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.

Genomic history of the Sardinian population

Abstract: The population of the Mediterranean island of Sardinia has made important contributions to genome-wide association studies of complex disease traits and, based on ancient DNA studies of mainland Europe, Sardinia is hypothesized to be a unique refuge for early Neolithic ancestry. To provide new insights on the genetic history of this flagship population, we analyzed 3,514 whole-genome sequenced individuals from Sardinia. Sardinian samples show elevated levels of shared ancestry with Basque individuals, especially samples from the more historically isolated regions of Sardinia. Our analysis also uniquely illuminates how levels of genetic similarity with mainland ancient DNA samples varies subtly across the island. Together, our results indicate that within-island substructure and sex-biased processes have substantially impacted the genetic history of Sardinia. These results give new insight into the demography of ancestral Sardinians and help further the understanding of sharing of disease risk alleles between Sardinia and mainland populations.

Mitogenome Diversity in Sardinians: A Genetic Window onto an Island's Past.

Abstract: Sardinians are “outliers” in the European genetic landscape and, according to paleogenomic nuclear data, the closest to early European Neolithic farmers. To learn more about their genetic ancestry, we analyzed 3,491 modern and 21 ancient mitogenomes from Sardinia. We observed that 78.4% of modern mitogenomes cluster into 89 haplogroups that most likely arose in situ. For each Sardinian-specific haplogroup (SSH), we also identified the upstream node in the phylogeny, from which non-Sardinian mitogenomes radiate. This provided minimum and maximum time estimates for the presence of each SSH on the island. In agreement with demographic evidence, almost all SSHs coalesce in the post-Nuragic, Nuragic and Neolithic-Copper Age periods. For some rare SSHs, however, we could not dismiss the possibility that they might have been on the island prior to the Neolithic, a scenario that would be in agreement with archeological evidence of a Mesolithic occupation of Sardinia.

Age-related macular degeneration-associated variants at chromosome 10q26 do not significantly alter ARMS2 and HTRA1 transcript levels in the human retina.

Abstract: PURPOSE Multiple studies demonstrate a strong association between three variants at chromosome 10q26 - rs10490924, del443ins54, and rs11200638 - near the age-related maculopathy susceptibility 2 (ARMS2) and high-temperature requirement factor A1 (HTRA1) genes with susceptibility to age-related macular degeneration (AMD). In different reports, the del443ins54 and rs11200638 variants are suggested to affect ARMS2 mRNA stability and/or HTRA1 mRNA expression, respectively. The goal of this study is to examine whether these AMD-associated variants alter expression levels of ARMS2 and HTRA1 in human retina samples. METHODS Genomic DNA and total RNA were obtained from 35 human retinas (three young controls, average age=32 years; twenty aged controls, average age=72 years; and twelve AMD retinas, average age=77 years) using standard procedures. As ARMS2 exhibits higher expression in the human placenta, we also included eighteen placenta samples in our analysis. Four polymorphisms - rs2736911, rs10490924, del443ins54, and rs11200638 - were genotyped by PCR followed by sequencing. Expression of ARMS2, HTRA1 and three endogenous control genes (rRNA [rRNA], hypoxanthine phosphoribosyltransferase 1 [HPRT1], and glyceraldehyde-3-phosphate dehydrogenase [GAPDH]) was measured by real-time quantitative RT-PCR using Taqman gene expression or SYBR Green assays. RESULTS ARMS2 and HTRA1 mRNA levels did not show a significant difference in expression among the control (young and elderly) and AMD retinas. No association of del443ins54 and rs11200638 variants was detected with mRNA expression levels of ARMS2 or HTRA1 in the retina. Human placenta samples showed high variability in expression levels. CONCLUSIONS We did not find association between AMD susceptibility variants at 10q26 and steady-state expression levels of either ARMS2 or HTRA1 in the human retina.

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium

Abstract: Author(s): Sajuthi, Satria P; DeFord, Peter; Jackson, Nathan D; Montgomery, Michael T; Everman, Jamie L; Rios, Cydney L; Pruesse, Elmar; Nolin, James D; Plender, Elizabeth G; Wechsler, Michael E; Mak, Angel Cy; Eng, Celeste; Salazar, Sandra; Medina, Vivian; Wohlford, Eric M; Huntsman, Scott; Nickerson, Deborah A; Germer, Soren; Zody, Michael C; Abecasis, Goncalo; Kang, Hyun Min; Rice, Kenneth M; Kumar, Rajesh; Oh, Sam; Rodriguez-Santana, Jose; Burchard, Esteban G; Seibold, Max A | Abstract: Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Fast and accurate short read alignment with Burrows–Wheeler transform

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]

Fast gapped-read alignment with Bowtie 2

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.

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

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.

Initial sequencing and analysis of the human genome.

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.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

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.