Genetic variation in an individual human exome

There is much interest in characterizing the variation in a human individual, because this may elucidate what contributes significantly to a person's phenotype, thereby enabling personalized genomics. We focus here on the variants in a person's 'exome,' which is the set of exons in a genome, because the exome is believed to harbor much of the functional variation. We provide an analysis of the approximately 12,500 variants that affect the protein coding portion of an individual's genome. We identified approximately 10,400 nonsynonymous single nucleotide polymorphisms (nsSNPs) in this individual, of which approximately 15-20% are rare in the human population. We predict approximately 1,500 nsSNPs affect protein function and these tend be heterozygous, rare, or novel. Of the approximately 700 coding indels, approximately half tend to have lengths that are a multiple of three, which causes insertions/deletions of amino acids in the corresponding protein, rather than introducing frameshifts. Coding indels also occur frequently at the termini of genes, so even if an indel causes a frameshift, an alternative start or stop site in the gene can still be used to make a functional protein. In summary, we reduced the set of approximately 12,500 nonsilent coding variants by approximately 8-fold to a set of variants that are most likely to have major effects on their proteins' functions. This is our first glimpse of an individual's exome and a snapshot of the current state of personalized genomics. The majority of coding variants in this individual are common and appear to be functionally neutral. Our results also indicate that some variants can be used to improve the current NCBI human reference genome. As more genomes are sequenced, many rare variants and non-SNP variants will be discovered. We present an approach to analyze the coding variation in humans by proposing multiple bioinformatic methods to hone in on possible functional variation.     

The legacy of pharmacogenetics and potential applications

Some 40 years of pharmacogenetic research indicates that knowledge of human genetic diversity is essential to a broader understanding of variation in human drug response, and suggests that drug therapy tailored to the genetic characteristics of the individual may be a realistic goal. Aided by new technologies, molecular studies of genetic polymorphisms of many human enzymes, receptors, and other proteins indicate that only a limited number of important protein variants account for the diversity in drug response, raising the prospect that these variants may be cataloged relatively soon for many human populations. The next great challenge of pharmacogenetics is to pin down the cellular location and effect of these variant proteins on the pathways and networks that govern individual variation in responses to drugs and other exogenous chemicals. In this paper, we will discuss some the current challenges to progress in pharmacogenetics and newer strategies that might be used to improve prospects of drug design and personalized therapy.     

Prediction of personalized drugs based on genetic variations provided by DNA sequencing technologies

Recent reports of death and illness caused by adverse drug reactions have boosted rational drug design research. It has been shown through sequencing of the entire human genome that human genetic variations play a key role in adverse reactions to drugs as well as in differences in the effectiveness of drug treatments. The advent of high-throughput DNA sequencing technologies with bioinformatics of system biology have allowed the easy identification of genetic variations and all other pharmacogenetic variants in a single assay, thus permitting truly personalized drug treatment. This would be particularly valuable for many patients with chronic diseases who must take many medications concurrently. In this review, we have focused on pharmacogenomics for the prediction of variable drug responses between individuals with relevant genetic variations through new DNA sequencing technologies and provided directions for personalized drug therapy in the future.     

Overcoming technical variation and biological variation in quantitative proteomics

Quantitative proteomics investigates physiology at the molecular level by measuring relative differences in protein expression between samples under different experimental conditions. A major obstacle to reliably determining quantitative changes in protein expression is to overcome error imposed by technical variation and biological variation. In drug discovery and development the issue of biological variation often rises in concordance with the developmental stage of research, spanning from in vitro assays to clinical trials. In this paper we present case studies to raise awareness to the issues of technical variation and biological variation and the impact this places on applying quantitative proteomics. We defined the degree of technical variation from the process of two-dimensional electrophoresis as 20-30% coefficient of variation. On the other hand, biological variation observed experiment-to-experiment showed a broader degree of variation depending upon the sample type. This was demonstrated with case studies where variation was monitored across experiments with bacteria, established cell lines, primary cultures, and with drug treated human subjects. We discuss technical variation and biological variation as key factors to consider during experimental design, and offer insight into preparing experiments that overcome this challenge to provide statistically significant outcomes for conducting quantitative proteomic research.     

Standing genetic variation and the evolution of drug resistance in HIV

Drug resistance remains a major problem for the treatment of HIV. Resistance can occur due to mutations that were present before treatment starts or due to mutations that occur during treatment. The relative importance of these two sources is unknown. Resistance can also be transmitted between patients, but this process is not considered in the current study. We study three different situations in which HIV drug resistance may evolve: starting triple-drug therapy, treatment with a single dose of nevirapine and interruption of treatment. For each of these three cases good data are available from literature, which allows us to estimate the probability that resistance evolves from standing genetic variation. Depending on the treatment we find probabilities of the evolution of drug resistance due to standing genetic variation between 0 and 39%. For patients who start triple-drug combination therapy, we find that drug resistance evolves from standing genetic variation in approximately 6% of the patients. We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation. We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation. Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.     

