MOTIVATION: Understanding kinship relationships is fundamental to genetic research, particularly in the context of genetic linkage studies and population genetics. Pedigree design and analysis are a prerequisite for these investigations. The legacy kinship2 CRAN package has been a cornerstone in this area; however, the need for handling larger and more complex datasets necessitates an updated, flexible, and user-friendly toolset. To address this issue, we present Pedixplorer, a novel Bioconductor package designed to enhance kinship analyses with modern functionality and usability, especially in large multigeneration complex pedigrees with inbreeding loops, which are frequently seen in domestic animal breeding. RESULTS: Pedixplorer builds upon the robust foundation of kinship2, integrating Bioconductor standards and most recent programming practices. Its core component is the S4 Pedigree object, facilitating efficient representation of complex pedigrees. The new functions enable automatic querying, filtering, and trimming of large pedigrees, while the graphical functions have been rewritten for better customization in pedigree visualizations. Additionally, Pedixplorer offers a comprehensive Shiny application, accessible both locally and via a dedicated website, allowing non-R users to easily create, filter, and customize pedigrees. AVAILABILITY AND IMPLEMENTATION: The Pedixplorer package is freely available at: https://www.bioconductor.org/packages/release/bioc/html/Pedixplorer.html with additional documentation at https://louislenezet.github.io/Pedixplorer. A user-friendly web application is available at: https://pedixplorer.univ-rennes.fr.

BACKGROUND: After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total. RESULTS: We analyzed 10,000 exomes from the Alzheimer's Disease Sequencing Project (ADSP) using multiple analytic pipelines consisting of different read aligners and variant calling strategies. We compared variants identified by using two aligners in 50,100, 200, 500, 1000, and 1952 samples; and compared variants identified by adding single-sample genotyping to the default multi-sample joint genotyping in 50,100, 500, 2000, 5000 and 10,000 samples. We found that using a single pipeline missed increasing numbers of high-quality variants correlated with sample sizes. By combining two read aligners and two variant calling strategies, we rescued 30% of pass-QC variants at sample size of 2000, and 56% at 10,000 samples. The rescued variants had higher proportions of low frequency (minor allele frequency [MAF] 1-5%) and rare (MAF < 1%) variants, which are the very type of variants of interest. In 660 Alzheimer's disease cases with earlier onset ages of ≤65, 4 out of 13 (31%) previously-published rare pathogenic and protective mutations in APP, PSEN1, and PSEN2 genes were undetected by the default one-pipeline approach but recovered by the multi-pipeline approach. CONCLUSIONS: Identification of the complete variant set from sequencing data is the prerequisite of genetic association analyses. The current analytic practice of calling genetic variants from sequencing data using a single bioinformatics pipeline is no longer adequate with the increasingly large projects. The number and percentage of quality variants that passed quality filters but are missed by the one-pipeline approach rapidly increased with sample size.