Estimation and visualization of identity-by-descent within pedigrees simplifies interpretation of complex trait analysis

Association of HLA-DRB1 and -DQB1 alleles with type 1 (autoimmune) diabetes in African Arabs: systematic review and meta-analysis

Several studies confirmed the association of HLA-DRB1 and -DQB1 alleles with altered risk of type 1 diabetes (T1D). However, data from individual studies based on small sample sizes yielded often conflicting findings in African Arabs. This is a systematic review and meta-analysis aimed at comprehensively evaluating this association with T1D, using molecular HLA data. Relevant studies were identified through systemic search of Medline/PubMed, Cochrane, Science Direct, ResearchGate, and EMBASE databases. Statistical analysis was carried out using RevMan, and Comprehensive Meta-analysis programs. Given the heterogeneity of African Arabs, we also performed subgroup analysis according to ethnicity. Analysis of sensitivity, heterogeneity, and publication bias were performed to validate the outcome of the findings. This meta-analysis included 862 T1DM cases, along with 1,390 normoglycemic control, and comprised ten comparisons. Our study indicates that DRB1*03 (OR = 2.86), DRB1*04 (OR = 2.78), and DQB1*02 (OR = 2.29), are positively associated with increased risk of T1DM, while DRB1*07 (OR = 0.48), DRB1*11 (OR = 0.20), DRB1*13 (OR = 0.47), DRB1*15 (OR = 0.30), DQB1*05 (OR = 0.39), and DQB1*06 (OR = 0.27) were negatively associated with T1D, suggesting a protective role against T1D. This meta-analysis was characterized by low heterogeneity, sensitivity, and publication bias, indicating the robustness and reliability of the results.

BACKGROUND

Several studies confirmed the association of HLA-DRB1 and -DQB1 alleles with altered risk of type 1 diabetes (T1D). However, data from individual studies based on small sample sizes yielded often conflicting findings in African Arabs. This is a systematic review and meta-analysis aimed at comprehensively evaluating this association with T1D, using molecular HLA data.

METHODS

Relevant studies were identified through systemic search of Medline/PubMed, Cochrane, Science Direct, ResearchGate, and EMBASE databases. Statistical analysis was carried out using Revman, and Comprehensive Meta-analysis programs. Given the heterogeneity of African Arabs, we also performed subgroup analysis according to ethnicity. Analysis of sensitivity, heterogeneity, and pub¬lication bias were performed to validate the outcome of the findings. This meta-analysis included 862 T1DM cases, along with 1,390 normoglycemic control, and comprised ten comparisons.

RESULTS

Our study indicates that DRB1*03 (OR = 2.86), DRB1*04 (OR = 2.78), and DQB1*02 (OR = 2.29), are positively associated with increased risk of T1DM, while DRB1*07 (OR = 0.48), DRB1*11 (OR = 0.20), DRB1*13 (OR = 0.47), DRB1*15 (OR = 0.30), DQB1*05 (OR = 0.39), and DQB1*06 (OR = 0.27) were negatively associated with T1D, suggesting a protective role against T1D.

CONCLUSION

This meta-analysis was characterized by low heterogeneity, sensitivity, and publication bias, indicating the robustness and reliability of the results.

Visualization of haplotype sharing patterns in pedigree samples

OBJECTIVES

A particular approach to the visualization of descent of founder DNA copies in a pedigree has been suggested, which helps to understand haplotype sharing patterns among subjects of interest. However, the approach does not provide the information in an ideal format to show haplotype sharing patterns. Therefore, we aimed to find an efficient way to visualize such sharing patterns and to demonstrate that our tool provides useful information for finding an informative subset of subjects for a sequence study.

METHODS

The visualization package, SharedHap, computes and visualizes a novel metric, the SharedHap proportion, which quantifies haplotype sharing among a set of subjects of interest. We applied SharedHap to simulated and real pedigree datasets to illustrate the approach.

RESULTS

SharedHap successfully represents haplotype sharing patterns that contribute to linkage signals in both simulated and real datasets. Using the visualizations we were also able to find ideal sets of subjects for sequencing studies.

CONCLUSIONS

Our novel metric that can be computed using the SharedHap package provides useful information about haplotype sharing patterns among subjects of interest. The visualization of the SharedHap proportion provides useful information in pedigree studies, allowing for a better selection of candidate subjects for use in further sequencing studies.

Identity-by-descent graphs offer a flexible framework for imputation and both linkage and association analyses

We demonstrate the flexibility of identity-by-descent (IBD) graphs for genotype imputation and testing relationships between genotype and phenotype. We analyzed chromosome 3 and the first replicate of simulated diastolic blood pressure. IBD graphs were obtained from complete pedigrees and full multipoint marker analysis, facilitating subsequent linkage and other analyses. For rare alleles, pedigree-based imputation using these IBD graphs had a higher call rate than did population-based imputation. Combining the two approaches improved call rates for common alleles. We found it advantageous to incorporate known, rather than estimated, pedigree relationships when testing for association. Replacing missing data with imputed alleles improved association signals as well. Analyses were performed with knowledge of the underlying model.

Efficient identification of equivalences in dynamic graphs and pedigree structures

We propose a new framework for designing test and query functions for complex structures that vary across a given parameter such as genetic marker position. The operations we are interested in include equality testing, set operations, isolating unique states, duplication counting, or finding equivalence classes under identifiability constraints. A motivating application is locating equivalence classes in identity-by-descent (IBD) graphs, which are graph structures in pedigree analysis that change over genetic marker location. The nodes of these graphs are unlabeled and identified only by their connecting edges, a constraint easily handled by our approach. The general framework introduced is powerful enough to build a range of testing functions for IBD graphs, dynamic populations, and other structures using a minimal set of operations. The theoretical and algorithmic properties of our approach are analyzed and proved. Computational results on several simulations demonstrate the effectiveness of our approach.

Identification of rare variants from exome sequence in a large pedigree with autism

We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.

The role of large pedigrees in an era of high-throughput sequencing

Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.