Michael E. Zwick

Bio: Michael E. Zwick is an academic researcher from Emory University. The author has contributed to research in topics: Population & Exome sequencing. The author has an hindex of 30, co-authored 97 publications receiving 6094 citations. Previous affiliations of Michael E. Zwick include Johns Hopkins University & University of California, Davis.

Synaptic, transcriptional and chromatin genes disrupted in autism

Abstract: The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.

Microarray-based genomic selection for high-throughput resequencing.

Abstract: We developed a general method, microarray-based genomic selection (MGS), capable of selecting and enriching targeted sequences from complex eukaryotic genomes without the repeat blocking steps necessary for bacterial artificial chromosome (BAC)-based genomic selection. We demonstrate that large human genomic regions, on the order of hundreds of kilobases, can be enriched and resequenced with resequencing arrays. MGS, when combined with a next-generation resequencing technology, can enable large-scale resequencing in single-investigator laboratories.

High-Throughput Variation Detection and Genotyping Using Microarrays

Abstract: The central goal of human genetics is to identify, characterize and ultimately understand the specific DNA variants that contribute to human phenotypes in general, and human disease in particular (Lander and Schork 1994; Chakravarti 1999; Zwick et al. 2000, 2001; On-line Mendelian Inheritance in Man 2001). The genetic approach to this problem is, in principle, straightforward. First, we identify individuals showing phenotypic variation for the trait of interest. Second, we genotype genetic variants, such as microsatellites or SNPs, in all of the individuals in a study. Third, we perform appropriate statistical tests to identify any genetic variants correlated with variation in the phenotype. Finally, if such variants are found, we perform additional experiments to demonstrate a causal relationship. Step two poses a question: What genetic variants should be examined? The answer to this question must balance technological and practical considerations. Nevertheless, in the best of all worlds, a researcher would be able to determine the genotype of every base in every sample, that is, a complete resequencing of the entire genome of all individuals under study. No technology currently exists to do this in an economical manner. Moreover, any technology used for this purpose must be capable of extraordinary resequencing accuracy. Nucleotide diversity in the general human population is ∼8 × 10−4 per site (Cargill et al. 1999; Halushka et al. 1999; The International SNP Map Working [TISMW] Group 2001; Venter et al. 2001; this study). This implies that a randomly selected chromosome will differ from the human reference sequence at ∼8 of every 10,000 bases. Now, imagine a technology that allowed one to rapidly and inexpensively determine the genotype of an individual at every nucleotide site of interest with an accuracy of 99.9%. Such a technology would be remarkable, but insufficient. The problem with only 99.9% accuracy is that this implies 10 errors for every 10,000 bases. Because the true rate of variation is eight in 10,000, 55.5% of the identified variants will be errors. This is unacceptably high. The error rate needs to be much lower. Microarrays are inherently parallel devices that offer the promise of determining the genotypes of individuals at every site of interest with a limited level of effort (Fodor et al. 1991; Southern et al. 1992; Pease et al. 1994; McGall et al. 1996; Lipshutz et al. 1999). Variation Detection Arrays (VDAs) manufactured by Affymetrix have been used to such an end with success (Chee et al. 1996; Hacia et al. 1996, 1998a,b, 1999, 2000; Hacia and Collins 1999; Halushka et al. 1999; Wang et al. 1998). Unfortunately, it has also been reported that between 12% and 45% of the detected variants are false (Cargill et al. 1999; Halushka et al. 1999; Wang et al. 1998). This indicates that VDAs are, on average, between 99.99% and 99.93% accurate. Although microarrays may be, on average, insufficiently accurate, it is certainly possible that a large fraction of genotype calls are, in fact, much more accurate than 99.9% and a smaller fraction are much less than 99.9% accurate. The approach used here is to construct an objective statistical framework to distinguish genotype calls that can be made with extraordinary accuracy from those less reliable. The need to build such a framework for microarrays is not a new idea (Southern et al. 1992) and the objectives are to strive for some of the accomplishments that Green and colleagues (Nickerson et al. 1997; Ewing and Green 1998; Ewing et al. 1998; Gordon et al. 1998; Rieder et al. 1998) have made for automated sequencing, namely the assignment to individual genotype calls of a quality score that is larger for calls more likely to be accurate. Green and colleagues, in fact, have done even more; phred provides not only a quality score that increases with increasing accuracy, but also a direct estimate of the probability that a base call is correct. Researchers performing automated sequencing routinely rely on these phred scores (Ewing and Green 1998; Ewing et al. 1998), and in conjunction with certain other neighborhood quality rules (Altshuler et al. 2000; Mullikin et al. 2000), can achieve an extremely high level of accuracy for SNP discovery (T.I.S.M.W. Group 2001). This work attempts the same task. An objective statistical framework is developed to assign to each VDA genotype call a quality score. Certain simple neighborhood rules are applied, and sites in which extraordinarily high confidence can be placed are distinguished from those less reliable sites. In contrast to automated sequencing experiments that employ only haploid targets (Altshuler et al. 2000; Mullikin et al. 2000), this statistical method can be applied to both haploid and diploid targets. We call the system ABACUS (from Adaptive Background genotype Calling Scheme, see below) and will show that, in general, greater than 99.9999% accuracy can be achieved on >80% of the genotype calls on a VDA.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Sequencing technologies-the next generation

Abstract: Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.

A haplotype map of the human genome

Abstract: 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.