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Genome engineering methodologies are transforming biological research and discovery. Approaches based on CRISPR technology have been broadly adopted and there is growing interest in the generation of massively parallel edited cell libraries. Comparing the libraries generated by these varying approaches is challenging and researchers lack a common framework for defining and assessing the characteristics of these libraries. Here we describe a framework for evaluating massively parallel libraries of edited genomes based on established methods for sampling complex populations. We define specific attributes and metrics that are informative for describing a complex cell library and provide examples for estimating these values. We also connect this analysis to generic phenotyping approaches, using either pooled (typically via a selection assay) or isolate (often referred to as screening) phenotyping approaches. We approach this from the context of creating massively parallel, precisely edited libraries with one edit per cell, though the approach holds for other types of modifications, including libraries containing multiple edits per cell (combinatorial editing). This framework is a critical component for evaluating and comparing new technologies as well as understanding how a massively parallel edited cell library will perform in a given phenotyping approach.

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention. © 2003 Nature Publishing Group.

Haplotyping of human chromosomes is a prerequisite for cataloguing the full repertoire of genetic variation. We present a microfluidics-based, linked-read sequencing technology that can phase and haplotype germline and cancer genomes using nanograms of input DNA. This high-throughput platform prepares barcoded libraries for short-read sequencing and computationally reconstructs long-range haplotype and structural variant information. We generate haplotype blocks in a nuclear trio that are concordant with expected inheritance patterns and phase a set of structural variants. We also resolve the structure of the EML4-ALK gene fusion in the NCI-H2228 cancer cell line using phased exome sequencing. Finally, we assign genetic aberrations to specific megabase-scale haplotypes generated from whole-genome sequencing of a primary colorectal adenocarcinoma. This approach resolves haplotype information using up to 100 times less genomic DNA than some methods and enables the accurate detection of structural variants.

We present single-molecule, real-time sequencing data obtained from a DNA polymerase performing uninterrupted template-directed synthesis using four distinguishable fluorescently labeled deoxyribonucleoside triphosphates (dNTPs). We detected the temporal order of their enzymatic incorporation into a growing DNA strand with zero-mode waveguide nanostructure arrays, which provide optical observation volume confinement and enable parallel, simultaneous detection of thousands of single-molecule sequencing reactions. Conjugation of fluorophores to the terminal phosphate moiety of the dNTPs allows continuous observation of DNA synthesis over thousands of bases without steric hindrance. The data report directly on polymerase dynamics, revealing distinct polymerization states and pause sites corresponding to DNA secondary structure. Sequence data were aligned with the known reference sequence to assay biophysical parameters of polymerization for each template position. Consensus sequences were generated from the single-molecule reads at 15-fold coverage, showing a median accuracy of 99.3%, with no systematic error beyond fluorophore-dependent error rates.

Single nucleotide polymorphisms (SNPs) that lead to non-synonymous changes in proteins may have functional effects and be subject to selection. Hence they are of particular interest in the study of genetic diseases. We have genotyped approximately 28,000 such SNPs in three ethnic populations (the HapMap plates) and ten primate species and analyzed these data for evidence of selection. We find SNPs predicted by PolyPhen to be damaging, have lower allele frequencies, and are particularly likely to be population-specific. We have also grouped SNPs by molecular function or biological process of the associated genes and find evidence that selection may be acting in concert on classes of genes.

Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution. © 2005 Nature Publishing Group., link_to_OA_fulltext

The combinatorial method restriction endonuclease protection, selection, and amplification (REPSA) was used to determine the preferred duplex DNA binding sites of the peptide N-methylpyrrolecarboxamide antibiotic distamycin A. After 12 rounds of REPSA, several sequences were identified that bound distamycin with an apparent affinity of 2-20 nM. Among these, the highest-affinity sites averaged 10 bp in length, suggesting that these sites may be occupied by multiple, cooperatively interacting distamycin molecules. Presently, REPSA is the only combinatorial approach that allows the identification of preferred DNA targets for small molecule ligands at physiologically relevant concentrations in solution. As such, it should prove useful in the design and screening of sequence-specific DNA-binding molecules.

