Metagenomics and personalized medicine

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn's disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes.     

U-statistics in genetic association studies

Many common human diseases are complex and are expected to be highly heterogeneous, with multiple causative loci and multiple rare and common variants at some of the causative loci contributing to the risk of these diseases. Data from the genome-wide association studies (GWAS) and metadata such as known gene functions and pathways provide the possibility of identifying genetic variants, genes and pathways that are associated with complex phenotypes. Single-marker-based tests have been very successful in identifying thousands of genetic variants for hundreds of complex phenotypes. However, these variants only explain very small percentages of the heritabilities. To account for the locus- and allelic-heterogeneity, gene-based and pathway-based tests can be very useful in the next stage of the analysis of GWAS data. U-statistics, which summarize the genomic similarity between pair of individuals and link the genomic similarity to phenotype similarity, have proved to be very useful for testing the associations between a set of single nucleotide polymorphisms and the phenotypes. Compared to single marker analysis, the advantages afforded by the U-statistics-based methods is large when the number of markers involved is large. We review several formulations of U-statistics in genetic association studies and point out the links of these statistics with other similarity-based tests of genetic association. Finally, potential application of U-statistics in analysis of the next-generation sequencing data and rare variants association studies are discussed.     

Microarray genotyping resource to determine population stratification in genetic association studies of complex disease

We have developed a robust microarray genotyping chip that will help advance studies in genetic epidemiology. In population-based genetic association studies of complex disease, there could be hidden genetic substructure in the study populations, resulting in false-positive associations. Such population stratification may confound efforts to identify true associations between genotype/haplotype and phenotype. Methods relying on genotyping additional null single nucleotide polymorphism (SNP) markers have been proposed, such as genomic control (GC) and structured association (SA), to correct association tests for population stratification. If there is an association of a disease with null SNPs, this suggests that there is a population subset with different genetic background plus different disease susceptibility. Genotyping over 100 null SNPs in the large numbers of patient and control DNA samples that are required in genetic association studies can be prohibitively expensive. We have therefore developed and tested a resequencing chip based on arrayed primer extension (APEX) from over 2000 DNA probe features that facilitate multiple interrogations of each SNP, providing a powerful, accurate, and economical means to simultaneously determine the genotypes at 110 null SNP loci in any individual. Based on 1141 known genotypes from other research groups, our GC SNP chip has an accuracy of 98.5%, including non-calls.     

Genetic design automation: engineering fantasy or scientific renewal?

The aim of synthetic biology is to make genetic systems more amenable to engineering, which has naturally led to the development of computer-aided design (CAD) tools. Experimentalists still primarily rely on project-specific ad hoc workflows instead of domain-specific tools, which suggests that CAD tools are lagging behind the front line of the field. Here, we discuss the scientific hurdles that have limited the productivity gains anticipated from existing tools. We argue that the real value of efforts to develop CAD tools is the formalization of genetic design rules that determine the complex relationships between genotype and phenotype.     

Modellvorstellungen zur Genetik multifaktorieller Krankheiten

In contrast to monogenic diseases, a straightforward genotype–phenotype relationship is unlikely for multifactorial diseases because of a number of genetic and nongenetic factors, including genetic heterogeneity, gene–gene and gene–environment interactions, and epigenetic mechanisms. As a consequence, the relative risk of particular genetic variants will generally be small, which implies that large sample sizes are required for their initial identification. No conclusions as to the frequency and diversity of the causative genetic variation can generally be drawn from the prevalence of a disease alone. Homogenization of the genetic background of the study population and the use of simple and clearly defined phenotypes together with “educated guesses” in candidate gene and gene–environment studies appear to be the most promising way to identify the genetic factors underlying multifactorial diseases. Replication of initial disease association findings, particularly for rare variants, should be carried out in populations that are genetically as similar as possible to the original population.     

