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Sue Richards

Bio: Sue Richards is a academic researcher at Oregon Health & Science University who has co-authored 6 publication(s) receiving 11583 citation(s). The author has an hindex of 6. The author has done significant research in the topic(s): Human Variome Project & External quality assessment.

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Open accessJournal ArticleDOI: 10.1038/GIM.2015.30
Sue Richards1, Nazneen Aziz2, Nazneen Aziz3, Sherri J. Bale4  +9 moreInstitutions (11)
Abstract: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

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11,349 Citations


Open accessJournal ArticleDOI: 10.1097/01.GIM.0000182738.95726.CA
Anne Maddalena1, Sherri J. Bale1, Soma Das2, Wayne W. Grody3  +1 moreInstitutions (4)
Abstract: Disclaimer: These standards and guidelines are designed primarily as an educational resource for clinical laboratory geneticists to help them provide quality clinical laboratory genetic services. Adherence to these standards and guidelines does not necessarily ensure a successful medical outcome. These standards and guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. In determining the propriety of any specific procedure or test, the clinical molecular geneticist should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. It may be prudent, however, to document in the laboratory record the rationale for any significant deviation from these standards and guidelines.

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79 Citations


Journal ArticleDOI: 10.1038/NG2024
28 Mar 2007-Nature Genetics
Abstract: Lists of variations in genomic DNA and their effects have been kept for some time and have been used in diagnostics and research Although these lists have been carefully gathered and curated, there has been little standardization and coordination, complicating their use Given the myriad possible variations in the estimated 24,000 genes in the human genome, it would be useful to have standard criteria for databases of variation Incomplete collection and ascertainment of variants demonstrates a need for a universally accessible system These and other problems led to the World Heath Organization–cosponsored meeting on June 20–23, 2006 in Melbourne, Australia, which launched the Human Variome Project This meeting addressed all areas of human genetics relevant to collection of information on variation and its effects Members of each of eight sessions (the clinic and phenotype, the diagnostic laboratory, the research laboratory, curation and collection, informatics, relevance to the emerging world, integration and federation and funding and sustainability) developed a number of recommendations that were then organized into a total of 96 recommendations to act as a foundation for future work worldwide Here we summarize the background of the project, the meeting and its recommendations

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Topics: Human Variome Project (66%)

64 Citations


Open accessJournal ArticleDOI: 10.5858/ARPA.2012-0311-RA
Abstract: Context.—Participation in proficiency testing (PT) or external quality assessment (EQA) programs allows the assessment and comparison of test performance among different clinical laboratories and technologies. In addition to the approximately 2300 tests for individual genetic disorders, recent advances in technology have enabled the development of clinical tests that quickly and economically analyze the entire human genome. New PT/EQA approaches are needed to ensure the continued quality of these complex tests. Objectives.—To review the availability and scope of PT/EQA for molecular genetic testing for inherited conditions in Europe, Australasia, and the United States; to evaluate the successes and demonstrated value of available PT/EQA programs; and to examine the challenges to the provision of comprehensive PT/EQA posed by new laboratory practices and methodologies. Data Sources.—The available literature on this topic was reviewed and supplemented with personal experiences of several PT/EQA providers. C...

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40 Citations


Open accessJournal ArticleDOI: 10.1002/HUMU.21379
01 Dec 2010-Human Mutation
Abstract: The third Human Variome Project (HVP) Meeting "Integration and Implementation" was held under UNESCO Patronage in Paris, France, at the UNESCO Headquarters May 10-14, 2010. The major aims of the HVP are the collection, curation, and distribution of all human genetic variation affecting health. The HVP has drawn together disparate groups, by country, gene of interest, and expertise, who are working for the common good with the shared goal of pushing the boundaries of the human variome and collaborating to avoid unnecessary duplication. The meeting addressed the 12 key areas that form the current framework of HVP activities: Ethics; Nomenclature and Standards; Publication, Credit and Incentives; Data Collection from Clinics; Overall Data Integration and Access-Peripheral Systems/Software; Data Collection from Laboratories; Assessment of Pathogenicity; Country Specific Collection; Translation to Healthcare and Personalized Medicine; Data Transfer, Databasing, and Curation; Overall Data Integration and Access-Central Systems; and Funding Mechanisms and Sustainability. In addition, three societies that support the goals and the mission of HVP also held their own Workshops with the view to advance disease-specific variation data collection and utilization: the International Society for Gastrointestinal Hereditary Tumours, the Micronutrient Genomics Project, and the Neurogenetics Consortium.

