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Evaluating the strength of genetic results: Risks and responsibilities
Giorgio Sirugo,Gregory M. Cooper,Gregory P. Copenhaver,Gregory S. Barsh,Hua Tang,Scott M. Williams
- , 2019, DOI: 10.1371/journal.pgen.1008437
Abstract:
ABC: software for interactive browsing of genomic multiple sequence alignment data
Gregory M Cooper, Senthil AG Singaravelu, Arend Sidow
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-192
Abstract: The Application for Browsing Constraints (ABC) is interactive Java software for intuitive and efficient exploration of multiple sequence alignments and data typically associated with alignments. It is used to move quickly from a summary view of the entire alignment via arbitrary levels of resolution to individual alignment columns. It allows for the simultaneous display of quantitative data, (e.g., sequence similarity or evolutionary rates) and annotation data (e.g. the locations of genes, repeats, and constrained elements). It can be used to facilitate basic comparative sequence tasks, such as export of data in plain-text formats, visualization of phylogenetic trees, and generation of alignment summary graphics.The ABC is a lightweight, stand-alone, and flexible graphical user interface for browsing genomic multiple sequence alignments of specific loci, up to hundreds of kilobases or a few megabases in length. It is coded in Java for cross-platform use and the program and source code are freely available under the General Public License. Documentation and a sample data set are also available http://mendel.stanford.edu/sidowlab/downloads.html webcite.Functional elements in a genome accumulate inter-specific substitutions more slowly than neutral DNA throughout evolution [1]. Therefore, comparing orthologous genomic sequences from related species is useful for the identification of elements that play important roles in the biology of an organism [2-7]. While the statistical and computational methods for extracting comparative information are variable, the types of data involved are generally quite similar. First, a multiple sequence alignment is necessary. Second, a vector of quantitative scores is produced that describes the similarity of the nucleotides observed in small windows, or individual columns, of the alignment; percent identity is the metric used by the popular program VISTA [8], while a variety of other scoring methods also exist [9-12]. Third, annotation
Mammalian Comparative Sequence Analysis of the Agrp Locus
Christopher B. Kaelin, Gregory M. Cooper, Arend Sidow, Gregory S. Barsh
PLOS ONE , 2007, DOI: 10.1371/journal.pone.0000702
Abstract: Agouti-related protein encodes a neuropeptide that stimulates food intake. Agrp expression in the brain is restricted to neurons in the arcuate nucleus of the hypothalamus and is elevated by states of negative energy balance. The molecular mechanisms underlying Agrp regulation, however, remain poorly defined. Using a combination of transgenic and comparative sequence analysis, we have previously identified a 760 bp conserved region upstream of Agrp which contains STAT binding elements that participate in Agrp transcriptional regulation. In this study, we attempt to improve the specificity for detecting conserved elements in this region by comparing genomic sequences from 10 mammalian species. Our analysis reveals a symmetrical organization of conserved sequences upstream of Agrp, which cluster into two inverted repeat elements. Conserved sequences within these elements suggest a role for homeodomain proteins in the regulation of Agrp and provide additional targets for functional evaluation.
