CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data.

MOTIVATION: Several algorithms exist for detecting copy number variants (CNVs) from human exome sequencing read depth, but previous tools have not been well suited for large population studies on the order of tens or hundreds of thousands of exomes. Their limitations include being difficult to integrate into automated variant-calling pipelines and being ill-suited for detecting common variants. To address these issues, we developed a new algorithm--Copy number estimation using Lattice-Aligned Mixture Models (CLAMMS)--which is highly scalable and suitable for detecting CNVs across the whole allele frequency spectrum. RESULTS: In this note, we summarize the methods and intended use-case of CLAMMS, compare it to previous algorithms and briefly describe results of validation experiments. We evaluate the adherence of CNV calls from CLAMMS and four other algorithms to Mendelian inheritance patterns on a pedigree; we compare calls from CLAMMS and other algorithms to calls from SNP genotyping arrays for a set of 3164 samples; and we use TaqMan quantitative polymerase chain reaction to validate CNVs predicted by CLAMMS at 39 loci (95% of rare variants validate; across 19 common variant loci, the mean precision and recall are 99% and 94%, respectively). In the Supplementary Materials (available at the CLAMMS Github repository), we present our methods and validation results in greater detail. AVAILABILITY AND IMPLEMENTATION: https://github.com/rgcgithub/clamms (implemented in C). CONTACT: jeffrey.reid@regeneron.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conditionals by inversion provide a universal method for the generation of conditional alleles.

Conditional mutagenesis is becoming a method of choice for studying gene function, but constructing conditional alleles is often laborious, limited by target gene structure, and at times, prone to incomplete conditional ablation. To address these issues, we developed a technology termed conditionals by inversion (COIN). Before activation, COINs contain an inverted module (COIN module) that lies inertly within the antisense strand of a resident gene. When inverted into the sense strand by a site-specific recombinase, the COIN module causes termination of the target gene's transcription and simultaneously provides a reporter for tracking this event. COIN modules can be inserted into natural introns (intronic COINs) or directly into coding exons as part of an artificial intron (exonic COINs), greatly simplifying allele design and increasing flexibility over previous conditional KO approaches. Detailed analysis of over 20 COIN alleles establishes the reliability of the method and its broad applicability to any gene, regardless of exon-intron structure. Our extensive testing provides rules that help ensure success of this approach and also explains why other currently available conditional approaches often fail to function optimally. Finally, the ability to split exons using the COIN's artificial intron opens up engineering modalities for the generation of multifunctional alleles.

High-throughput engineering of the mouse genome coupled with high-resolution expression analysis.

One of the most effective approaches for determining gene function involves engineering mice with mutations or deletions in endogenous genes of interest. Historically, this approach has been limited by the difficulty and time required to generate such mice. We describe the development of a high-throughput and largely automated process, termed VelociGene, that uses targeting vectors based on bacterial artificial chromosomes (BACs). VelociGene permits genetic alteration with nucleotide precision, is not limited by the size of desired deletions, does not depend on isogenicity or on positive-negative selection, and can precisely replace the gene of interest with a reporter that allows for high-resolution localization of target-gene expression. We describe custom genetic alterations for hundreds of genes, corresponding to about 0.5-1.0% of the entire genome. We also provide dozens of informative expression patterns involving cells in the nervous system, immune system, vasculature, skeleton, fat and other tissues.

ACVR1R206H receptor mutation causes fibrodysplasia ossificans progressiva by imparting responsiveness to activin A.

Fibrodysplasia ossificans progressiva (FOP) is a rare genetic disorder characterized by episodically exuberant heterotopic ossification (HO), whereby skeletal muscle is abnormally converted into misplaced, but histologically normal bone. This HO leads to progressive immobility with catastrophic consequences, including death by asphyxiation. FOP results from mutations in the intracellular domain of the type I BMP (bone morphogenetic protein) receptor ACVR1; the most common mutation alters arginine 206 to histidine (ACVR1(R206H)) and has been thought to drive inappropriate bone formation as a result of receptor hyperactivity. We unexpectedly found that this mutation rendered ACVR1 responsive to the activin family of ligands, which generally antagonize BMP signaling through ACVR1 but cannot normally induce bone formation. To test the implications of this finding in vivo, we engineered mice to carry the Acvr1(R206H) mutation. Because mice that constitutively express Acvr1[R206H] die perinatally, we generated a genetically humanized conditional-on knock-in model for this mutation. When Acvr1[R206H] expression was induced, mice developed HO resembling that of FOP; HO could also be triggered by activin A administration in this mouse model of FOP but not in wild-type controls. Finally, HO was blocked by broad-acting BMP blockers, as well as by a fully human antibody specific to activin A. Our results suggest that ACVR1(R206H) causes FOP by gaining responsiveness to the normally antagonistic ligand activin A, demonstrating that this ligand is necessary and sufficient for driving HO in a genetically accurate model of FOP; hence, our human antibody to activin A represents a potential therapeutic approach for FOP.

The loss-of-allele assay for ES cell screening and mouse genotyping.

Targeting vectors used to create directed mutations in mouse embryonic stem (ES) cells consist, in their simplest form, of a gene for drug selection flanked by mouse genomic sequences, the so-called homology arms that promote site-directed homologous recombination between the vector and the target gene. The VelociGene method for the creation of targeted mutations in ES cells employs targeting vectors, called BACVecs, that are based on bacterial artificial chromosomes. Compared with conventional short targeting vectors, BacVecs provide two major advantages: (1) their much larger homology arms promote high targeting efficiencies without the need for isogenicity or negative selection strategies; and (2) they enable deletions and insertions of up to 100kb in a single targeting event, making possible gene-ablating definitive null alleles and other large-scale genomic modifications. Because of their large arm sizes, however, BACVecs do not permit screening by conventional assays, such as long-range PCR or Southern blotting, that link the inserted targeting vector to the targeted locus. To exploit the advantages of BACVecs for gene targeting, we inverted the conventional screening logic in developing the loss-of-allele (LOA) assay, which quantifies the number of copies of the native locus to which the mutation was directed. In a correctly targeted ES cell clone, the LOA assay detects one of the two native alleles (for genes not on the X or Y chromosome), the other allele being disrupted by the targeted modification. We apply the same principle in reverse as a gain-of-allele assay to quantify the copy number of the inserted targeting vector. The LOA assay reveals a correctly targeted clone as having lost one copy of the native target gene and gained one copy of the drug resistance gene or other inserted marker. The combination of these quantitative assays makes LOA genotyping unequivocal and amenable to automated scoring. We use the quantitative polymerase chain reaction (qPCR) as our method of allele quantification, but any method that can reliably distinguish the difference between one and two copies of the target gene can be used to develop an LOA assay. We have designed qPCR LOA assays for deletions, insertions, point mutations, domain swaps, conditional, and humanized alleles and have used the insert assays to quantify the copy number of random insertion BAC transgenics. Because of its quantitative precision, specificity, and compatibility with high throughput robotic operations, the LOA assay eliminates bottlenecks in ES cell screening and mouse genotyping and facilitates maximal speed and throughput for knockout mouse production.