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Genomics and Rare Diseases
Research Guide
What is Genomics and Rare Diseases?
Genomics and Rare Diseases is the application of genomic technologies, standards, and tools for interpreting genetic variants to diagnose and understand Mendelian disorders and other rare genetic conditions.
This field encompasses 59,145 papers focused on standards, guidelines, and tools for genetic variant interpretation in clinical genomics and Mendelian disorders. Key areas include pathogenicity prediction, functional annotations, sequence interpretation, and exome sequencing for identifying disease-causing variants. Growth rate over the past five years is not available.
Topic Hierarchy
Research Sub-Topics
ACMG Guidelines for Variant Pathogenicity
Researchers refine ACMG/AMP criteria for classifying sequence variants using population data and functional evidence. Studies validate frameworks through large-scale reclassification efforts and inter-laboratory comparisons.
Pathogenicity Prediction Algorithms for Missense Variants
This field develops and benchmarks computational tools like SIFT, PolyPhen, and CADD for predicting damaging mutations. Performance evaluations use clinically curated datasets to improve accuracy in rare disease contexts.
Exome Sequencing in Mendelian Disorders
Scientists apply whole-exome sequencing to identify causal variants in undiagnosed pediatric and adult Mendelian cases. Trio analyses and phenotype-driven filtering strategies enhance diagnostic yields.
Functional Annotation of Noncoding Genetic Variants
Research annotates regulatory variants using ENCODE data, chromatin states, and eQTL mapping for disease association. Tools like ANNOVAR and VEP integrate multi-omics to infer noncoding impact.
Variant Databases for Rare Disease Genomics
Curators build and query resources like ClinVar, gnomAD, and DECIPHER for aggregating pathogenic variant evidence. Studies assess database utility in reanalysis workflows and pathogenicity reassessment.
Why It Matters
Genomics and Rare Diseases enables precise diagnosis of Mendelian disorders through standardized variant interpretation, as outlined in "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" by Richards et al. (2015), which provides ACMG guidelines used in clinical labs worldwide to classify variants as pathogenic or benign. Tools like ANNOVAR by Wang et al. (2010) annotate high-throughput sequencing variants, supporting exome sequencing studies that identify causal mutations in rare diseases. The gnomAD database from Lek et al. (2016), analyzing protein-coding variation in 60,706 humans, establishes population allele frequencies to distinguish rare disease variants from common polymorphisms, aiding over 10,000 citations in clinical genomics applications.
Reading Guide
Where to Start
"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" by Richards et al. (2015), as it establishes the foundational ACMG framework for variant classification used in all clinical genomics for rare diseases.
Key Papers Explained
Richards et al. (2015) provide the ACMG consensus guidelines for variant interpretation, which are applied using annotation tools like ANNOVAR from Wang et al. (2010) and prediction methods from Adzhubei et al. (2010). Population references such as Auton et al. (2015) and Lek et al. (2016) supply allele frequencies, while Karczewski et al. (2020) quantifies mutational constraints to refine classifications. DePristo et al. (2011) supports upstream variant discovery from sequencing data.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent quantification of mutational constraints in 141,456 humans by Karczewski et al. (2020) extends gnomAD frameworks, focusing on intolerance scores for rare disease gene discovery. No preprints or news from the last 12 months indicate steady reliance on established ACMG standards and databases like gnomAD.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Standards and guidelines for the interpretation of sequence va... | 2015 | Genetics in Medicine | 30.3K | ✓ |
| 2 | A global reference for human genetic variation | 2015 | Nature | 19.0K | ✓ |
| 3 | The cBio Cancer Genomics Portal: An Open Platform for Explorin... | 2012 | Cancer Discovery | 17.8K | ✓ |
| 4 | ANNOVAR: functional annotation of genetic variants from high-t... | 2010 | Nucleic Acids Research | 15.0K | ✓ |
| 5 | A method and server for predicting damaging missense mutations | 2010 | Nature Methods | 13.3K | ✓ |
| 6 | A framework for variation discovery and genotyping using next-... | 2011 | Nature Genetics | 12.0K | ✓ |
| 7 | Analysis of protein-coding genetic variation in 60,706 humans | 2016 | Nature | 10.1K | ✓ |
| 8 | The Genotype-Tissue Expression (GTEx) project. | 2013 | PubMed | 9.6K | ✓ |
| 9 | The mutational constraint spectrum quantified from variation i... | 2020 | Nature | 9.5K | ✓ |
| 10 | Finding the missing heritability of complex diseases | 2009 | Nature | 8.4K | ✕ |
Frequently Asked Questions
What are the ACMG guidelines for variant interpretation?
The ACMG guidelines, detailed in "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" by Richards et al. (2015), provide a framework combining population data, computational predictions, functional studies, and segregation evidence to classify variants as pathogenic, likely pathogenic, uncertain, likely benign, or benign. These standards are applied in clinical genomics for Mendelian disorders. The guidelines have been cited 30,258 times.
How does ANNOVAR annotate genetic variants?
ANNOVAR, developed by Wang et al. (2010), annotates single nucleotide variants from high-throughput sequencing with functional information such as gene impacts, allele frequencies, and conservation scores. It addresses challenges in pinpointing functionally important variants among massive genomic data. The tool supports clinical genomics and rare disease studies with 14,994 citations.
What is the role of gnomAD in rare disease genomics?
gnomAD, from "Analysis of protein-coding genetic variation in 60,706 humans" by Lek et al. (2016), aggregates exome and genome data to provide allele frequency benchmarks, helping identify rare variants causative for Mendelian disorders. It distinguishes disease-causing mutations from common variants. The resource has 10,122 citations and informs pathogenicity assessments.
What do SIFT predictions indicate about missense mutations?
SIFT, described in "A method and server for predicting damaging missense mutations" by Adzhubei et al. (2010), predicts whether amino acid substitutions affect protein function based on sequence homology and physicochemical properties. Damaging predictions support variant pathogenicity in rare diseases. The method has 13,297 citations and integrates into ACMG frameworks.
How does the 1000 Genomes Project aid variant interpretation?
The 1000 Genomes Project, in "A global reference for human genetic variation" by Auton et al. (2015), catalogs common and rare variants across global populations, providing frequency data for clinical interpretation of rare disease variants. It enables filtering of non-pathogenic polymorphisms. The reference has 19,035 citations.
What is the current state of variant databases in clinical genomics?
Databases like gnomAD and GTEx, from Lek et al. (2016) and Lonsdale (2013), quantify genetic variation and expression constraints, supporting pathogenicity prediction under ACMG guidelines. They aggregate data from tens of thousands of individuals for rare disease diagnostics. These resources underpin ongoing sequence interpretation standards.
Open Research Questions
- ? How can integration of multi-omic data improve accuracy in pathogenicity prediction for ultra-rare variants?
- ? What methods best resolve variants of uncertain significance in Mendelian disorders with incomplete penetrance?
- ? How do population-specific allele frequencies refine variant interpretation across diverse ancestries?
- ? What functional assays are needed to validate computational predictions for non-coding variants in rare diseases?
- ? How can real-time updates to variant databases address the lag in rare disease diagnosis?
Recent Trends
The field maintains 59,145 papers with no specified five-year growth rate, reflecting sustained focus on ACMG guidelines from Richards et al. and resources like gnomAD from Lek et al. (2016).
2015Karczewski et al. advanced constraint metrics across 141,456 humans, building on prior exome analyses in 60,706 individuals.
2020No recent preprints or news coverage in the last 12 months reported.
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