PLOS Genetics Data Sharing Policy: In Pursuit of Functional Utility
Gregory S. Barsh？,Gregory M. Cooper？,Gregory P. Copenhaver？,Greg Gibson？,Mark I. McCarthy？,Hua Tang？,Scott M. Williams
PLOS Genetics , 2015, DOI: 10.1371/journal.pgen.1005716
Abstract:
Molecular Analysis of Gli3, Ihh, Rab23, and Jag1 in a Rabbit Model of Craniosynostosis: Likely Exclusion as the Loci of Origin
Gregory M. Cooper,Gwen M. Taylor,James R. Gilbert,Joseph E. Losee,Mark P. Mooney
- , 2018, DOI: 10.1177/1055665617739001
Abstract: Craniosynostosis (CS) involves the premature fusion of one or more cranial sutures. The etiology of CS is complex and mutations in more than 50 distinct genes have been causally linked to the disorder. Many of the genes that have been associated with CS in humans play an essential role in tissue patterning and early craniofacial development. Among these genes are members of the Hedgehog (HH) and Notch signal transduction pathways, including the GLI family member Gli3, Indian Hedgehog (Ihh), the RAS oncogene family member Rab23, and the Notch ligand JAGGED1 (Jag1). We have previously described a colony of rabbits with a heritable pattern of coronal suture synostosis, although the genetic basis for synostosis within this model remains unknown. The present study was performed to determine if coding errors in Gli3, Ihh, Rab23, or Jag1 could be causally linked to craniosynostosis in this unique animal model. Sequencing of cDNA templates was performed using samples obtained from wild-type and craniosynostotic rabbits. Several nucleotide polymorphisms were identified in Gli3, Ihh, and Rab23, although these variants failed to segregate by phenotype. No nucleotide polymorphisms were identified in Jag1. These data indicate that the causal locus for heritable craniosynostosis in this rabbit model is not located within the protein coding regions of Gli3, Ihh, Rab23, or Jag1
Molecular Analyses in a Rabbit Model of Craniosynostosis: Likely Exclusion of Known Candidate Genes as the Loci of Origin
Gregory M. Cooper,Gwen M. Taylor,James R. Gilbert,Joseph E. Losee,Mark P. Mooney
- , 2019, DOI: 10.1177/1055665618808623
Abstract: Craniosynostosis (CS) involves the premature fusion of one or more cranial sutures. We work with a naturally occurring rabbit model of CS with an undefined etiology. Known causes of coronal CS were evaluated to identify potential associations with CS in the rabbit. Candidate genes were sequenced in control New Zealand White (NZW) rabbits (n = 4) and synostotic NZW rabbits (n = 4). Variants were identified by alignment using Clustal Omega. Single nucleotide variants (SNVs) were classified according to phenotypic associations and predicted impact on protein structure. Human correlates were identified in the database of single nucleotide polymorphisms (dbSNP). A total of 21 SNVs were identified in the 10 genes examined. Variant classification and inheritance patterns are inconsistent with causality. The genetic basis for disease in the CS rabbit likely involves novel loci and is not associated with known causes of coronal synostosis
Molecular Analysis of Ephrin A4 and Ephrin B1 in a Rabbit Model of Craniosynostosis: Likely Exclusion as the Loci of Origin
Gregory M. Cooper,Gwen M. Taylor,James Gilbert,Joseph E. Losee,Mark P. Mooney
- , 2018, DOI: 10.1597/16-135
Abstract: Craniosynostosis (CS) has a prevalence of approximately 1 in every 2000 live births and is characterized by the premature fusion of one or more cranial sutures. Failure to maintain the cell lineage boundary at the coronal suture is thought to be involved in the pathology of some forms of CS. The Ephrin family of receptor tyrosine kinases consists of membrane-bound receptors and ligands that control cell patterning and the formation of developmental boundaries. Mutations in the ephrin A4 (EFNA4) and ephrin B1 (EFNB1) ligands have been linked to nonsyndromic CS and craniofrontonasal syndrome, respectively, in patient samples. We have previously described a colony of rabbits with a heritable pattern of coronal suture synostosis, although the genetic basis for synostosis within this model remains unknown. The present study was performed to determine if EFNA4 or EFNB1 could be the loci of the causal mutation in this unique animal model. Sequencing of EFNA4 and EFNB1 was performed using templates obtained from wild-type (n = 4) and craniosynostotic (n = 4) rabbits. No structural coding errors were identified in either gene. A single-nucleotide transversion was identified in one wild-type rabbit within the third intron of EFNA4. These data indicate that the causal locus for heritable CS in this rabbit model is not located within the structural coding regions of either EFNA4 or EFNB1
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++
Eugene V. Davydov,David L. Goode,Marina Sirota,Gregory M. Cooper,Arend Sidow ,Serafim Batzoglou
PLOS Computational Biology , 2010, DOI: 10.1371/journal.pcbi.1001025
Abstract: Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.
A Bayesian Method for Evaluating and Discovering Disease Loci Associations
Xia Jiang,M. Michael Barmada,Gregory F. Cooper,Michael J. Becich
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0022075
Abstract: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need.
A Method for Using Belief Networks as Influence Diagrams
Gregory F. Cooper
Computer Science , 2013,
Abstract: This paper demonstrates a method for using belief-network algorithms to solve influence diagram problems. In particular, both exact and approximation belief-network algorithms may be applied to solve influence-diagram problems. More generally, knowing the relationship between belief-network and influence-diagram problems may be useful in the design and development of more efficient influence diagram algorithms.
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