We have devised a combinatorial method, restriction endonuclease protection selection and amplification (REPSA), to identify consensus ligand binding sequences in DNA. In this technique, cleavage by a type IIS restriction endonuclease (an enzyme that cleaves DNA at a site distal from its recognition sequence) is prevented by a bound ligand while unbound DNA is cleaved. Since the selection step of REPSA is performed in solution under mild conditions, this approach is amenable to the investigation of ligand-DNA complexes that are either insufficiently stable or not readily separable by other methods. Here we report the use of REPSA to identify the consensus duplex DNA sequence recognized by a G/T-rich oligodeoxyribonucleotide under conditions favoring purine-motif triple-helix formation. Analysis of 47 sequences indicated that recognition between 13 bases on the oligonucleotide 3' end and the duplex DNA was sufficient for triplex formation and indicated the possible existence of a new base triplet, G.AT. This information should help identify appropriate target sequences for purine-motif triplex formation and demonstrates the power of REPSA for investigating ligand-DNA interactions.

Background: Drug metabolism is a multistep process by which the body disposes of xenobiotic agents such as therapeutic drugs. Genetic variation in the enzymes involved in this process can lead to variability in a patient’s response to medication. Methods: We used molecular-inversion probe technology to develop a multiplex genotyping assay that can simultaneously test for 1227 genetic variants in 169 genes involved in drug metabolism, excretion, and transport. Within this larger set of variants, we performed analytical validation of a clinically defined core set of 165 variants in 27 genes to assess accuracy, imprecision, and dynamic range. Results: In a test set of 91 samples, genotyping accuracy for the core set probes was 99.8% for called genotypes, with a 1.2% no-call (NC) rate. The majority of the core set probes (133 of 165) had ≤1 genotyping failure in the test set; a subset of 12 probes was responsible for the majority of failures (mainly NC). Genotyping results were reproducible upon repeat testing with overall within- and between-run variation of 1.1% and 1.4%, respectively—again, primarily NCs in a subset of probes. The assay showed stable genotyping results over a 6-fold range of input DNA. Conclusions: This assay generates a comprehensive assessment of a patient’s metabolic genotype and is a tool that can provide a more thorough understanding of patient-to-patient variability in pharmacokinetic responses to drugs.

The combined effects of multiple polymorphisms in several drug-metabolizing enzyme and transporter genes can contribute to considerable interindividual variation in drug disposition and response. Therefore, it has been of increasing interest to generate scalable, flexible and cost-effective technologies for large-scale genotyping of the drug-metabolizing enzyme and transporter genes. However, the number of drug-metabolizing enzyme and transporter gene variants exceeds the capacity of current technologies to comprehensively assess multiple polymorphisms in a single, multiplexed assay. The Targeted Genotyping System (Affymetrix®, CA, USA) provides a solution to this challenge, by combining molecular inversion probe technology with universal microarrays to provide a method that is capable of analyzing thousands of variants in a single reaction, while remaining relatively insensitive to cross-reactivity between reaction components. This review will focus on the Targeted Genotyping System and how this technology was adapted to enable comprehensive analysis of drug-metabolizing enzyme and transporter gene polymorphisms.

High-throughput genotyping technologies that enable large association studies are already available. Tools for genotype determination starting from raw signal intensities need to be automated, robust, and flexible to provide optimal genotype determination given the specific requirements of a study. The key metrics describing the performance of a custom genotyping study are assay conversion, call rate, and genotype accuracy. These three metrics can be traded off against each other. Using the highly multiplexed Molecular Inversion Probe technology as an example, we describe a methodology for identifying the optimal trade-off. The methodology comprises: a robust clustering algorithm and assessment of a large number of data filter sets. The clustering algorithm allows for automatic genotype determination. Many different sets of filters are then applied to the clustered data, and performance metrics resulting from each filter set are calculated. These performance metrics relate to the power of a study and provide a framework to choose the most suitable filter set to the particular study.

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.

