@article {146, title = {Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 06 03}, pages = {965-982}, abstract = {

Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

}, keywords = {Case-Control Studies, Cohort Studies, Epilepsy, Exome, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Phenotype, Whole Exome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.04.009} } @article {85, title = {A brief history of human disease genetics.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 01}, pages = {179-189}, abstract = {

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved~our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

}, keywords = {Animals, Genetic Testing, Genetic Variation, Genomics, Genotype, Humans, Phenotype, Rare Diseases}, issn = {1476-4687}, doi = {10.1038/s41586-019-1879-7}, author = {Claussnitzer, Melina and Cho, Judy H and Collins, Rory and Cox, Nancy J and Dermitzakis, Emmanouil T and Hurles, Matthew E and Kathiresan, Sekar and Kenny, Eimear E and Lindgren, Cecilia M and MacArthur, Daniel G and North, Kathryn N and Plon, Sharon E and Rehm, Heidi L and Risch, Neil and Rotimi, Charles N and Shendure, Jay and Soranzo, Nicole and McCarthy, Mark I} } @article {75, title = {Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.}, journal = {Epilepsia}, volume = {61}, year = {2020}, month = {2020 02}, pages = {249-258}, abstract = {

OBJECTIVE: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.

METHODS: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.

RESULTS: Overall, we identified pathogenic or likely pathogenic variants in 40\% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26\% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25\%).

SIGNIFICANCE: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {128, title = {A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 11}, pages = {e1008802}, abstract = {

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

}, keywords = {Alagille Syndrome, Alleles, Biological Variation, Population, DiGeorge Syndrome, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Male, Marfan Syndrome, Noonan Syndrome, Phenotype, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008802}, author = {Tcheandjieu, Catherine and Aguirre, Matthew and Gustafsson, Stefan and Saha, Priyanka and Potiny, Praneetha and Haendel, Melissa and Ingelsson, Erik and Rivas, Manuel A and Priest, James R} }