Ivan Iossifov

Genome-wide genotyping is widely used in human genetics research, and targeted sequencing-based approaches such as the Twist Bioscience genome-wide SNP capture platform (GxS) have emerged as alternatives to conventional SNP arrays. Here, we evaluated GxS genotype calls from 555 individuals in 184 nuclear families against matched whole-genome sequencing (WGS) calls and compared platform performance with that of the Illumina Infinium Global Screening Array-24 (GSA), which was evaluated in 987 individuals from 279 nuclear families. Genotype data were harmonized across platforms, and analyses were restricted to overlapping SNP loci. Across all callable positions, mean per-SNP call rates were 98.26% for GxS and 98.67% for GSA. Overall SNP concordance with WGS was 99.79% for GxS and 99.87% for GSA, and mean per-individual concordance was also 99.79% and 99.87%, respectively. Per-trio Mendelian violation rates of GxS are about 10 times those of WGS, while those of GSA are about 4 times those of WGS on average. These results indicate that GxS performs slightly worse than GSA by key concordance and inheritance metrics, while still showing strong overall agreement with WGS.

Interpretation of genomic variants, positions, and regions depends on reliable annotation—adding evidence such as predicted effect, conservation, population frequency, and gene-level context—yet the underlying resources are numerous, versioned, and assembly-specific. We present the Genomic Annotation Infrastructure (GAIn), a platform that generates transparent, reproducible annotations via declarative pipelines that define annotation tasks as ordered lists of components, called annotators, that produce annotation attributes using genomic resources from Genomic Resource Repositories (GRRs). We provide two public GRRs: a main repository containing more than 250 heterogeneous genomic resources, and a separate GRR-ENCODE repository containing resources derived from thousands of ENCODE (Encyclopedia of DNA Elements) project experiments. Users can use the annotation pipelines we made available, author custom annotation pipelines, and execute annotation tasks with these pipelines via GAIn’s web and command-line interfaces. The web interface can be used without any setup, but it relies on shared computational infrastructure and imposes limits on the size of annotation tasks. The command-line interface requires setup but supports arbitrarily large annotation tasks through simple-to-use parallelization and offers a broader set of features. For example, command-line GAIn can be extended by using custom GRRs or creating custom annotators via its plugin architecture. In addition, GAIn’s re-annotation feature, which updates annotations as they evolve, substantially simplifies maintaining annotations in a large genomics analysis project. GAIn’s resource management, explicit versioning, and pipeline abstraction provide an auditable, maintainable, and efficient foundation for modern genomic annotation across reference assemblies and use cases.

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale data sets requires specialized computational tools to organize and analyze the extensive data. Genotypes and Phenotypes in Families (GPF) is an open-source platform that manages genotypes and phenotypes derived from collections of families. GPF allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, phenotype/genotype association tools, and secure data sharing. GPF is used to disseminate two family collection data sets, SSC and SPARK, for the study of autism, built by the Simons Foundation. The GPF instance at the Simons Foundation (GPF-SFARI) provides protected access to comprehensive genotypic and phenotypic data for SSC and SPARK. GPF-SFARI also provides public access to an extensive collection of de novo mutations from individuals with autism and related disorders and to gene-level statistics of the protected data sets characterizing the genes’ roles in autism. However, GPF is versatile and can manage genotypic data from other small or large family collections. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform ( https://github.com/iossifovlab/gpf ) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance ( https://gpf.sfari.org/ ) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes’ roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

Snakemake is one of the most popular workflow management systems, particularly in biological sciences. Snakemake workflows are highly portable, scalable, and transparent. Moreover, they enable the painless reproduction of published results and adaption to similar data processing and analysis projects. Here we present Snakeobjects, an extension of Snakemake that considerably simplifies the development of workflows and improves their readability and adaptability while preserving all the benefits of Snakemake that ensure the large and growing community of its users.

Autism arises in high and low-risk families. De novo mutation contributes to autism incidence in low-risk families as there is a higher incidence in the affected of the simplex families than in their unaffected siblings. But the extent of contribution in low-risk families cannot be determined solely from simplex families as they are a mixture of low and high-risk. The rate of de novo mutation in nearly pure populations of high-risk families, the multiplex families, has not previously been rigorously determined. Moreover, rates of de novo mutation have been underestimated from studies based on low resolution microarrays and whole exome sequencing. Here we report on findings from whole genome sequence (WGS) of both simplex families from the Simons Simplex Collection (SSC) and multiplex families from the Autism Genetic Resource Exchange (AGRE). After removing the multiplex samples with excessive cell-line genetic drift, we find that the contribution of de novo mutation in multiplex is significantly smaller than the contribution in simplex. We use WGS to provide high resolution CNV profiles and to analyze more than coding regions, and revise upward the rate in simplex autism due to an excess of de novo events targeting introns. Based on this study, we now estimate that de novo events contribute to 52–67% of cases of autism arising from low risk families, and 30–39% of cases of all autism.

Copy number profiling and whole-exome sequencing has allowed us to make remarkable progress in our understanding of the genetics of autism over the past ten years, but there are major aspects of the genetics that are unresolved. Through whole-genome sequencing, additional types of genetic variants can be observed. These variants are abundant and to know which are functional is challenging. We have analyzed whole-genome sequencing data from 510 of the Simons Simplex Collections quad families and focused our attention on intronic variants. Within the introns of 546 high-quality autism target genes, we identified 63 de novo indels in the affected and only 37 in the unaffected siblings. The difference of 26 events is significantly larger than expected (p-val = 0.01) and using reasonable extrapolation shows that de novo intronic indels can contribute to at least 10% of simplex autism. The significance increases if we restrict to the half of the autism targets that are intolerant to damaging variants in the normal human population, which half we expect to be even more enriched for autism genes. For these 273 targets we observe 43 and 20 events in affected and unaffected siblings, respectively (p-value of 0.005). There was no significant signal in the number of de novo intronic indels in any of the control sets of genes analyzed. We see no signal from de novo substitutions in the introns of target genes.