Managing expectational language: translational genetic professionals consider the clinical potential of next-generation sequencing technologies

Clinical genetic professionals are used to being flooded by claims about the seemingly endless potential and promise of next-generation sequencing (NGS) in medicine today. This paper is about managing expectations in translational medicine. From 2009 to 2011, we conducted focus groups with genetic and allied professionals concerned with genomics in the clinic to examine their attitudes and perspectives of genetic and genomic tools in this environment. In this paper, we examine and explore some of their discussions, specifically related to NGS and whole genome sequencing tests and their introduction as normal clinical tools. Informed by sociology of expectations (SE), we discuss expectational language in the arena of translational medicine. Through SE, illuminated are some barriers and strategies used by professionals to manage expectations. Further, our work suggests the importance of SE and more nuanced study to understand the discursive realm of translational genomic medicine.     

Selection and mutation in the “new” genetics: an emerging hypothesis

It has been anticipated that new, much more sensitive, next generation sequencing (NGS) techniques, using massively parallel sequencing, will likely provide radical insights into the genetics of multifactorial diseases. While NGS has been used initially to analyze individual human genomes, and has revealed considerable differences between healthy individuals, we have used NGS to examine genetic variation within individuals, by sequencing tissues “in depth”, i.e., oversequencing many thousands of times. Initial studies have revealed intra-tissue genetic heterogeneity, in the form of multiple variants of a single gene that exist as distinct “majority and “minority” variants. This highly specialized form of somatic mosaicism has been found within both cancer and normal tissues. If such genetic variation within individual tissues is widespread, it will need to be considered as a significant factor in the ontogeny of many multifactorial diseases, including cancer. The discovery of majority and minority gene variants and the resulting somatic cell heterogeneity in both normal and diseased tissues suggests that selection, as opposed to mutation, might be the critical event in disease ontogeny. We, therefore, are proposing a hypothesis to explain multifactorial disease ontogeny in which pre-existing multiple somatic gene variants, which may arise at a very early stage of tissue development, are eventually selected due to changes in tissue microenvironments.     

X染色体变异对男性精神发育迟滞致病性的研究进展

精神发育迟滞(旧称智力低下)作为儿科神经科常见的一组疾患,具有高度的遗传和表型异质性,大约25%~50%的精神发育迟滞是由遗传因素引起的,其中X染色体基因/基因组变异占25%~30%,导致X连锁的精神发育迟滞。X连锁的精神发育迟滞患者占所有精神发育迟滞患者的10%~15％以上,约20%~25％的男性精神发育迟滞归因于X连锁的精神发育迟滞。精神发育迟滞男女患病比例为1.3:1,这与男性只有一条X染色体的遗传背景有关。随着新一代基因组检测技术的快速发展和临床应用,尤其是全外显子测序、高深度测序、X染色体深度测序和全基因组芯片杂交,这些大大改善了精神发育迟滞患者的X染色体基因/基因组变异检出。本文综述了致精神发育迟滞的X染色体基因组/基因变异特点、其对男性精神发育迟滞的致病性,以及如何采用新测序技术提高检出率,旨在促进科研人员认识X染色体变异在男性精神发育迟滞的致病性,拓宽精神发育迟滞遗传病因的认识,同时也为遗传咨询和产前诊断提供理论依据。     

Using genetic variation to study human disease.

The generation of a draft sequence of the human genome has spawned a unique opportunity to investigate the role of genetic variation in human diseases. The difference between any two human genomes has been estimated to be less than 0.1% overall, but still, this means that there are at least several million nucleotide differences per individual. The study of single nucleotide polymorphisms (SNPs), the most common type of variant, is likely to contribute substantially to deciphering genetic determinants of common and rare diseases. The effort to identify SNPs has been accelerated by three developments: the availability of sequence data from the genome project, improved informatic tools for searching the former and high-throughput genotype platforms. With these new tools in hand, dissecting the genetics of disease will rapidly move forward, although a number of formidable challenges will have to be met to see its promise realized in clinical medicine.     

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.     

