–9. Our findings suggest that effects of URVs in HC genes are not confined to severe neurodevelopmental disorder, but influence the cognitive spectrum in the general population

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823 ; see the Perspective by Quintana-Murci ) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.

Co-inheritance of α-thalassemia has a significant protective effect on the severity of complications of sickle cell disease (SCD), including stroke. However, little information exists on the association and interactions for the common African ancestral α-thalassemia mutation (−α3.7 deletion) and β-globin traits (HbS trait [SCT] and HbC trait) on important clinical phenotypes such as red blood cell parameters, anemia, and chronic kidney disease (CKD). In a community-based cohort of 2,916 African Americans from the Jackson Heart Study, we confirmed the expected associations between SCT, HbC trait, and the −α3.7 deletion with lower mean corpuscular volume/mean corpuscular hemoglobin and higher red blood cell count and red cell distribution width. In addition to the recently recognized association of SCT with lower estimated glomerular filtration rate and glycated hemoglobin (HbA1c), we observed a novel association of the −α3.7 deletion with higher HbA1c levels. Co-inheritance of each additional copy of the −α3.7 deletion significantly lowered the risk of anemia and chronic kidney disease among individuals with SCT (P-interaction = 0.031 and 0.019, respectively). Furthermore, co-inheritance of a novel α-globin regulatory variant was associated with normalization of red cell parameters in individuals with the −α3.7 deletion and significantly negated the protective effect of α-thalassemia on stroke in 1,139 patients with sickle cell anemia from the Cooperative Study of Sickle Cell Disease (CSSCD) (P-interaction = 0.0049). Functional assays determined that rs11865131, located in the major alpha-globin enhancer MCS-R2, was the most likely causal variant. These findings suggest that common α- and β-globin variants interact to influence hematologic and clinical phenotypes in African Americans, with potential implications for risk-stratification and counseling of individuals with SCD and SCT., Author summary Recent work has shown that inheriting a single copy of the β-globin gene variant which causes sickle cell disease can be associated with medical risks, such as worsening kidney function. In individuals with sickle cell disease, co-inheritance of other globin gene variants, notably α-thalassemia, can modify an individual’s risk of clinical sequelae such as stroke. In this paper, our results suggest that inheritance of the same 3.7kb deletion that causes α-thalassemia in African populations can lower the risk of anemia and chronic kidney disease among African American community-dwelling individuals with sickle cell trait. Another α-globin genetic locus, located upstream within a well-known non-coding regulatory element, was found to modify associations of the α-thalassemia 3.7kb copy number variant with red blood cell traits in the African American general population and, in sickle cell disease patients, with risk of stroke. Using functional fine mapping and reporter assays, we localized rs11865131 and rs11248850 as the two most likely causal variants for these phenotypic associations. Additional molecular studies will be required to understand the complex regulatory mechanism by which these variants influence α-globin production.

In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

One of the first protein polymorphisms identified in humans involves the abundant blood protein haptoglobin. Two exons of the HP gene (encoding haptoglobin) exhibit copy number variation that affects HP protein structure and multimerization. The evolutionary origins and medical relevance of this polymorphism have been uncertain. Here we show that this variation has likely arisen from many recurring deletions, more specifically, reversions of an ancient hominin-specific duplication of these exons. Although this polymorphism has been largely invisible to genome-wide genetic studies thus far, we describe a way to analyze it by imputation from SNP haplotypes and find among 22,288 individuals that these HP exonic deletions associate with reduced LDL and total cholesterol levels. We further show that these deletions, and a SNP that affects HP expression, appear to drive the strong association of cholesterol levels with SNPs near HP. Recurring exonic deletions in HP likely enhance human health by lowering cholesterol levels in the blood.