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Topics: Human Variome Project (64%), Variome (61%)

37 Citations


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Open accessJournal ArticleDOI: 10.1038/NATURE19057
18 Aug 2016-Nature
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

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Topics: Exome (62%), Genomics (54%), Genetic variation (53%) ...read more

7,679 Citations


Open accessJournal ArticleDOI: 10.1093/NAR/GKV1222
Melissa J. Landrum1, Jennifer M. Lee1, Mark L. Benson1, Garth Brown1  +15 moreInstitutions (1)
Abstract: ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) at the National Center for Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical significance of variants for reported conditions. The database includes germline and somatic variants of any size, type or genomic location. Interpretations are submitted by clinical testing laboratories, research laboratories, locus-specific databases, OMIM®, GeneReviews™, UniProt, expert panels and practice guidelines. In NCBI's Variation submission portal, submitters upload batch submissions or use the Submission Wizard for single submissions. Each submitted interpretation is assigned an accession number prefixed with SCV. ClinVar staff review validation reports with data types such as HGVS (Human Genome Variation Society) expressions; however, clinical significance is reported directly from submitters. Interpretations are aggregated by variant-condition combination and assigned an accession number prefixed with RCV. Clinical significance is calculated for the aggregate record, indicating consensus or conflict in the submitted interpretations. ClinVar uses data standards, such as HGVS nomenclature for variants and MedGen identifiers for conditions. The data are available on the web as variant-specific views; the entire data set can be downloaded via ftp. Programmatic access for ClinVar records is available through NCBI's E-utilities. Future development includes providing a variant-centric XML archive and a web page for details of SCV submissions.

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1,680 Citations


Journal ArticleDOI: 10.1038/NRG3031
Abstract: Exome sequencing — the targeted sequencing of the subset of the human genome that is protein coding — is a powerful and cost-effective new tool for dissecting the genetic basis of diseases and traits that have proved to be intractable to conventional gene-discovery strategies. Over the past 2 years, experimental and analytical approaches relating to exome sequencing have established a rich framework for discovering the genes underlying unsolved Mendelian disorders. Additionally, exome sequencing is being adapted to explore the extent to which rare alleles explain the heritability of complex diseases and health- related traits. These advances also set the stage for applying exome and whole-genome sequencing to facilitate clinical diagnosis and personalized disease-risk profiling.

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Topics: Exome (75%), Exome sequencing (75%), Cancer genome sequencing (64%)

1,584 Citations


Open accessPosted ContentDOI: 10.1101/030338
30 Oct 2015-bioRxiv
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities. The resulting catalogue of human genetic diversity has unprecedented resolution, with an average of one variant every eight bases of coding sequence and the presence of widespread mutational recurrence. The deep catalogue of variation provided by the Exome Aggregation Consortium (ExAC) can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 79% of which have no currently established human disease phenotype. Finally, we show that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human knockout variants in protein-coding genes.

