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Goodman, D. B.

Publications and source records attributed to Goodman, D. B..

3 recordsLinked to original sources

Large-scale screening of rare genetic variants in humans reveals frequent splicing disruptions

Any individuals genome contains [~]4-5 million genetic variants that differ from reference, and understanding how these variants give rise to trait diversity and disease susceptibility is a central goal of human genetics1. A vast majority (96-99%) of an individuals variants are common, though at a population level the overwhelming majority of variants are rare2-5. Because of their scarcity in an individuals genome, rare variants that play important roles in complex traits are likely to have large functional effects6,7. Mutations that cause an exon to be skipped can have severe functional consequences on gene function, and many known disease-causing mutations reduce or eliminate exon recognition8. Here we explore the extent to which rare genetic variation in humans results in near complete loss of exon recognition. We developed a Multiplexed Functional Assay of Splicing using Sort-seq (MFASS) that allows us to measure exon inclusion in thousands of human exons and surrounding intronic sequence simultaneously. We assayed 27,733 extant variants in the Exome Aggregation Consortium (ExAC)9 within or adjacent to 2,339 human exons, and found that 3.8% (1,050) of the variants, almost all of which were extremely rare, led to large-effect defects in exon recognition. Importantly, we find that 83% of these splice-disrupting variants (SDVs) are located outside of canonical splice sites, are distributed evenly across distinct exonic and intronic regions, and are difficult to predict a priori. Our results indicate that loss of exon recognition is an important and underappreciated means by which rare variants exert large functional effects, and that MFASS enables their empirical assessment for splicing defects at scale.

genetics

Millstone: Software for Multiplex Microbial Genome Analysis and Engineering

Inexpensive DNA sequencing and advances in genome editing have made computational analysis a major rate-limiting step in adaptive laboratory evolution and microbial genome engineering. We describe Millstone, a web-based platform which automates genotype comparison and visualization for projects with up to hundreds of genomic samples. To enable iterative genome engineering, Millstone allows users to design oligonucleotide libraries and create successive versions of reference genomes. Millstone is open source and easily deployable to a cloud platform, local cluster, or desktop, making it a scalable solution for any lab.

synthetic biology

Optimizing complex phenotypes through model-guided multiplex genome engineering

Optimization of complex phenotypes in engineered microbial strains has traditionally been accomplished by laboratory evolution. However, only a subset of the resulting mutations may affect the phenotype of interest and many others may have unintended effects. Multiplexed genome editing can complement evolutionary approaches by creating diverse combinations of targeted changes, but in both cases it remains challenging to identify which alleles influence the desired phenotype. We present a method for identifying a minimal set of genomic modifications that optimizes a complex phenotype by combining iterative cycles of multiplex genome engineering and predictive modeling. We applied our method to the 63-codon E. coli strain C321.{Delta}A, which has 676 mutations relative to its wild-type ancestor, and identified six single nucleotide mutations that together recover 59% of the fitness defect exhibited by the strain. The resulting optimized strain, C321.DA.opt, is an improved chassis for production of proteins containing non-standard amino acids. Our data reveal how multiple cycles of multiplex automated genome engineering (MAGE) and inexpensive sequencing can generate rich genotypic and phenotypic diversity that can be combined with linear regression techniques to quantify individual allelic effects. While laboratory evolution relies on enrichment as a proxy for allelic effect, our model-guided approach is less susceptible than enrichment to bias from population dynamics and recombination efficiency. We also show that the method can identify beneficial de novo mutations that arise adventitiously. Beyond improving the fitness of C321, {Delta}A, our work provides a proof-of-principle for high-throughput quantification of individual allelic effects which can be used with any method for generating targeted genotypic diversity.

synthetic biology