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Gonçalo R. Abecasis

Bio: Gonçalo R. Abecasis is an academic researcher from University of Michigan. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 179, co-authored 595 publications receiving 230323 citations. Previous affiliations of Gonçalo R. Abecasis include Johns Hopkins University School of Medicine & Wellcome Trust Centre for Human Genetics.


Papers
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Journal ArticleDOI
TL;DR: In this large study of preoperative opioid use that includes patient-reported outcome measures, more than 1 in 4 patients presenting for surgery reported opioid use, and these data provide important insights into this complicated patient population that would appear to help guide future perioperative opioid-weaning interventions.
Abstract: Importance Patterns of preoperative opioid use are not well characterized across different surgical services, and studies in this patient population have lacked important self-reported data of pain and affect Objectives To assess the prevalence of preoperative opioid use and the characteristics of these patients in a broadly representative surgical cohort Design, Setting, and Participants Cross-sectional, observational study of patients undergoing surgery at a tertiary care academic medical center Data were collected as a part of large prospective institutional research registries from March 1, 2010, through April 30, 2016 Exposures Preoperative patient and procedural characteristics, including prospectively assessed self-reported pain and functional measures Main Outcomes and Measures Patient-reported opioid use before surgery Results Of the total 34 186 patients recruited (542% women; mean [SD] age, 531 [161] years), preoperative opioid use was reported in 7894 (231%) The most common opioids used were hydrocodone bitartrate (4685 [594%]), tramadol hydrochloride (1677 [212%]), and oxycodone hydrochloride (1442 [183%]) Age of 31 to 40 years (adjusted odds ratio [aOR], 126; 95% CI, 110-145), tobacco use (former use aOR, 132 [95% CI, 122-142]; current use aOR, 162 [95% CI, 148-178]), illicit drug use (aOR, 174; 95% CI, 116-260), higher pain severity (aOR, 133; 95% CI, 131-135), depression (aOR, 122; 95% CI, 112-133), higher Fibromyalgia Survey scores (aOR, 106, 95% CI, 105-107), lower life satisfaction (aOR, 095, 95% CI, 093-096), and more medical comorbidities (American Society of Anesthesiology score aOR, 147 [95% CI, 137-158]; Charlson Comorbidity Index aOR, 129 [95% CI, 118-141]) were all independently associated with preoperative opioid use Preoperative opioid use was most commonly reported by patients undergoing orthopedic (226 [651%]) and neurosurgical spinal (596 [551%]) procedures and least common among patients undergoing thoracic procedures (244 [157%]) After adjusting for patient characteristics, the patients undergoing lower extremity procedures were most likely to report preoperative opioid use (aOR, 361; 95% CI, 281-464), as well as those undergoing pelvic (excluding hip) (aOR, 309; 95% CI, 188-508), upper extremity (aOR, 307; 95% CI, 212-445), and spinal or spinal cord (aOR, 268; 95% CI, 215-332) procedures, with the group undergoing intrathoracic surgery as the reference group Conclusions and Relevance In this large study of preoperative opioid use that includes patient-reported outcome measures, more than 1 in 4 patients presenting for surgery reported opioid use These data provide important insights into this complicated patient population that would appear to help guide future preoperative optimization and perioperative opioid-weaning interventions

145 citations

Journal ArticleDOI
TL;DR: Monte Carlo simulation is used to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs.
Abstract: Errors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.

145 citations

Journal ArticleDOI
TL;DR: The genotype inference method is illustrated by inferring over 53 million SNP genotypes for 78 children in the Centre d'Etude du Polymorphisme Humain families, showing its utility in obtaining high-density genotypes in different family structures.
Abstract: Our genotype inference method combines sparse marker data from a linkage scan and high-resolution SNP genotypes for several individuals to infer genotypes for related individuals. We illustrate the method's utility by inferring over 53 million SNP genotypes for 78 children in the Centre d'Etude du Polymorphisme Humain families. The method can be used to obtain high-density genotypes in different family structures, including nuclear families commonly used in complex disease gene mapping studies.

144 citations

01 Jan 2010
TL;DR: In this article, the authors examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs and up to 125,931 additional individuals.
Abstract: Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

142 citations

Journal ArticleDOI
07 Jan 2010-Blood
TL;DR: The results confirm the association of TMPRSS6 variants with iron level and provide further evidence of association with other anemia-related phenotypes.

141 citations


Cited by
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Journal ArticleDOI
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

43,862 citations

Journal ArticleDOI
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

37,898 citations

Journal ArticleDOI
TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

26,280 citations

Journal ArticleDOI
Eric S. Lander1, Lauren Linton1, Bruce W. Birren1, Chad Nusbaum1  +245 moreInstitutions (29)
15 Feb 2001-Nature
TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

22,269 citations

Journal ArticleDOI
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

20,557 citations