99 research outputs found
Testing the role of predicted gene knockouts in human anthropometric trait variation
National Heart, Lung, and Blood Institute (NHLBI)
S.L. is funded by a Canadian Institutes of Health Research
Banting doctoral scholarship. G.L. is funded by Genome Canada
and Génome Québec; the Canada Research Chairs program; and
the Montreal Heart Institute Foundation. C.M.L. is supported by
Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A);
and the Li Ka Shing Foundation. N.S. is funded by National Institutes
of Health (grant numbers HL088456, HL111089, HL116747).
The Mount Sinai BioMe Biobank Program is supported by the Andrea
and Charles Bronfman Philanthropies. GO ESP is supported
by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO,
RC2 HL-102924 to WHISP). The ESP exome sequencing was
performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL-
102926 to SeattleGO). EGCUT work was supported through the
Estonian Genome Center of University of Tartu by the Targeted
Financing from the Estonian Ministry of Science and Education
(grant number SF0180142s08); the Development Fund of the University
of Tartu (grant number SP1GVARENG); the European Regional
Development Fund to the Centre of Excellence in
Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and
through FP7 (grant number 313010). EGCUT were further supported
by the US National Institute of Health (grant number
R01DK075787). A.K.M. was supported by an American Diabetes
Association Mentor-Based Postdoctoral Fellowship (#7-12-MN-
02). The BioVU dataset used in the analyses described were obtained
from Vanderbilt University Medical Centers BioVU which
is supported by institutional funding and by the Vanderbilt CTSA
grant ULTR000445 from NCATS/NIH. Genome-wide genotyping
was funded by NIH grants RC2GM092618 from NIGMS/OD and
U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access
publication charges for this article was provided by a block
grant from Research Councils UK to the University of Cambridge
Testing the role of predicted gene knockouts in human anthropometric trait variation
National Heart, Lung, and Blood Institute (NHLBI)
S.L. is funded by a Canadian Institutes of Health Research
Banting doctoral scholarship. G.L. is funded by Genome Canada
and Génome Québec; the Canada Research Chairs program; and
the Montreal Heart Institute Foundation. C.M.L. is supported by
Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A);
and the Li Ka Shing Foundation. N.S. is funded by National Institutes
of Health (grant numbers HL088456, HL111089, HL116747).
The Mount Sinai BioMe Biobank Program is supported by the Andrea
and Charles Bronfman Philanthropies. GO ESP is supported
by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO,
RC2 HL-102924 to WHISP). The ESP exome sequencing was
performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL-
102926 to SeattleGO). EGCUT work was supported through the
Estonian Genome Center of University of Tartu by the Targeted
Financing from the Estonian Ministry of Science and Education
(grant number SF0180142s08); the Development Fund of the University
of Tartu (grant number SP1GVARENG); the European Regional
Development Fund to the Centre of Excellence in
Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and
through FP7 (grant number 313010). EGCUT were further supported
by the US National Institute of Health (grant number
R01DK075787). A.K.M. was supported by an American Diabetes
Association Mentor-Based Postdoctoral Fellowship (#7-12-MN-
02). The BioVU dataset used in the analyses described were obtained
from Vanderbilt University Medical Centers BioVU which
is supported by institutional funding and by the Vanderbilt CTSA
grant ULTR000445 from NCATS/NIH. Genome-wide genotyping
was funded by NIH grants RC2GM092618 from NIGMS/OD and
U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access
publication charges for this article was provided by a block
grant from Research Councils UK to the University of Cambridge
Testing the role of predicted gene knockouts in human anthropometric trait variation
National Heart, Lung, and Blood Institute (NHLBI)
S.L. is funded by a Canadian Institutes of Health Research
Banting doctoral scholarship. G.L. is funded by Genome Canada
and Génome Québec; the Canada Research Chairs program; and
the Montreal Heart Institute Foundation. C.M.L. is supported by
Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A);
and the Li Ka Shing Foundation. N.S. is funded by National Institutes
of Health (grant numbers HL088456, HL111089, HL116747).
