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Despite the importance of secondary dormancy for plant life cycle timing and survival, there is insufficient knowledge about the (epigenetic) regulation of this trait at the molecular level. Our aim was to determine the role of (epi)genetic processes in the regulation of secondary seed dormancy using natural genotypes of the widely distributed Capsella bursa-pastoris. Seeds of nine ecotypes were exposed to control conditions or histone deacetylase inhibitors [trichostatin A (TSA), valproic acid] during imbibition to study the effects of hyper-acetylation on secondary seed dormancy induction and germination. Valproic acid increased secondary dormancy and both compounds caused a delay of t50 for germination (radicle emergence) but not of t50 for testa rupture, demonstrating that they reduced speed of germination. Transcriptome analysis of one accession exposed to valproic acid versus water showed mixed regulation of ABA, negative regulation of GAs, BRs and auxins, as well as up-regulation of SNL genes, which might explain the observed delay in germination and increase in secondary dormancy. In addition, two accessions differing in secondary dormancy depth (deep vs non-deep) were studied using RNA-seq to reveal the potential regulatory processes underlying this trait. Phytohormone synthesis or signalling was generally up-regulated for ABA (e.g. NCED6, NCED2, ABCG40, ABI3) and down-regulated for GAs (GA20ox1, GA20ox2, bHLH93), ethylene (ACO1, ERF4-LIKE, ERF105, ERF109-LIKE), BRs (BIA1, CYP708A2-LIKE, probable WRKY46, BAK1, BEN1, BES1, BRI1) and auxin (GH3.3, GH3.6, ABCB19, TGG4, AUX1, PIN6, WAT1). Epigenetic candidates for variation in secondary dormancy depth include SNL genes, histone deacetylases and associated genes (HDA14, HDA6-LIKE, HDA-LIKE, ING2, JMJ30), as well as sequences linked to histone acetyltransferases (bZIP11, ARID1A-LIKE), or to gene silencing through histone methylation (SUVH7, SUVH9, CLF). Together, these results show that phytohormones and epigenetic regulation play an important role in controlling differences in secondary dormancy depth between accessions.
The classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption — that monozygotic and dizygotic twins experience similar degrees of environmental similarity — an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.
I am grateful to Krishnarao Appasani for making the Herculean effort to prepare this volume on the rapidly expanding fields of Genome-Wide Association Studies (GWAS) and personalized medicine, and for inviting me to offer a few of my own introductory statements.
The complexity of the human disease state has always been an area of human curiosity. Over the last decade, GWASs have enabled us to expand our understanding of complex diseases using genetic-based approaches. We now see GWAS as a technology platform that promises to help move us into the era of personalized medicine.
Genome-Wide Association Studies: From Polymorphism to Personalized Medicine edited by Dr. Appasani has assembled the contributing chapters into five main areas encompassing: an introduction to GWAS in medicine, GWAS in pharmacogenomics, different classes of genetic variants for GWAS, new technologies including next-generation sequencing, and population genetics. The component chapters will be highly valuable, not only to those who are experimentally active in these aspects of research, but also to those interested in potential drug discovery applications. The historical perspectives offered also bring forward a unique vantage point into ongoing and future research in this field.
I believe that this book will become a reliable guide for anyone attempting to understand the successes with GWAS, planning new experiments, as well as its potential for the advancement of medicine. We approach a time where advances in genome sequencing technologies will deliver the long-awaited $1,000 genome, which promises to enable capture of all classes of genetic variants in a single experiment empowering new and innovative future studies. As such, studying the content of this book will allow us to pause and reflect, of where the field has come from, and where it needs to now go.
Almost 10 years of GWAS: a feast of discoveries
Genome-wide association studies (GWASs) are less than 10 years old but have revolutionized discoveries in human genetics. GWAS is an experimental design based upon association because it exploits the fact that genetic variants that are close together tend to be statistically correlated. Driven by advances in array technologies these genome surveys of genetic variation have led to the discovery of thousands of DNA variants that are associated with complex traits, including many diseases. They have also led to new insights in human evolution and population differences.
Over the last twenty years, genome-wide association studies (GWAS) have revealed a great deal about the genetic basis of a wide range of complex diseases and they will undoubtedly continue to have a broad impact as we move to an era of personalised medicine. This authoritative text, written by leaders and innovators from both academia and industry, covers the basic science as well as the clinical, biotechnological and pharmaceutical potential of these methods. With special emphasis given to highlighting pharmacogenomics and population genomics studies using next-generation technology approaches, this is the first book devoted to combining association studies with single nucleotide polymorphisms, copy number variants, haplotypes and expressed quantitative trait loci. A reliable guide for newcomers to the field as well as for experienced scientists, this is a unique resource for anyone interested in how the revolutionary power of genomics can be applied to solve problems in complex disease.
Monozygotic (MZ) twins form an important system for the study of biological plasticity in humans. While MZ twins are generally considered to be genetically identical, a number of studies have emerged that have demonstrated copy-number differences within a twin pair, particularly in those discordant for disease. The rate of autosomal copy-number variation (CNV) discordance within MZ twin pairs was investigated using a population sample of 376 twin pairs genotyped on Illumina Human610-Quad arrays. After CNV calling using both QuantiSNP and PennCNV followed by manual annotation, only a single CNV difference was observed within the MZ twin pairs, being a 130 KB duplication of chromosome 5. Five other potential discordant CNV were called by the software, but excluded based on manual annotation of the regions. It is concluded that large CNV discordance is rare within MZ twin pairs, indicating that any CNV difference found within phenotypically discordant MZ twin pairs has a high probability of containing the causal gene(s) involved.
Genome-wide association analysis on monozygotic twin-pairs offers a route to discovery of gene–environment interactions through testing for variability loci associated with sensitivity to individual environment/lifestyle. We present a genome-wide scan of loci associated with intra-pair differences in serum lipid and apolipoprotein levels. We report data for 1,720 monozygotic female twin-pairs from GenomEUtwin project with 2.5 million SNPs, imputed or genotyped, and measured serum lipid fractions for both twins. We found one locus associated with intra-pair differences in high-density lipoprotein cholesterol, rs2483058 in an intron of SRGAP2, where twins carrying the C allele are more sensitive to environmental factors (P = 3.98 × 10−8). We followed up the association in further genotyped monozygotic twins (N = 1,261), which showed a moderate association for the variant (P = 0.200, same direction of an effect). In addition, we report a new association on the level of apolipoprotein A-II (P = 4.03 × 10−8).
Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects.
When using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a χ2 distribution with degrees of freedom equal to the number of parameters tested. Using the relationship between least squares and maximum likelihood estimators for balanced designs, it is shown why the asymptotic distribution of the likelihood ratio test for variance components does not follow a χ2 distribution with degrees of freedom equal to the number of parameters tested when the null hypothesis is true. Instead, the distribution of the likelihood ratio test is a mixture of χ2 distributions with different degrees of freedom. Implications for testing variance components in twin designs and for quantitative trait loci mapping are discussed. The appropriate distribution of the likelihood ratio test statistic should be used in hypothesis testing and model selection.