Human genome project: pharmacogenomics and drug development

Now that all 30,000 or so genes that make up the human genome have been deciphered, pharmaceutical industries are emerging to capitalize the custom based drug treatment. Understanding human genetic variation promises to have a great impact on our ability to uncover the cause of individual variation in response to therapeutics. The study of association between genetics and drug response is called pharmacogenomics. The potential implication of genomics and pharmacogenomics in clinical research and clinical medicine is that disease could be treated according to the interindividual differences in drug disposition and effects, thereby enhancing the drug discovery and providing a stronger scientific basis of each patient's genetic constitution. Sequence information derived from the genomes of many individuals is leading to the rapid discovery of single nucleotide polymorphisms or SNPs. Detection of these human polymorphisms will fuel the discipline of pharmacogenomics by developing more personalized drug therapies. A greater understanding of the way in which individuals with a particular genotype respond to a drug allows manufacturers to identify population subgroups that will benefit most from a particular drug. The increasing emphasis on pharmacogenomics is likely to raise ethical and legal questions regarding, among other things, the design of research studies, the construction of clinical trials and the pricing of drugs.     

人类基因组SNPs的研究现状及应用前景

基因组DNA是生物体各种生理、病理性状的物质基础,人类DNA序列变异约90%表现为单核苷酸多态性(singlenucleotidepolymorphisms,SNPs),这是一种常见的遗传变异类型,在人类基因组中广泛存在,被认为是人类疾病易感性和药物反应的决定性因素。本文主要介绍了SNPs的分类及特点、人类基因组SNPs的研究现状、SNPs在实践中的应用,以及SNPs在遗传作图、医药、遗传易感性、个体化医疗等方面的研究前景,并探讨了当前SNPs研究中存在的问题。     

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

Dealing with stochastic environmental variation in space and time: bet hedging by generalist,specialist, and diversified strategies

Building on previous work, we derive an optimization model for a two-state stochastic environment and evaluate the fitnesses of five reproductive strategies across generations. To do this, we characterize spatiotemporal variation and define grain (=patch) size as the scale of fitness autocorrelation. Fitness functions of environmental condition are Gaussian. The strategies include two specialists on each of the environmental conditions; two generalists that each fare equally well under both conditions, but one (a conservative bet hedger) optimizes the shape of the fitness function; and a diversified bet hedger producing an optimal mix of the two specialists within individual broods. When the environment is primarily in one of the two states, the specialist on that state achieves the highest fitness. In the more interesting situation where the two environments are equally prevalent in the long term, with low-moderate environmental variation, a generalist strategy (that copes with both states well) does best. Higher variation favors diversified bet hedgers, or surprisingly, specialists, depending mainly on whether spatial or temporal variation predominates. These strategies reduce variance in fitness and optimize the distribution of offspring among patches differently: specialists by spreading offspring among many independently varying patches, while diversified bet hedgers put all offspring into a few patches or a single patch. We distinguish features consistent with strategies like diversified bet hedgers that spread risk in time from features linked to strategies like specialists that spread risk in space. Finally, we present testable hypotheses arising from this study and suggest directions for future work.     

Precision medicine in human heart modeling

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

     

Cis-regulatory variation: significance in biomedicine and evolution

Cis-regulatory regions (CRR) control gene expression and chromatin modifications. Genetic variation at CRR in individuals across a population contributes to phenotypic differences of biomedical relevance. This standing variation is important for personalized genomic medicine as well as for adaptive evolution and speciation. This review focuses on genetic variation at CRR, its influence on chromatin, gene expression, and ultimately disease phenotypes. In addition, we summarize our understanding of how this variation may contribute to evolution. Recent technological and computational advances have accelerated research in the direction of personalized medicine, combining strengths of molecular biology and genomics. This will pave new ways to understand how CRR variation affects phenotypes and chart out possible avenues of intervention.     

Chemical genetic interrogation of natural variation uncovers a molecule that is glycoactivated

Natural variation in human drug metabolism and target genes can cause pharmacogenetic or interindividual variation in drug sensitivity. We reasoned that natural pharmacogenetic variation in model organisms could be systematically exploited to facilitate the characterization of new small molecules. To test this, we subjected multiple Arabidopsis thaliana accessions to chemical genetic screens and discovered 12 accession-selective hit molecules. As a model for understanding this variation, we characterized natural resistance to hypostatin, a new inhibitor of cell expansion. Map-based cloning identified HYR1, a UDP glycosyltransferase (UGT), as causative for hypostatin resistance. Multiple lines of evidence demonstrate that HYR1 glucosylates hypostatin in vivo to form a bioactive glucoside. Additionally, we delineated a HYR1 substrate motif and used it to identify another molecule modulated by glucosylation. Our results demonstrate that natural variation can be exploited to inform the biology of new small molecules, and that UGT sequence variation affects xenobiotic sensitivity across biological kingdoms.     