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  • Figure 1 | Patterns of genetic variation in 60,706 humans. a, The size and diversity of public reference exome data sets. ExAC exceeds previous data sets in size for all studied populations. b, Principal component analysis (PCA) dividing ExAC individuals into five continental populations. PC2 and PC3 are shown; additional PCs are in Extended Data Fig. 5a. c, The allele frequency spectrum of ExAC highlights that the majority of genetic variants are rare and novel (absent from prior databases of genetic variation, such as dbSNP). d, The proportion of possible variation observed by mutational context and functional class. Over half of all possible CpG transitions are observed. Error bars represent standard error of the mean. e, f, The number (e), and frequency distribution (proportion singleton; f) of indels, by size. Compared to in-frame indels, frameshift variants are less common (have a higher proportion of singletons, a proxy for predicted deleteriousness on gene product). Error bars indicate 95% confidence intervals.
    Figure 1 | Patterns of genetic variation in 60,706 humans. a, The size and diversity of public reference exome data sets. ExAC exceeds previous data sets in size for all studied populations. b, Principal component analysis (PCA) dividing ExAC individuals into five continental populations. PC2 and PC3 are shown; additional PCs are in Extended Data Fig. 5a. c, The allele frequency spectrum of ExAC highlights that the majority of genetic variants are rare and novel (absent from prior databases of genetic variation, such as dbSNP). d, The proportion of possible variation observed by mutational context and functional class. Over half of all possible CpG transitions are observed. Error bars represent standard error of the mean. e, f, The number (e), and frequency distribution (proportion singleton; f) of indels, by size. Compared to in-frame indels, frameshift variants are less common (have a higher proportion of singletons, a proxy for predicted deleteriousness on gene product). Error bars indicate 95% confidence intervals.
  • Figure 2 | Mutational recurrence at large sample sizes. a, Proportion of validated de novo variants from two external data sets that are independently found in ExAC, separated by functional class and mutational context. Error bars represent standard error of the mean. Colours are consistent in a–d. b, Number of unique variants observed, by mutational context, as a function of number of individuals (downsampled from ExAC). CpG transitions, the most likely mutational event, begin reaching saturation at ~ 20,000 individuals. c, The site frequency spectrum is shown for each mutational context. d, For doubletons (variants with an allele count (AC) of 2), mutation rate is positively correlated with the likelihood of being found in two individuals of different continental populations. e, The mutability-adjusted proportion of singletons (MAPS) is shown across functional classes. Error bars represent standard error of the mean of the proportion of singletons.
    Figure 2 | Mutational recurrence at large sample sizes. a, Proportion of validated de novo variants from two external data sets that are independently found in ExAC, separated by functional class and mutational context. Error bars represent standard error of the mean. Colours are consistent in a–d. b, Number of unique variants observed, by mutational context, as a function of number of individuals (downsampled from ExAC). CpG transitions, the most likely mutational event, begin reaching saturation at ~ 20,000 individuals. c, The site frequency spectrum is shown for each mutational context. d, For doubletons (variants with an allele count (AC) of 2), mutation rate is positively correlated with the likelihood of being found in two individuals of different continental populations. e, The mutability-adjusted proportion of singletons (MAPS) is shown across functional classes. Error bars represent standard error of the mean of the proportion of singletons.
  • Figure 4 | Filtering for Mendelian variant discovery. a, Predicted missense and protein-truncating variants in 500 randomly chosen ExAC individuals were filtered based on allele frequency (AF) information from ESP, or from the remaining ExAC individuals. At a 0.1% allele frequency filter, ExAC provides greater power to remove candidate variants, leaving an average of 154 variants for analysis, compared to 1,090 after filtering against ESP. Popmax allele frequency also provides greater power than global allele frequency, particularly when populations are unequally sampled. b, Estimates of allele frequency in Europeans based on ESP are more precise at higher allele frequencies. Sampling variance and ascertainment bias make allele frequency estimates unreliable, posing problems for Mendelian variant filtration. 69% of ESP European singletons are not seen a second time in ExAC (tall bar at left), illustrating the dangers of filtering on very low allele counts. c, Allele frequency spectrum of disease-causing variants in the Human Gene Mutation Database (HGMD) and/or pathogenic or probable pathogenic variants in ClinVar for well-characterized autosomal dominant and autosomal recessive disease genes28. Most are not found in ExAC; however, many of the reportedly pathogenic variants found in ExAC are at too high a frequency to be consistent with disease prevalence and penetrance. d, Literature review of variants with > 1% global allele frequency or > 1% Latin American or South Asian population allele frequency confirmed there is insufficient evidence for pathogenicity for the majority of these variants. Variants were reclassified by American College of Medical Genetics and Genomics (ACMG) guidelines24.
    Figure 4 | Filtering for Mendelian variant discovery. a, Predicted missense and protein-truncating variants in 500 randomly chosen ExAC individuals were filtered based on allele frequency (AF) information from ESP, or from the remaining ExAC individuals. At a 0.1% allele frequency filter, ExAC provides greater power to remove candidate variants, leaving an average of 154 variants for analysis, compared to 1,090 after filtering against ESP. Popmax allele frequency also provides greater power than global allele frequency, particularly when populations are unequally sampled. b, Estimates of allele frequency in Europeans based on ESP are more precise at higher allele frequencies. Sampling variance and ascertainment bias make allele frequency estimates unreliable, posing problems for Mendelian variant filtration. 69% of ESP European singletons are not seen a second time in ExAC (tall bar at left), illustrating the dangers of filtering on very low allele counts. c, Allele frequency spectrum of disease-causing variants in the Human Gene Mutation Database (HGMD) and/or pathogenic or probable pathogenic variants in ClinVar for well-characterized autosomal dominant and autosomal recessive disease genes28. Most are not found in ExAC; however, many of the reportedly pathogenic variants found in ExAC are at too high a frequency to be consistent with disease prevalence and penetrance. d, Literature review of variants with > 1% global allele frequency or > 1% Latin American or South Asian population allele frequency confirmed there is insufficient evidence for pathogenicity for the majority of these variants. Variants were reclassified by American College of Medical Genetics and Genomics (ACMG) guidelines24.
Topics: Exome (57%), Genetic variation (54%), Human genetic variation (53%) ...read more

