Institution
Wellcome Trust Sanger Institute
Nonprofit•Cambridge, United Kingdom•
About: Wellcome Trust Sanger Institute is a nonprofit organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Genome. The organization has 4009 authors who have published 9671 publications receiving 1224479 citations.
Topics: Population, Genome, Gene, Genome-wide association study, Genomics
Papers published on a yearly basis
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University of Copenhagen1, University of California, Berkeley2, University of Massachusetts Amherst3, Wellcome Trust Sanger Institute4, Technical University of Denmark5, Pennsylvania State University6, La Trobe University7, Stanford University8, King Abdullah University of Science and Technology9, University of Cambridge10, Estonian Biocentre11, University of Tartu12, University of California, San Francisco13, Washington State University14, University of Porto15, University of Illinois at Urbana–Champaign16, Carlos III Health Institute17, University of Utah18, Science for Life Laboratory19, Aarhus University20, University College London21, University of Reading22, University of Bristol23, University of Guadalajara24, University of Bologna25, Oregon State University26, University of Paris27, University of Zurich28, Max Planck Society29, St. John's University30, University of California, Irvine31, University of Tarapacá32, University of Toulouse33, Novosibirsk State University34, Russian Academy of Sciences35, Kemerovo State University36, Bashkir State University37, North-Eastern Federal University38, Western Washington University39, Northwest Community College40, Simon Fraser University41, University of Western Ontario42, Laboratory of Molecular Biology43, University of Kansas44, University of California, Davis45, Texas A&M University46, Santa Barbara Museum of Natural History47, Southern Methodist University48
TL;DR: The results suggest that there has been gene flow between some Native Americans from both North and South America and groups related to East Asians and Australo-Melanesians, the latter possibly through an East Asian route that might have included ancestors of modern Aleutian Islanders.
Abstract: How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative "Paleoamerican" relict populations, including the historical Mexican Pericues and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.
459 citations
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TL;DR: A significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4 is identified and identified.
Abstract: Background: Over the past decade genome-wide association studies (GWAS) have been applied to aid in the
understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried
by the true risk variants and the corresponding statistical power to observe such effects given the study design and
sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however,
these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).
Methods: We conducted a large-scale coordinated international collaboration to combine independent genotyping
data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide
genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary
statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).
Results: We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription
factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social
skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia
which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P=9 ×10−6). We further
combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to
identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12
novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a
‘neurodevelopmental hub’ on chromosome 8p11.23.
Conclusions: This study is an important step in the ongoing endeavour to identify the loci which underpin the common
variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia
and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4.
458 citations
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Queen Mary University of London1, Discovery Institute2, University of Glasgow3, Columbia University4, University College London5, King's College London6, Dartmouth College7, Brigham and Women's Hospital8, Leiden University Medical Center9, University of California, Los Angeles10, University of California, San Diego11, Temple University12, Brown University13, University of Edinburgh14, Wellcome Trust Sanger Institute15, Babraham Institute16, University of Bristol17, University of Essex18, CAS-MPG Partner Institute for Computational Biology19, RWTH Aachen University20, Macau University of Science and Technology21
TL;DR: Key challenges to understand clock mechanisms and biomarker utility are discussed, including dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models.
Abstract: Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.
457 citations
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TL;DR: The role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines, skin, and fat is explored and it is proposed that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissues-specificity.
Abstract: While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.
457 citations
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TL;DR: A meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease identifies novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
Abstract: Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
456 citations
Authors
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Name | H-index | Papers | Citations |
---|---|---|---|
Nicholas J. Wareham | 212 | 1657 | 204896 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
Panos Deloukas | 162 | 410 | 154018 |
Michael R. Stratton | 161 | 443 | 142586 |
David W. Johnson | 160 | 2714 | 140778 |
Michael John Owen | 160 | 1110 | 135795 |
Naveed Sattar | 155 | 1326 | 116368 |
Robert E. W. Hancock | 152 | 775 | 88481 |
Julian Parkhill | 149 | 759 | 104736 |
Nilesh J. Samani | 149 | 779 | 113545 |
Michael Conlon O'Donovan | 142 | 736 | 118857 |
Jian Yang | 142 | 1818 | 111166 |
Christof Koch | 141 | 712 | 105221 |
Andrew G. Clark | 140 | 823 | 123333 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |