Current Protocols in Human Genetics最新文献

Genotype imputation infers missing genotypes in silico using haplotype information from reference samples with genotypes from denser genotyping arrays or sequencing. This approach can confer a number of improvements on genome-wide association studies: it can improve statistical power to detect associations by reducing the number of missing genotypes; it can simplify data harmonization for meta-analyses by improving overlap of genomic variants between differently-genotyped sample sets; and it can increase the overall number and density of genomic variants available for association testing. This article reviews the general concepts behind imputation, describes imputation approaches and methods for various types of genotype data, including family-based data, and identifies web-based resources that can be used in different steps of the imputation process. For practical application, it provides a step-by-step guide to implementation of a two-step imputation process consisting of phasing of the study genotypes and the imputation of reference panel genotypes into the study haplotypes. In addition, this review describes recently developed haplotype reference panel resources and online imputation servers that are capable of remotely and securely implementing an imputation workflow on uploaded genotype array data. © 2019 by John Wiley & Sons, Inc.

Cover: In Rasooly and Patel (https://doi.org/10.1002/cphg.82), the image shows the scatterplot suggests a positive causal relationship of the SNP effects on BMI against the SNP effects on type 2 diabetes. Each point represents a single genetic variant. The horizontal and vertical lines extending from each point represent 95% confidence intervals for the genetic associations. The x-axis displays the estimated genetic associations with the exposure (BMI), and the y-axis displays the estimated genetic associations with the outcome (type 2 diabetes). The color of the lines indicate the type of MR test used (light blue for IVW, dark blue for MR Egger, light green for simple mode, dark green for weighted median, and red for weighted mode).

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Mendelian randomization (MR) is defined as the utilization of genetic variants as instrumental variables to assess the causal relationship between an exposure and an outcome. By leveraging genetic polymorphisms as proxy for an exposure, the causal effect of an exposure on an outcome can be assessed while addressing susceptibility to biases prone to conventional observational studies, including confounding and reverse causation, where the outcome causes the exposure. Analogous to a randomized controlled trial where patients are randomly assigned to subgroups based on different treatments, in an MR analysis, the random allocation of alleles during meiosis from parent to offspring assigns individuals to different subgroups based on genetic variants. Recent methods use summary statistics from genome-wide association studies to perform MR, bypassing the need for individual-level data. Here, we provide a straightforward protocol for using summary-level data to perform MR and provide guidance for utilizing available software. © 2019 by John Wiley & Sons, Inc.

Mapping patterns of DNA methylation throughout the epigenome are critical to our understanding of several important biological and regulatory functions, such as transcriptional regulation, genomic imprinting, and embryonic development. The development and rapid advancement of next-generation sequencing (NGS) technologies have provided clinicians and researchers with accurate and reliable read-outs of genomic and epigenomic information at the nucleotide level. Such improvements have significantly lowered the cost required for genome-wide sequencing, facilitating the vast acquisition of data that has led to many improvements in patient care. However, the torrid rate of NGS data generation has left targeted validation approaches behind, including the confirmation of epigenetic marks such as DNA methylation. To overcome these shortcomings, we present a rapid and robust protocol for the parallel examination of multiple methylated sequences that we have termed simultaneous targeted methylation sequencing (sTM-Seq). Key features of this technique include the elimination of the need for large amounts of high-molecular weight DNA and the nucleotide specific distinction of both 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Moreover, sTM-Seq is scalable and can be used to investigate multiple loci in dozens of samples within a single sequencing run. By utilizing freely available web-based software and universal primers for multipurpose barcoding, library preparation, and customized sequencing, sTM-Seq is affordable, efficient, and widely applicable. Together, these features enable sTM-Seq to have wide-reaching clinical applications that will greatly improve turnaround rates for same-day procedures and allow clinicians to collect high-resolution data that can be used in a variety of patient settings. © 2019 by John Wiley & Sons, Inc.

Electronic health records contain patient-level data collected during and for clinical care. Data within the electronic health record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and physician notes. With repeated clinic visits, these data are longitudinal, providing important information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of electronic health records nationally has the potential to provide population-scale real-world clinical data accessible for biomedical research, including genetic association studies. For this research potential to be realized, high-quality research-grade variables must be extracted from these clinical data warehouses. We describe here common and emerging electronic phenotyping approaches applied to electronic health records, as well as current limitations of both the approaches and the biases associated with these clinically collected data that impact their use in research. © 2018 by John Wiley & Sons, Inc.

Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.

Family health history has long been known to be a powerful predictor of individual disease risk. It can be obtained prior to DNA sequencing in order to examine inheritance patterns, to be used as a proxy for genetic information, or as a tool to guide decision-making on the utility of diagnostic genetic testing. Increasingly, it is also being obtained retrospectively from sequenced individuals to examine familial disease penetrance and to identify at-risk relatives for cascade testing. The collection of adequate family history information to screen patients for disease risk and guide decision-making is a time-consuming process that is difficult to accomplish exclusively through discussion between patients and their providers. Engaging individuals and families in data collection and data entry has the potential to improve data accuracy through re-iterative review with family members and health care providers, and to empower patients in their healthcare. In addition, electronic datasets can be shared amongst relatives and stored in electronic health records or personal files, enabling portability of family history information. The U.S. Surgeon General, the Centers for Disease Control and Prevention (CDC), and others have developed tools for electronic family history collection to help families and providers obtain this useful information in an efficient manner. This unit describes the utility of the web-based My Family Health Portrait (https://familyhistory.hhs.gov) as the prototype for patient-entered family history. © 2018 by John Wiley & Sons, Inc.