Bread matters: a national initiative to profile the genetic diversity of Australian wheat

The large and complex genome of wheat makes genetic and genomic analysis in this important species both expensive and resource intensive. The application of next-generation sequencing technologies is particularly resource intensive, with at least 17?Gbp of sequence data required to obtain minimal (1×) coverage of the genome. A similar volume of data would represent almost 40× coverage of the rice genome. Progress can be made through the establishment of consortia to produce shared genomic resources. Australian wheat genome researchers, working with Bioplatforms Australia, have collaborated in a national initiative to establish a genetic diversity dataset representing Australian wheat germplasm based on whole genome next-generation sequencing data. Here, we describe the establishment and validation of this resource which can provide a model for broader international initiatives for the analysis of large and complex genomes.     

Predicting complex phenotype–genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both “red” and “white” biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype–phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.     

Recent advances in the genetic investigation of osteoarthritis

Osteoarthritis (OA) demonstrates considerable clinical heterogeneity, generating heated debate over whether OA is a single disease or a complex mix of disparate diseases and concerning which tissues are principally involved in disease initiation and progression. Epidemiological studies have demonstrated a major genetic component to OA risk. However, these studies have also revealed differences in risk between males and females and for disease at different skeletal sites. This observation has resulted in the concept of genes for specific sites rather than a generalised OA phenotype. Recent breakthroughs have shed considerable light on the nature of OA genetic susceptibility. Many candidate genes have been confirmed, such as the interleukin-1 gene cluster and the oestrogen alpha-receptor gene ESR1. Genome-wide linkage scans have revealed several regions harbouring novel loci, some of which are beginning to yield their genes.     

Population genomics in Sardinia: a novel approach to hunt for genomic combinations underlying complex traits and diseases.

The availability of highly polymorphic markers permits testing whether complex traits and diseases result from genomic interactions between nonallelic normal variants at separate loci. Such variants may be identified by deviations from the expected distributions of alleles at a high number of polymorphic loci, when individuals with the phenotype of interest are compared to normal controls of the same breeding unit, provided that both groups share the same remote ancestry and had no ancestors in common for the last three to four generations. The circumstances needed for such studies are ideally met on the island of Sardinia. The recurrent finding of the same type of association in separate breeding units between the phenotype of interest and a given genotype should allow a distinction between true genetic identity by descent and randomly occurring identities, as these will be obviously different in separate breeding units. The availability of several breeding units located in sharply different ecological environments will permit assessment of the role of nature/nurture factors in the degree of manifestation of each newly discovered genotype/phenotype association. A pilot study to evaluate the proposed strategy has been carried out in the Sardinian village of Carloforte, a community of about 8,000 individuals who have remained genetically homogeneous. Fifty-five control samples have been genotyped with six tetranucleotide microsatellites and with a subset of the 400 markers contained in the ABI PRISM linkage mapping panel, version 2. The allele frequencies for these microsatellite markers have been determined for these 55 individuals and compared to those from a random sampling of subsets of these 55 persons. For the six tetranucleotide microsatellites, a subset of as few as 20 people displayed the same allele frequency distributions as observed with the original 55 unrelated individuals. In conclusion, when samples are chosen from the same breeding unit, the number of individuals sufficient to draw the genomic profile of an isolated population can be relatively small. Likewise, the number of probands with the phenotype of interest can be even smaller when they are ascertained with the same genealogical criteria as the normal controls. By comparing the genomic profile of the probands to a fraction of the control samples within each of several separate breeding units of common remote ancestry, the search for genotype/phenotype association for mono- and multifactorial traits and diseases should be simplified and yield unequivocal results.     