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Background Cancers arise from multiple acquired mutations, which presumably occur over many years. Early stages in cancer development might be present years before cancers become clinically apparent. Methods We analyzed data from whole-exome sequencing of DNA in peripheral-blood cells from 12,380 persons, unselected for cancer or hematologic phenotypes. We identified somatic mutations on the basis of unusual allelic fractions. We used data from Swedish national patient registers to follow health outcomes for 2 to 7 years after DNA sampling. Results Clonal hematopoiesis with somatic mutations was observed in 10% of persons older than 65 years of age but in only 1% of those younger than 50 years of age. Detectable clonal expansions most frequently involved somatic mutations in three genes (DNMT3A, ASXL1, and TET2) that have previously been implicated in hematologic cancers. Clonal hematopoiesis was a strong risk factor for subsequent hematologic cancer (hazard ratio, 12.9; 95% confidence interval, 5.8 to 28.7). Approximately 42% of hematologic cancers in this cohort arose in persons who had clonality at the time of DNA sampling, more than 6 months before a first diagnosis of cancer. Analysis of bone marrow–biopsy specimens obtained from two patients at the time of diagnosis of acute myeloid leukemia revealed that their cancers arose from the earlier clones. Conclusions Clonal hematopoiesis with somatic mutations is readily detected by means of DNA sequencing, is increasingly common as people age, and is associated with increased risks of hematologic cancer and death. A subset of the genes that are mutated in patients with myeloid cancers is frequently mutated in apparently healthy persons; these mutations may represent characteristic early events in the development of hematologic cancers. (Funded by the National Human Genome Research Institute and others.), National Human Genome Research Institute (U.S.) (Grant U54 HG003067), National Human Genome Research Institute (U.S.) (Grant R01 HG006855), Stanley Center for Psychiatric Research, Alexander and Margaret Stewart Trust, National Institute of Mental Health (U.S.) (Grant R01 MH 077139), National Institute of Mental Health (U.S.) (Grant RC2 MH089905), Sylvan C. Herman Foundation

SummaryAlthough many distinct mutations in a variety of genes are known to cause Amyotrophic Lateral Sclerosis (ALS), it remains poorly understood how they selectively impact motor neuron biology and whether they converge on common pathways to cause neuronal degeneration. Here, we have combined reprogramming and stem cell differentiation approaches with genome engineering and RNA sequencing to define the transcriptional and functional changes that are induced in human motor neurons by mutant SOD1. Mutant SOD1 protein induced a transcriptional signature indicative of increased oxidative stress, reduced mitochondrial function, altered subcellular transport, and activation of the ER stress and unfolded protein response pathways. Functional studies demonstrated that these pathways were perturbed in a manner dependent on the SOD1 mutation. Finally, interrogation of stem-cell-derived motor neurons produced from ALS patients harboring a repeat expansion in C9orf72 indicates that at least a subset of these changes are more broadly conserved in ALS.

Contains fulltext : 137213.pdf (Publisher’s version ) (Closed access) Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage ( approximately 13x) and trio design enabled extensive characterization of structural variation, including midsize events (30-500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.

A principal obstacle to completing maps and analyses of the human genome involves the genome’s “inaccessible” regions: sequences (often euchromatic and containing genes) that are isolated from the rest of the euchromatic genome by heterochromatin and other repeat-rich sequence. We describe a way to localize these sequences by using ancestry linkage disequilibrium in populations that derive ancestry from at least three continents, as is the case for Latinos. We used this approach to map the genomic locations of almost 20 megabases of sequence unlocalized or missing from the current human genome reference (NCBI Genome GRCh37)—a substantial fraction of the human genome’s remaining unmapped sequence. We show that the genomic locations of most sequences that originated from fosmids and larger clones can be admixture mapped in this way, by using publicly available whole-genome sequence data. Genome assembly efforts and future builds of the human genome reference will be strongly informed by this localization of genes and other euchromatic sequences that are embedded within highly repetitive pericentromeric regions.

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 (

Summary: The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies). Availability: SNAP server is available at http://www.broad.mit.edu/mpg/snap/. Contact: debakker@broad.mit.edu

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.