The Mount Sinai BioMe Biobank Program is supported by the Andrea
and Charles Bronfman Philanthropies. GO ESP is supported
by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO,
RC2 HL-102924 to WHISP). The ESP exome sequencing was
performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL-
102926 to SeattleGO). EGCUT work was supported through the
Estonian Genome Center of University of Tartu by the Targeted
Financing from the Estonian Ministry of Science and Education
(grant number SF0180142s08); the Development Fund of the University
of Tartu (grant number SP1GVARENG); the European Regional
Development Fund to the Centre of Excellence in
Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and
through FP7 (grant number 313010). EGCUT were further supported
by the US National Institute of Health (grant number
R01DK075787). A.K.M. was supported by an American Diabetes
Association Mentor-Based Postdoctoral Fellowship (#7-12-MN-
02). The BioVU dataset used in the analyses described were obtained
from Vanderbilt University Medical Centers BioVU which
is supported by institutional funding and by the Vanderbilt CTSA
grant ULTR000445 from NCATS/NIH. Genome-wide genotyping
was funded by NIH grants RC2GM092618 from NIGMS/OD and
U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access
publication charges for this article was provided by a block
grant from Research Councils UK to the University of Cambridge
Higher levels of glutamate in the associative-striatum of subjects with prodromal symptoms of schizophrenia and patients with first-episode psychosis
The glutamatergic and dopaminergic systems are thought to be involved in the pathophysiology of schizophrenia. Their interaction has been widely documented and may have a role in the neurobiological basis of the disease. The aim of this study was to compare, using proton magnetic resonance spectroscopy (1H-MRS), glutamate levels in the precommissural dorsal-caudate (a dopamine-rich region) and the cerebellar cortex (negligible for dopamine) in the following: (1) 18 antipsychotic-naïve subjects with prodromal symptoms and considered to be at ultra high-risk for schizophrenia (UHR), (2) 18 antipsychotic-naïve first- episode psychosis patients (FEP), and (3) 40 age- and sex- matched healthy controls. All subjects underwent a 1H-MRS study using a 3Tesla scanner. Glutamate levels were quantified and corrected for the proportion of cerebrospinal fluid and percentage of gray matter in the voxel. The UHR and FEP groups showed higher levels of glutamate than controls, without differences between UHR and FEP. In the cerebellum, no differences were seen between the three groups. The higher glutamate level in the precommissural dorsal-caudate and not in the cerebellum of UHR and FEP suggests that a high glutamate level (a) precedes the onset of schizophrenia, and (b) is present in a dopamine-rich region previously implicated in the pathophysiology of schizophrenia.peer-reviewe
A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined similar to 18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p <5 x 10(-8)) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p <5 x 10(-8)). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling MSRA, EBF2).Peer reviewe
Localization and broadband follow-up of the gravitational-wave transient GW150914
A gravitational-wave (GW) transient was identified in data recorded by the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) detectors on 2015 September 14. The event, initially designated G184098 and later given the name GW150914, is described in detail elsewhere. By prior arrangement, preliminary estimates of the time, significance, and sky location of the event were shared with 63 teams of observers covering radio, optical, near-infrared, X-ray, and gamma-ray wavelengths with ground- and space-based facilities. In this Letter we describe the low-latency analysis of the GW data and present the sky localization of the first observed compact binary merger. We summarize the follow-up observations reported by 25 teams via private Gamma-ray Coordinates Network circulars, giving an overview of the participating facilities, the GW sky localization coverage, the timeline, and depth of the observations. As this event turned out to be a binary black hole merger, there is little expectation of a detectable electromagnetic (EM) signature. Nevertheless, this first broadband campaign to search for a counterpart of an Advanced LIGO source represents a milestone and highlights the broad capabilities of the transient astronomy community and the observing strategies that have been developed to pursue neutron star binary merger events. Detailed investigations of the EM data and results of the EM follow-up campaign are being disseminated in papers by the individual teams
All-sky search for gravitational-wave bursts in the second joint LIGO-Virgo run
We present results from a search for gravitational-wave bursts in the data
collected by the LIGO and Virgo detectors between July 7, 2009 and October 20,
2010: data are analyzed when at least two of the three LIGO-Virgo detectors are
in coincident operation, with a total observation time of 207 days. The
analysis searches for transients of duration < 1 s over the frequency band
64-5000 Hz, without other assumptions on the signal waveform, polarization,
direction or occurrence time. All identified events are consistent with the
expected accidental background. We set frequentist upper limits on the rate of
gravitational-wave bursts by combining this search with the previous LIGO-Virgo
search on the data collected between November 2005 and October 2007. The upper
limit on the rate of strong gravitational-wave bursts at the Earth is 1.3
events per year at 90% confidence. We also present upper limits on source rate
density per year and Mpc^3 for sample populations of standard-candle sources.
As in the previous joint run, typical sensitivities of the search in terms of
the root-sum-squared strain amplitude for these waveforms lie in the range 5
10^-22 Hz^-1/2 to 1 10^-20 Hz^-1/2. The combination of the two joint runs
entails the most sensitive all-sky search for generic gravitational-wave bursts
and synthesizes the results achieved by the initial generation of
interferometric detectors.Comment: 15 pages, 7 figures: data for plots and archived public version at
https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=70814&version=19, see
also the public announcement at
http://www.ligo.org/science/Publication-S6BurstAllSky
Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways.
The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization
Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration
Both short and long sleep are associated with an adverse lipid profile,
likely through different biological pathways. To elucidate the biology
of sleep-associated adverse lipid profile, we conduct multi-ancestry
genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c,
LDL-c and triglycerides). In the total study sample
(discovery + replication) of 126,926 individuals from 5 different
ancestry groups, when considering either long or short total sleep time
interactions in joint analyses, we identify 49 previously unreported
lipid loci, and 10 additional previously unreported lipid loci in a
restricted sample of European-ancestry cohorts. In addition, we identify
new gene-sleep interactions for known lipid loci such as LPL and PCSK9.
The previously unreported lipid loci have a modest explained variance
in lipid levels: most notable, gene-short-sleep interactions explain
4.25% of the variance in triglyceride level. Collectively, these
findings contribute to our understanding of the biological mechanisms
involved in sleep-associated adverse lipid profiles.</p
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