MicroRNA pharmacogenomics: post-transcriptional regulation of drug response

The field of pharmacogenomics aims to predict which drugs will be most effective and safe for a particular individual based on their genome sequence or expression profile, thereby allowing personalized treatment. The bulk of pharmacogenomic research has focused on the role of single nucleotide polymorphisms, copy number variations or differences in gene expression levels of drug metabolizing or transporting genes and drug targets. In this review paper, we focus instead on microRNAs (miRNAs): small noncoding RNAs, prevalent in metazoans, that negatively regulate gene expression in many cellular processes. We discuss how miRNAs, by regulating the expression of pharmacogenomic-related genes, can play a pivotal role in drug efficacy and toxicity and have potential clinical implications for personalized medicine.     

Human loci involved in drug biotransformation: worldwide genetic variation, population structure, and pharmacogenetic implications

Understanding the role of inheritance in individual variation in drug response is the focus of pharmacogenetics (PGx). A key part of this understanding is quantifying the role of genetic ancestry in this phenotypic outcome. To provide insight into the relationship between ethnicity and drug response, this study first infers the global distribution of PGx variation and defines its structure. Second, the study evaluates if geographic population structure stems from all PGx loci in general, or if structure is caused by specific genes. Lastly, we identify the genetic variants contributing the greatest proportion of such structure. Our study describes the global genetic structure of PGx loci across the 52 populations of the Human Genome Diversity Cell-Line Panel, the most inclusive set of human populations freely available for studies on human genetic variation. By analysing genetic variation at 1,001 single nucleotide polymorphisms (SNPs) involved in biotransformation of exogenous substances, we describe the between-populations PGx variation, as well geographical groupings of diversity. In addition, with discriminant analysis of principal component (DAPC), we infer how many and which groups of populations are supported by PGx variation, and identify which SNPs actually contribute to the PGx structure between such groups. Our results show that intergenic, synonymous and non-synonymous SNPs show similar levels of genetic variation across the globe. Conversely, loci coding for Cytochrome P450s (mainly metabolizing exogenous substances) show significantly higher levels of genetic diversity between populations than the other gene categories. Overall, genetic variation at PGx loci correlates with geographic distances between populations, and the apportionment of genetic variation is similar to that observed for the rest of the genome. In other words, the pattern of PGx variation has been mainly shaped by the demographic history of our species, as in the case of most of our genes. The population structure defined by PGx loci supports the presence of six genetic clusters reflecting geographic location of samples. In particular, the results of the DAPC analyses show that 27 SNPs substantially contribute to the first three discriminant functions. Among these SNPs, some, such as the intronic rs1403527 of NR1I2 and the non-synonymous rs699 of AGT, are known to be associated with specific drug responses. Their substantial variation between different groups of populations may have important implications for PGx practical applications.     

Inter-individual variation in DNA repair capacity: A need for multi-pathway functional assays to promote translational DNA repair research

Why does a constant barrage of DNA damage lead to disease in some individuals, while others remain healthy? This article surveys current work addressing the implications of inter-individual variation in DNA repair capacity for human health, and discusses the status of DNA repair assays as potential clinical tools for personalized prevention or treatment of disease. In particular, we highlight research showing that there are significant inter-individual variations in DNA repair capacity (DRC), and that measuring these differences provides important biological insight regarding disease susceptibility and cancer treatment efficacy. We emphasize work showing that it is important to measure repair capacity in multiple pathways, and that functional assays are required to fill a gap left by genome wide association studies, global gene expression and proteomics. Finally, we discuss research that will be needed to overcome barriers that currently limit the use of DNA repair assays in the clinic.     

Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis

Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC), and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ breast cancer subtypes exhibit marked differences in etiologic factors, clinical characteristics and therapeutic potential. Anthracycline drugs are typically used as the first-line clinical treatment for basal-like breast cancer subtypes. However, certain patients develop drug resistance following chemotherapy, which can lead to disease relapse and death. Even among patients with basal-like breast cancer, there can be significant molecular differences, and it is difficult to identify specific drug resistance proteins in any given patient using conventional variance testing methods. Therefore, we designed a new method for identifying drug resistance genes. Subgroups, personalized biomarkers, and therapy targets were identified using cluster analysis of differentially expressed genes. We found that basal-like breast cancer could be further divided into at least four distinct subgroups, including two groups at risk for drug resistance and two groups characterized by sensitivity to pharmacotherapy. Based on functional differences among these subgroups, we identified nine biomarkers related to drug resistance: SYK, LCK, GAB2, PAWR, PPARG, MDFI, ZAP70, CIITA and ACTA1. Finally, based on the deviation scores of the examined pathways, 16 pathways were shown to exhibit varying degrees of abnormality in the various subgroups, indicating that patients with different subtypes of basal-like breast cancer can be characterized by differences in the functional status of these pathways. Therefore, these nine differentially expressed genes and their associated functional pathways should provide the basis for novel personalized clinical treatments of basal-like breast cancer.