Next Generation Mendelian Genetics: Familial Combined Hyperlipidemia
Ontology highlight
ABSTRACT: The NHGRI Next Generation Mendelian Genetics project uses exome resequencing to identify variants in unsolved Mendelian diseases. The research study procedures included the collection of a fasting blood sample, 2 medical history/life style questionnaires and a measurement of subjects' waist circumference that were completed many years ago. Blood samples were analyzed for metabolic markers including cholesterol, HDL, triglycerides and APOE. Genetic tests targeted known and novel genes and polymorphisms associated with carotid artery disease.
PROVIDER: phs000538.v1.p1 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:<p>The NHGRI Next Generation Mendelian Genetics project uses exome resequencing to identify variants in unsolved Mendelian diseases.</p> <p>The research study procedures included the collection of a fasting blood sample, 2 medical history/life style questionnaires and a measurement of subjects' waist circumference that were completed many years ago. Blood samples were analyzed for metabolic markers including cholesterol, HDL, triglycerides and APOE. Genetic tests targeted known and novel genes and polymorphisms associated with carotid artery disease. </p>
| phs000538 | dbGaP
Project description:NHGRI Mendelian Disorders Sequencing Centers
| PRJNA158501 | ENA
Project description:According to Mendel's laws, each parent makes an equal genetic contribution to an offspring in sexually reproducing organisms. The bipolar mitotic spindle controls the equal segregation of paternal and maternal chromosomes during the first cell division. By overexpression of a single protein, GPR-1, in the maternal strain we changed the structure of the mitotic spindle from bipolar to two monopolar spindles to segregate maternal and paternal chromosomes into different cell lineages, resulting in non-mendelian segregation for entire genomes. To follow maternal and paternal segregation of the chromosomes we used red and green histone markers respectively. By mating gpr-1-overexpressing hermaphrodites with wild-type males, mendelian F1 worms that express both markers simultaneously in all tissues and non-mendelian F1 worms that express red and green markers in different tissues will be produced representing embryos with bipolar and embryos with two monopolar spindles. Thus, we show that the rules of genetic inheritance can be changed, which may inspire the formation of a new field of synthetic zoology. Transcriptional profiling was done to investigate the differences in gene expression between mendelian and non-mendelian offspring. Approximately 60 adult worms were used per sample. Four conditions were collected: hermaphrodites of the paternal strain, hermaphrodites of the maternal strain, co-segregating (mendelian) F1 after crossing of parental strains, and (non-mendelian) F1 that segregates the paternal genotype to body wall muscle, intestine + germline and the maternal genotype to the nervous system after crossing of parental strains.
2015-11-11 | E-GEOD-74848 | biostudies-arrayexpress
Project description:<p>The NHGRI Next Generation Mendelian Genetics project uses exome resequencing to identify variants in unsolved Mendelian diseases.</p> <p>Samples were collected from a single, multi-generational family with the same phenotype of exaggerated muscular development (muscular hypertrophy) and strength characterized by reduced fat pad thickness under the skin. All family members deny "body building" activities, and are so far negative for known gene mutation that have been identified as associated with excessive muscle development. All family members have examples of demonstrating extraordinary strength occurring both in childhood and old age. No negative associated phenotype traits with the muscle hypertrophy phenotype have been identified.</p>
| phs000541.v1.p1 | EGA
Project description:According to Mendel's laws, each parent makes an equal genetic contribution to an offspring in sexually reproducing organisms. The bipolar mitotic spindle controls the equal segregation of paternal and maternal chromosomes during the first cell division. By overexpression of a single protein, GPR-1, in the maternal strain we changed the structure of the mitotic spindle from bipolar to two monopolar spindles to segregate maternal and paternal chromosomes into different cell lineages, resulting in non-mendelian segregation for entire genomes. To follow maternal and paternal segregation of the chromosomes we used red and green histone markers respectively. By mating gpr-1-overexpressing hermaphrodites with wild-type males, mendelian F1 worms that express both markers simultaneously in all tissues and non-mendelian F1 worms that express red and green markers in different tissues will be produced representing embryos with bipolar and embryos with two monopolar spindles. Thus, we show that the rules of genetic inheritance can be changed, which may inspire the formation of a new field of synthetic zoology. Transcriptional profiling was done to investigate the differences in gene expression between mendelian and non-mendelian offspring.
2015-11-11 | GSE74848 | GEO
Project description:<p>The NHGRI Next Generation Mendelian Genetics project uses exome resequencing to identify variants in unsolved Mendelian diseases.</p> <p>Ehlers-Danlos syndrome Type VIII is a dominantly inherited connective tissue disorder that is distinguished from other forms of EDS by significant early-onset periodontal disease. Although the clinical phenotype is well delineated, the underlying molecular basis remains unknown. By studying a large family of affected and unaffected individuals with the EDS VIII by exome sequencing, we hope to identify unique regions of homology to assist in identifying the causative gene. </p>
| phs000540.v1.p1 | EGA
Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.
2024-08-06 | GSE254084 | GEO
Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.
2024-08-06 | GSE254083 | GEO
Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.
2024-08-06 | GSE254086 | GEO
Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.
2024-08-06 | GSE254085 | GEO