Dataset Information

Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies.

ABSTRACT: Despite genetics being accepted as the primary cause of familial aggregation for most diseases, it is still unclear whether afflicted families are likely to share a single highly penetrant rare variant, many minimally penetrant common variants, or a combination of the two types of variants. We therefore use recent estimates of SNP heritability and the liability threshold model to estimate the proportion of afflicted families likely to carry a rare, causal variant. We then show that Polygenic Risk Scores (PRS) may be useful for identifying families likely to carry such a rare variant and therefore for prioritizing families to include in sequencing studies with that aim. Specifically, we introduce a new statistic that estimates the proportion of individuals carrying causal rare variants based on the family structure, disease pattern, and PRS of genotyped individuals. Finally, we consider data from the MelaNostrum consortium and show that, despite an estimated PRS heritability of only 0.05 for melanoma, families carrying putative causal variants had a statistically significantly lower PRS, supporting the idea that PRS prioritization may be a useful future tool. However, it will be important to evaluate whether the presence of rare mendelian variants are generally associated with the proposed test statistic or lower PRS in future and larger studies.

SUBMITTER: Schlafly A

PROVIDER: S-EPMC6881075 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies.

Schlafly Andrew A Pfeiffer Ruth M RM Nagore Eduardo E Puig Susana S Calista Donato D Ghiorzo Paola P Menin Chiara C Fargnoli Maria Concetta MC Peris Ketty K Song Lei L Zhang Tongwu T Shi Jianxin J Landi Maria Teresa MT Sampson Joshua Neil JN

PLoS genetics 20191115 11

Despite genetics being accepted as the primary cause of familial aggregation for most diseases, it is still unclear whether afflicted families are likely to share a single highly penetrant rare variant, many minimally penetrant common variants, or a combination of the two types of variants. We therefore use recent estimates of SNP heritability and the liability threshold model to estimate the proportion of afflicted families likely to carry a rare, causal variant. We then show that Polygenic Ris ...[more]

PMID: 31730655

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Project description:Purpose of reviewFamilial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.Recent findingsIn recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Project description:ObjectiveCopy number variants (CNVs) contribute to genetic risk and genetic etiology of both rare and common epilepsies. Whereas many studies have explored the role of CNVs in sporadic or severe cases, fewer have been done in familial generalized and focal epilepsies.MethodsWe analyzed exome sequence data from 267 multiplex families and 859 first-degree relative pairs with a diagnosis of genetic generalized epilepsies or nonacquired focal epilepsies to predict CNVs. Validation and segregation studies were performed using an orthogonal method when possible.ResultsWe identified CNVs likely to contribute to epilepsy risk or etiology in the probands of 43 of 1116 (3.9%) families, including known recurrent CNVs (16p13.11 deletion, 15q13.3 deletion, 15q11.2 deletion, 16p11.2 duplication, 1q21.1 duplication, and 5-Mb duplication of 15q11q13). We also identified CNVs affecting monogenic epilepsy genes, including four families with CNVs disrupting the DEPDC5 gene, and a de novo deletion of HNRNPU in one affected individual from a multiplex family. Several large CNVs (>500 kb) of uncertain clinical significance were identified, including a deletion in 18q, a large duplication encompassing the SCN1A gene, and a 15q13.3 duplication (BP4-BP5).SignificanceThe overall CNV landscape in common familial epilepsies is similar to that of sporadic epilepsies, with large recurrent deletions at 15q11, 15q13, and 16p13 contributing in 2.5%-3% of families. CNVs that interrupt known epilepsy genes and rare, large CNVs were also identified. Multiple etiologies were found in a subset of families, emphasizing the importance of genetic testing for multiple affected family members. Rare CNVs found in a single proband remain difficult to interpret and require larger cohorts to confirm their potential role in disease. Overall, our work indicates that CNVs contribute to the complex genetic architecture of familial generalized and focal epilepsies, supporting the role for clinical testing in affected individuals.

Dataset Information

Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies.

Publications

Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies.

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