Complex human diseases are known to have a significant genetic component. Despite some important successes (Altshuler et al. 2000; Hugot et al. 2001), the elucidation of the underlying genetic determinants have proven resistant to standard methods. Linkage analysis using affected sib pairs has limited power to uncover such signals, as each individual functional variant contributes only modestly to disease risk (Risch 2000). Large-scale association studies that would allow genome-wide mapping in large collections of cases with matched controls have therefore been proposed (e.g., Kruglyak 1999; Risch 2000). Some of the barriers to adopting such strategies, including the need to establish large-case control populations (Geschwind et al. 2001; Shmulewitz et al. 2001; Cupples et al. 2003) and develop comprehensive SNP resources (dbSNP), have been overcome. In addition, the International HapMap project (The International HapMap Consortium 2003) is completing a first draft of a whole genome haplotype map in the Centre d'Etude du Polymorphisme Humain (CEU) population by the end of 2004. The Hap-Map effort will yield a broad view of the genetic architecture of human populations and allow for the efficient selection of the most informative tagging SNPs for subsequent association studies. The remaining requirement to fully enable large-scale genetic association studies is the development of truly cost-effective and scalable SNP genotyping technologies. These methods must allow hundreds of thousands of markers to be efficiently and accurately scored in thousands of patients. The first generation of SNP genotyping technologies were based on single amplification reactions for each locus and were not appropriate for these largescale, whole-genome studies. Recent advances have assayed thousands of random SNPs using ultra high-density wafer hybridizations (Matsuzaki et al. 2004) but will not allow the advantages of the tagging SNP approach to be fully realized because these technologies will not be able to convert all the informative tagging SNPs and/or putative functional SNPs into working genotyping assays. Multiplexed genotyping technologies that allow the multiplexing of hundreds of targeted SNPs have recently been developed based on various versions of the Oligo Ligation Assay (OLA) (Grossman et al. 1994; Samiotaki et al. 1994; Oliphant et al. 2002). These technologies would still demand a fairly large infrastructure in order to process the thousands of reactions necessary per patient. Here, we describe an advanced Molecular Inversion Probe (MIP) genotyping technology (Hardenbol et al. 2003) that combines the strengths of all of the above methods. MIP exploits the advantages of the OLA methodologies but can be more highly multiplexed due to a unique unimolecular method of action and an enzymology that combines the specificity of both ligase and polymerase enzymes. This assay enables the use of >12,000 oligo probes to simultaneously interrogate human genomic DNA and, following a single PCR, detect the results via a single universal tag DNA chip array. The application of MIP to generate a haplotype map of Chromosome 12 in CEU families (The International HapMap Consortium 2003) as part of the Human HapMap project has yielded 3,509,052 genotypes from 38,429 assays. In this study we analyze the important features of the assay performance that will enable MIP to be effectively used for many future largescale studies.

We report on the development of molecular inversion probe (MIP) genotyping, an efficient technology for large-scale single nucleotide polymorphism (SNP) analysis. This technique uses MIPs to produce inverted sequences, which undergo a unimolecular rearrangement and are then amplified by PCR using common primers and analyzed using universal sequence tag DNA microarrays, resulting in highly specific genotyping. With this technology, multiplex analysis of more than 1,000 probes in a single tube can be done using standard laboratory equipment. Genotypes are generated with a high call rate (95%) and high accuracy (>99%) as determined by independent sequencing.

Here we describe the application of a novel combinatorial method, restriction endonuclease protection selection and amplification (REPSA), to identification of a consensus DNA binding site for the TATA binding subunit (hTBP) of the human general transcription factor TFIID. Unlike most combinatorial methods, REPSA is based on inhibition of an enzymatic template inactivation process by specific ligand‐DNA complexes. The mild conditions of this method allow examination of proteins with atypical binding characteristics (e.g. limited discrimination between specific and non-specific binding sites), such as those found with hTBP. Analysis of 57 emergent sequences identified 47 sequences containing consensus 6 bp TATA elements as previously defined. However, further examination of these sequences indicated that a larger consensus, 54-TATAAATA-34, could be supported by the data. Studies of the binding affinities and transcriptional activities of these four consensus TATA sequences demonstrated that hTBP binding affinity correlated directly with transcriptional activity in vitro and that the TATAAATA sequence was the best among the TATA sequences investigated.

Mithramycin is a DNA-binding antibiotic that has been reported to selectively affect c-myc expression [Snyder, R. C. et al., (1991) Biochemistry30, 4290–4297]. We used in vitro transcription to investigate the specificity of mithramycin action. We found that mithramycin inhibited transcription from the human c-myc P1 and P2 promoters, as well as from a minimal adenovirus-2 major late promoter, with equal efficiencies. Mithramycin also inhibited transcription elongation by creating kinetic blockades to the passage of RNA polymerase II. These data suggest that mithramycin may inhibit transcription non-specifically by affecting general processes such as transcription elongation.

Correction to: European Journal of Human Genetics (2006) 14, 207–215. doi: 10.1038/sj.ejhg.5201528 Since publication of the above paper, the authors have identified an error in one of the equations. In the following equation, published on p 208