1,552 Citations


Open accessJournal ArticleDOI: 10.1038/GIM.2016.190
Sarah S. Kalia1, Kathy Adelman, Sherri J. Bale2, Wendy K. Chung3  +13 moreInstitutions (14)
Abstract: Disclaimer: These recommendations are designed primarily as an educational resource for medical geneticists and other healthcare providers to help them provide quality medical services. Adherence to these recommendations is completely voluntary and does not necessarily assure a successful medical outcome. These recommendations should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed toward obtaining the same results. In determining the propriety of any specific procedure or test, the clinician should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. Clinicians are encouraged to document the reasons for the use of a particular procedure or test, whether or not it is in conformance with this statement. Clinicians also are advised to take notice of the date this statement was adopted and to consider other medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures.To promote standardized reporting of actionable information from clinical genomic sequencing, in 2013, the American College of Medical Genetics and Genomics (ACMG) published a minimum list of genes to be reported as incidental or secondary findings. The goal was to identify and manage risks for selected highly penetrant genetic disorders through established interventions aimed at preventing or significantly reducing morbidity and mortality. The ACMG subsequently established the Secondary Findings Maintenance Working Group to develop a process for curating and updating the list over time. We describe here the new process for accepting and evaluating nominations for updates to the secondary findings list. We also report outcomes from six nominations received in the initial 15 months after the process was implemented. Applying the new process while upholding the core principles of the original policy statement resulted in the addition of four genes and removal of one gene; one gene did not meet criteria for inclusion. The updated secondary findings minimum list includes 59 medically actionable genes recommended for return in clinical genomic sequencing. We discuss future areas of focus, encourage continued input from the medical community, and call for research on the impact of returning genomic secondary findings.Genet Med 19 2, 249-255.

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Topics: Personal genomics (53%), Return of results (51%)

1,069 Citations


Performance
Metrics

Author's H-index: 6

No. of papers from the Author in previous years
YearPapers
20201
20151
20131
20101
20071
20051

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Author's top 4 most impactful journals

Genetics in Medicine

3 papers, 11.4K citations

Nature Genetics

1 papers, 64 citations

Human Mutation

1 papers, 37 citations

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Related Authors (1)
Wayne W. Grody

258 papers, 25.2K citations

73% related