2022 · Nature
Vassily Trubetskoy, Antonio F. Pardiñas, Ting Qi, Georgia Panagiotaropoulou, Swapnil Awasthi, Tim B. Bigdeli, Julien Bryois,…
Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.
2,848 citations3 viewsFull text
DOI: 10.1038/s41586-022-04434-52020 · Nature
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, J…
Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
23,187 citations4 viewsFull text
DOI: 10.1038/s41586-020-2649-22018 · Nature
Roy S. Herbst, Daniel Morgensztern, Chris Boshoff
5,039 citations0 views
DOI: 10.1038/nature251832015 · Nature
Corresponding authors, Adam Auton, Gonçalo R. Abecasis, David M. Altshuler, Richard Durbin, David R. Bentley, Aravinda Chakr…
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. Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics. The 1000 Genomes Project has sought to comprehensively catalogue human genetic variation across populations, providing a valuable public genomic resource. The data obtained so far have found applications ranging from association studies and fine mapping studies to the filtering of likely neutral variants in rare-disease cohorts. The authors now report on the final phase of the project, phase 3, which covers previously uncharacterized areas of human genetic diversity in terms of the populations sampled and categories of characterized variation. The sample now includes more than 2,500 individuals from 26 global populations, with low coverage whole-genome and deep exome sequencing, as well as dense microarray genotyping. They find that while most common variants are shared across populations, rarer variants are often restricted to closely related populations. The authors also demonstrate the use of the phase 3 dataset as a reference panel for imputation to improve the resolution in genetic association studies.
20,291 citations4 viewsFull text
DOI: 10.1038/nature153932015 · Nature
The LifeLines Cohort Study, Adam E. Locke, The AGEN-BMI Working Group, The GLGC, The ICBP, The MAGIC Investigators, Bratati …
4,946 citations3 viewsFull text
DOI: 10.1038/nature141772013 · Nature
Thomas A. Wynn, Ajay Chawla, Jeffrey W. Pollard
4,759 citations0 viewsFull text
DOI: 10.1038/nature120342012 · Nature
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, K. Venkatesan, Adam A. Margolin, Sung Joon Kim, Christopher J. Wilson…
8,589 citations1 views
DOI: 10.1038/nature110032011 · Nature
Nevin D. Young, Frédéric Debellé, Giles Oldroyd, René Geurts, Steven B. Cannon, Michael K. Udvardi, Vagner A. Benedito, Klau…
Sequencing of Medicago truncatula, a model organism of legume biology, shows that genome duplications had a role in the evolution of endosymbiotic nitrogen fixation. Legumes are unusual among plants in that they can carry out endosymbiotic nitrogen fixation with rhizobial bacteria. The genome of Medicago truncatula (also known as barrel medic or barrel clover), a well-established model for the study of legume biology, has now been sequenced. Genome analysis shows that M. truncatula has undergone several rounds of whole-genome duplication, and that the duplication that took place approximately 58 million years ago played an important part in the evolution of endosymbiotic nitrogen fixation. Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation1. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species2. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ∼94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa’s genomic toolbox.
1,307 citations2 viewsFull text
DOI: 10.1038/nature106252011 · Nature
Georg Ehret, Vasyl Pihur, Khanh-Dung Hoang Nguyen, Dan E. Arking, Gina Hilton, A Chakravarti, Murielle Bochud, P Munroe, Sue…
2,082 citations3 viewsFull text
DOI: 10.1038/nature104052011 · Nature
Peter Libby, Paul M. Ridker, Göran K. Hansson
4,025 citations0 views
DOI: 10.1038/nature101462007 · Nature
Sarah S. Murray, Dennis G. Ballinger, David R. Cox, David A. Hinds, Laura L. Stuvé, John W. Belmont, Suzanne M. Leal, David …
4,607 citations2 viewsFull text
DOI: 10.1038/nature062582005 · Nature
Drew Endy
1,510 citations0 views
DOI: 10.1038/nature043422004 · Nature
Róbert Langer, David A. Tirrell
3,185 citations0 views
DOI: 10.1038/nature023882003 · Nature
Richard A. Gibbs, John W. Belmont, Paul Hardenbol, T. D. Willis, Fuli Yu, Huanming Yang, Lan-Yang Ch'ang, Wei Huang, Bin Liu…
The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.
6,188 citations4 viewsFull text
DOI: 10.1038/nature021682001 · Nature
Michael Brownlee
9,104 citations2 views
DOI: 10.1038/414813a2000 · Nature
Toren Finkel, Nikki J. Holbrook
9,392 citations2 views
DOI: 10.1038/350416871999 · Nature
Leland H. Hartwell, J. J. Hopfield, Stanislas Leibler, Andrew W. Murray
3,708 citations0 viewsFull text
DOI: 10.1038/350115401998 · Nature
Stewart T. Cole, Roland Brosch, Julian Parkhill, T. Garnier, Carol Churcher, David Harris, Stephen V. Gordon, Karin Eiglmeie…
7,892 citations2 viewsFull text
DOI: 10.1038/311591993 · Nature
T.V.P. Bliss, Graham L. Collingridge
11,589 citations2 views
DOI: 10.1038/361031a01984 · Nature
P. V. E. McClintock
0 citations2 viewsFull text
DOI: 10.1038/308314a01983 · Nature
P. V. E. McClintock
2 citations2 views
DOI: 10.1038/306422a01980 · Nature
P. V. E. McClintock
4 citations1 views
DOI: 10.1038/284510b01979 · Nature
Roy M. Anderson, Robert M. May
3,254 citations1 viewsFull text
DOI: 10.1038/280361a01979 · Nature
P. V. E. McClintock
3 citations1 viewsFull text
DOI: 10.1038/278120a0