Population structure,landscape genomics,and genetic signatures of adaptation to exotic disease pressure in Cornus florida L.—Insights from GWAS and GBS data

Understanding the consequences of exotic diseases on native forests is important to evolutionary ecology and conservation biology because exotic pathogens have drastically altered US eastern deciduous forests. Cornus florida L. (flowering dogwood tree) is one such species facing heavy mortality. Characterizing the genetic structure of C. florida populations and identifying the genetic signature of adaptation to dogwood anthracnose (an exotic pathogen responsible for high mortality) remain vital for conservation efforts. By integrating genetic data from genotype by sequencing (GBS) of 289 trees across the host species range and distribution of disease, we evaluated the spatial patterns of genetic variation and population genetic structure of C. florida and compared the pattern to the distribution of dogwood anthracnose. Using genome‐wide association study and gradient forest analysis, we identified genetic loci under selection and associated with ecological and diseased regions. The results revealed signals of weak genetic differentiation of three or more subgroups nested within two clusters—explaining up to 2%–6% of genetic variation. The groups largely corresponded to the regions within and outside the eastern Hot‐Continental ecoregion, which also overlapped with areas within and outside the main distribution of dogwood anthracnose. The fungal sequences contained in the GBS data of sampled trees bolstered visual records of disease at sampled locations and were congruent with the reported range of Discula destructiva, suggesting that fungal sequences within‐host genomic data were informative for detecting or predicting disease. The genetic diversity between populations at diseased vs. disease‐free sites across the range of C. florida showed no significant difference. We identified 72 single‐nucleotide polymorphisms (SNPs) from 68 loci putatively under selection, some of which exhibited abrupt turnover in allele frequencies along the borders of the Hot‐Continental ecoregion and the range of dogwood anthracnose. One such candidate SNP was independently identified in two prior studies as a possible L‐type lectin‐domain containing receptor kinase. Although diseased and disease‐free areas do not significantly differ in genetic diversity, overall there are slight trends to indicate marginally smaller amounts of genetic diversity in disease‐affected areas. Our results were congruent with previous studies that were based on a limited number of genetic markers in revealing high genetic variation and weak population structure in C. florida.     

Application of exome sequencing in the search for genetic causes of rare disorders of copper metabolism

The genetic defect in a number of rare disorders of metal metabolism remains elusive. The limited number of patients with these disorders impedes the identification of the causative gene through positional cloning, which requires numerous families with multiple affected individuals. However, with next-generation sequencing all coding DNA (exomes) or whole genomes of patients can be sequenced to identify genes that are consistently mutated in patients. With this strategy only a limited number of patients and/or pedigrees is needed, bringing the elucidation of the genetic cause of even very rare diseases within reach. The main challenge associated with whole exome sequencing is the identification of the disease-causing mutation(s) among abundant genetic candidate variants. We describe several strategies to manage this data wealth, including comparison with control databases, increasing the number of patients and controls, and reducing the genomic region under investigation through homozygosity mapping. In this review we introduce a number of rare disorders of copper metabolism, with a suspected but yet unknown monogenetic cause, as an attractive target for this strategy. We anticipate that use of these novel techniques will identify the basic defect in the disorders described in this review, as well as in other genetic disorders of metal metabolism, in the next few years.     

Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches,success, challenges and prospects

Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with \(\sim \)5–10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited. This review provides a concise account of the genetics that we know, the pathways that they implicate, the challenges that are faced and the prospects that are envisaged for the sporadic, complex forms of neurodegenerative diseases, taking four most common conditions, namely Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease as examples. Poor replication across studies, inability to establish genotype–phenotype correlations and the overall failure to predict risk and/or prevent disease in this group poses a continuing challenge. Among others, clinical heterogeneity emerges as the most important impediment warranting newer approaches. Advanced computational and system biology tools to analyse the big data are being generated and the alternate strategy such as subgrouping of case–control cohorts based on deep phenotyping using the principles of Ayurveda to overcome current limitation of phenotype heterogeneity seem to hold promise. However, at this point, with advances in discovery genomics and functional analysis of putative determinants with translation potential for the complex forms being minimal, stem cell therapies are being attempted as potential interventions. In this context, the possibility to generate patient derived induced pluripotent stem cells, mutant/gene/genome correction through CRISPR/Cas9 technology and repopulating the specific brain regions with corrected neurons, which may fulfil the dream of personalized medicine have been mentioned briefly. Understanding disease pathways / biology using this technology, with implications for development of novel therapeutics are optimistic expectations in the near future.