Human pluripotent stem cells recurrently acquire and expand dominant negative P53 mutations.

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.NB is the Herbert Cohn Chair in Cancer Research and was partially supported by The Rosetrees Trust and The Azrieli Foundation. Costs associated with acquiring and sequencing hESC lines were supported by HHMI and the Stanley Center for Psychiatric Research. FTM, SAM, and KE were supported by grants from the NIH (HL109525, 5P01GM099117, 5K99NS08371). KE was supported by the Miller consortium of the HSCI and FTM is currently supported by funds from the Wellcome Trust, the Medical Research Council (MR/P501967/1), and the Academy of Medical Sciences (SBF001\1016)

An integrated map of structural variation in 2,504 human genomes

© 2015 Macmillan Publishers Limited. All rights reserved. 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

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Large multi-allelic copy number variations in humans

Thousands of genome segments appear to be present in widely varying copy number in different human genomes. We developed ways to use increasingly abundant whole genome sequence data to identify the copy numbers, alleles and haplotypes present at most large, multi-allelic CNVs (mCNVs). We analyzed 849 genomes sequenced by the 1000 Genomes Project to identify most large (>5 kb) mCNVs, including 3,878 duplications, of which 1,356 appear to have three or more segregating alleles. We find that mCNVs give rise to most human gene-dosage variation – exceeding sevenfold the contribution of deletions and biallelic duplications – and that this variation in gene dosage generates abundant variation in gene expression. We describe “runaway duplication haplotypes” in which genes, including HPR and ORM1, have mutated to high copy number on specific haplotypes. We describe partially successful initial strategies for analyzing mCNVs via imputation and provide an initial data resource to support such analyses

Chromosomal phase improves aneuploidy detection in non-invasive prenatal testing at low fetal DNA fractions

Abstract 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

Whole-genome analysis of human embryonic stem cells enables rational line selection based on genetic variation.

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.Academy of Medical Sciences, New York Stem Cell Foundatio

A global reference for human genetic variation

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.Wellcome Trust (London, England) (Core Award 090532/Z/09/Z)Wellcome Trust (London, England) (Senior Investigator Award 095552/Z/11/Z )Wellcome Trust (London, England) (WT095908)Wellcome Trust (London, England) (WT109497)Wellcome Trust (London, England) (WT098051)Wellcome Trust (London, England) (WT086084/Z/08/Z)Wellcome Trust (London, England) (WT100956/Z/13/Z )Wellcome Trust (London, England) (WT097307)Wellcome Trust (London, England) (WT0855322/Z/08/Z )Wellcome Trust (London, England) (WT090770/Z/09/Z )Wellcome Trust (London, England) (Major Overseas program in Vietnam grant 089276/Z.09/Z)Medical Research Council (Great Britain) (grant G0801823)Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I02593X/1)Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I021213/1)Zhongguo ke xue ji shu qing bao yan jiu suo. Office of 863 Programme of China (2012AA02A201)National Basic Research Program of China (2011CB809201)National Basic Research Program of China (2011CB809202)National Basic Research Program of China (2011CB809203)National Natural Science Foundation of China (31161130357)Shenzhen Municipal Government of China (grant ZYC201105170397A)Canadian Institutes of Health Research (grant 136855)Quebec Ministry of Economic Development, Innovation, and Exports (PSR-SIIRI-195)Germany. Bundesministerium für Bildung und Forschung (0315428A)Germany. Bundesministerium für Bildung und Forschung (01GS08201)Germany. Bundesministerium für Bildung und Forschung (BMBF-EPITREAT grant 0316190A)Deutsche Forschungsgemeinschaft (Emmy Noether Grant KO4037/1-1)Beatriu de Pinos Program (2006 BP-A 10144)Beatriu de Pinos Program (2009 BP-B 00274)Spanish National Institute for Health (grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER)Japan Society for the Promotion of Science (fellowship number PE13075)Marie Curie Actions Career Integration (grant 303772)Fonds National Suisse del la Recherche, SNSF, Scientifique (31003A_130342)National Center for Biotechnology Information (U.S.) (U54HG3067)National Center for Biotechnology Information (U.S.) (U54HG3273)National Center for Biotechnology Information (U.S.) (U01HG5211)National Center for Biotechnology Information (U.S.) (U54HG3079)National Center for Biotechnology Information (U.S.) (R01HG2898)National Center for Biotechnology Information (U.S.) (R01HG2385)National Center for Biotechnology Information (U.S.) (RC2HG5552)National Center for Biotechnology Information (U.S.) (U01HG6513)National Center for Biotechnology Information (U.S.) (U01HG5214)National Center for Biotechnology Information (U.S.) (U01HG5715)National Center for Biotechnology Information (U.S.) (U01HG5718)National Center for Biotechnology Information (U.S.) (U01HG5728)National Center for Biotechnology Information (U.S.) (U41HG7635)National Center for Biotechnology Information (U.S.) (U41HG7497)National Center for Biotechnology Information (U.S.) (R01HG4960)National Center for Biotechnology Information (U.S.) (R01HG5701)National Center for Biotechnology Information (U.S.) (R01HG5214)National Center for Biotechnology Information (U.S.) (R01HG6855)National Center for Biotechnology Information (U.S.) (R01HG7068)National Center for Biotechnology Information (U.S.) (R01HG7644)National Center for Biotechnology Information (U.S.) (DP2OD6514)National Center for Biotechnology Information (U.S.) (DP5OD9154)National Center for Biotechnology Information (U.S.) (R01CA166661)National Center for Biotechnology Information (U.S.) (R01CA172652)National Center for Biotechnology Information (U.S.) (P01GM99568)National Center for Biotechnology Information (U.S.) (R01GM59290)National Center for Biotechnology Information (U.S.) (R01GM104390)National Center for Biotechnology Information (U.S.) (T32GM7790)National Center for Biotechnology Information (U.S.) (P01GM99568)National Center for Biotechnology Information (U.S.) (R01HL87699)National Center for Biotechnology Information (U.S.) (R01HL104608)National Center for Biotechnology Information (U.S.) (T32HL94284)National Center for Biotechnology Information (U.S.) (HHSN268201100040C)National Center for Biotechnology Information (U.S.) (HHSN272201000025C)Lundbeck Foundation (grant R170-2014-1039Simons Foundation (SFARI award SF51)National Science Foundation (U.S.) (Research Fellowship DGE-1147470

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes, and obesity

Hundreds of genes reside in structurally complex, poorly understood regions of the human genome1-3. One such region contains the three amylase genes (AMY2B, AMY2A, and AMY1) responsible for digesting starch into sugar. The copy number of AMY1 is reported to be the genome’s largest influence on obesity4, though genome-wide association studies for obesity have found this locus unremarkable. Using whole genome sequence analysis3,5, droplet digital PCR6, and genome mapping7, we identified eight common structural haplotypes of the amylase locus that suggest its mutational history. We found that AMY1 copy number in individuals’ genomes is generally even (rather than odd) and partially correlates to nearby SNPs, which do not associate with BMI. We measured amylase gene copy number in 1,000 obese or lean Estonians and in two other cohorts totaling ~3,500 individuals. We had 99% power to detect the lower bound of the reported effects on BMI4, yet found no association