Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants

Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

The Genetic Landscape of Familial Pulmonary Fibrosis

Rationale and Objectives: Up to 20% of idiopathic interstitial lung disease is familial, referred to as familial pulmonary fibrosis (FPF). An integrated analysis of FPF genetic risk was performed by comprehensively evaluating for genetic rare variants (RVs) in a large cohort of FPF kindreds. Methods: Whole-exome sequencing and/or candidate gene sequencing from affected individuals in 569 FPF kindreds was performed, followed by cosegregation analysis in large kindreds, gene burden analysis, gene-based risk scoring, cell-type enrichment analysis, and coexpression network construction. Measurements and Main Results: It was found that 14.9-23.4% of genetic risk in kindreds could be explained by RVs in genes previously linked to FPF, predominantly telomere-related genes. New candidate genes were identified in a small number of families-including SYDE1, SERPINB8, GPR87, and NETO1-and tools were developed for evaluation and prioritization of RV-containing genes across kindreds. Several pathways were enriched for RV-containing genes in FPF, including focal adhesion and mitochondrial complex I assembly. By combining single-cell transcriptomics with prioritized candidate genes, expression of RV-containing genes was discovered to be enriched in smooth muscle cells, type II alveolar epithelial cells, and endothelial cells. Conclusions: In the most comprehensive FPF genetic study to date, the prevalence of RVs in known FPF-related genes was defined, and new candidate genes and pathways relevant to FPF were identified. However, new RV-containing genes shared across multiple kindreds were not identified, thereby suggesting that heterogeneous genetic variants involving a variety of genes and pathways mediate genetic risk in most FPF kindreds.

Genetic and epigenetic studies in non-syndromic oral clefts

The etiology of non-syndromic oral clefts (NSOFC) is complex with genetics, genomics, epigenetics, and stochastics factors playing a role. Several approaches have been applied to understand the etiology of non-syndromic oral clefts. These include linkage, candidate gene association studies, genome-wide association studies, whole-genome sequencing, copy number variations, and epigenetics. In this review, we shared these approaches, genes, and loci reported in some studies.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Detection of rare disease variants in extended pedigrees using RVS

Summary

Family-based sequencing studies enable researchers to identify highly penetrant genetic variants too rare to be tested in conventional case-control studies, by studying co-segregation of variant and disease phenotypes. When multiple affected subjects in a family are sequenced, the probability that a variant or a set of variants is shared identical-by-descent by some or all affected relatives provides evidence against the null hypothesis of complete absence of linkage and association. The Rare Variant Sharing software package RVS implements a suite of tools to assess association and linkage between rare genetic variants and a dichotomous disease indicator in family pedigrees.

Availability and Implementation

RVS is available as open source software from the Bioconductor webpage at https://bioconductor.org/packages/release/bioc/html/RVS.html.

Supplementary information

Supplementary data are available at Bioinformatics online.