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Genome replication programs are highly orchestrated. In this issue, Koren and colleagues leverage whole-genome sequencing data to discover that DNA replication timing patterns differ between individuals and are associated with genetic variants. These so-called rtQTLs represent a new form of quantitative trait loci that may influence gene dosage and mutational frequency and provide mechanistic insights into the regulation of DNA replication.
Researchers are using various strategies to identify the genes that may be associated with alcoholism. The initial efforts primarily relied on candidate gene and linkage studies; more recently, however, modern advances in genotyping have resulted in widespread use of genome-wide association studies for alcohol dependence. The key findings of the earlier studies were that variations (i.e., polymorphisms) in the DNA sequences of the genes encoding alcohol dehydrogenase 1B (i.e., the ADH1B gene), aldehyde dehydrogenase 2 (i.e., the ALDH2 gene), and other alcohol-metabolizing enzymes mediate the risk for alcoholism; moreover, these polymorphisms also have an impact on the risk of alcohol-related cancers, such as esophageal cancer. In addition, a gene encoding one of the receptors for the neurotransmitter γ-aminobutyric acid (GABA) known as GABRA2 seems to have a role in the development of alcohol dependence. Genome-wide association studies now offer a host of emerging opportunities, as well as challenges, for discovering the genetic etiology of alcohol dependence and for unveiling new treatment strategies.
Streptococcus pneumoniae is an opportunistic pathogen that causes substantial illness and death among children worldwide. The genetic backgrounds of pneumococci that cause infection versus asymptomatic carriage vary substantially. To determine the evolutionary mechanisms of opportunistic pathogenicity, we conducted a genomic surveillance study in China. We collected 783 S. pneumoniae isolates from infected and asymptomatic children. By using a 2-stage genomewide association study process, we compared genomic differences between infection and carriage isolates to address genomic variation associated with pathogenicity. We identified 8 consensus k-mers associated with adherence, antimicrobial resistance, and immune modulation, which were unevenly distributed in the infection isolates. Classification accuracy of the best k-mer predictor for S. pneumoniae infection was good, giving a simple target for predicting pathogenic isolates. Our findings suggest that S. pneumoniae pathogenicity is complex and multifactorial, and we provide genetic evidence for precise targeted interventions.
The genetics of aging in the yeast Saccharomyces cerevisiae has involved the manipulation of individual genes in laboratory strains. We have instituted a quantitative genetic analysis of the yeast replicative lifespan by sampling the natural genetic variation in a wild yeast isolate. Haploid segregants from a cross between a common laboratory strain (S288c) and a clinically derived strain (YJM145) were subjected to quantitative trait locus (QTL) analysis, using 3048 molecular markers across the genome. Five significant, replicative lifespan QTL were identified. Among them, QTL 1 on chromosome IV has the largest effect and contains SIR2, whose product differs by five amino acids in the parental strains. Reciprocal gene swap experiments showed that this gene is responsible for the majority of the effect of this QTL on lifespan. The QTL with the second-largest effect on longevity was QTL 5 on chromosome XII, and the bulk of the underlying genomic sequence contains multiple copies (100-150) of the rDNA. Substitution of the rDNA clusters of the parental strains indicated that they play a predominant role in the effect of this QTL on longevity. This effect does not appear to simply be a function of extrachromosomal ribosomal DNA circle production. The results support an interaction between SIR2 and the rDNA locus, which does not completely explain the effect of these loci on longevity. This study provides a glimpse of the complex genetic architecture of replicative lifespan in yeast and of the potential role of genetic variation hitherto unsampled in the laboratory.
Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet.
Individuals who live to 85 and beyond without developing major age-related diseases may achieve this, in part, by lacking disease susceptibility factors, or by possessing resistance factors that enhance their ability to avoid disease and prolong lifespan. Healthy aging is a complex phenotype likely to be affected by both genetic and environmental factors. We sequenced 24 candidate healthy aging genes in DNA samples from 47 healthy individuals aged eighty-five years or older (the 'oldest-old'), to characterize genetic variation that is present in this exceptional group. These healthy seniors were never diagnosed with cancer, cardiovascular disease, pulmonary disease, diabetes, or Alzheimer disease. We re-sequenced all exons, intron-exon boundaries and selected conserved non-coding sequences of candidate genes involved in aging-related processes, including dietary restriction (PPARG, PPARGC1A, SIRT1, SIRT3, UCP2, UCP3), metabolism (IGF1R, APOB, SCD), autophagy (BECN1, FRAP1), stem cell activation (NOTCH1, DLL1), tumor suppression (TP53, CDKN2A, ING1), DNA methylation (TRDMT1, DNMT3A, DNMT3B) Progeria syndromes (LMNA, ZMPSTE24, KL) and stress response (CRYAB, HSPB2). We detected 935 variants, including 848 single nucleotide polymorphisms (SNPs) and 87 insertion or deletions; 41% (385) were not recorded in dbSNP. This study is the first to present a comprehensive analysis of genetic variation in aging-related candidate genes in healthy oldest-old. These variants and especially our novel polymorphisms are valuable resources to test for genetic association in models of disease susceptibility or resistance. In addition, we propose an innovative tagSNP selection strategy that combines variants identified through gene re-sequencing- and HapMap-derived SNPs.
How genetic and phenotypic variation are maintained has long been one of the fundamental questions in population and quantitative genetics. A variety of factors have been implicated to explain the maintenance of genetic variation in some contexts (e.g. balancing selection), but the potential role of epigenetic regulation to influence population dynamics has been understudied. It is well recognized that epigenetic regulation, including histone methylation, small RNA expression, and DNA methylation, helps to define differences between cell types and facilitate phenotypic plasticity. In recent years, empirical studies have shown the potential for epigenetic regulation to also be heritable for at least a few generations without selection, raising the possibility that differences in epigenetic regulation can act alongside genetic variation to shape evolutionary trajectories. Heritable differences in epigenetic regulation that arise spontaneously are termed "epimutations." Epimutations differ from genetic mutations in 2 key ways-they occur at a higher rate and the loci at which they occur often revert back to their original state within a few generations. Here, we present an extension of the standard population genetic model with selection to incorporate epigenetic variation arising via epimutation. Our model assumes a diploid, sexually reproducing population with random mating. In addition to spontaneous genetic mutation, we included parameters for spontaneous epimutation and back-epimutation, allowing for 4 potential epialleles at a single locus (2 genetic alleles, each with 2 epigenetic states), each of which affect fitness. We then analyzed the conditions under which stable epialleles were maintained. Our results show that highly reversible epialleles can be maintained in long-term equilibrium under neutral conditions in a manner that depends on the epimutation and back-epimutation rates, which we term epimutation-back-epimutation equilibrium. On the other hand, epialleles that compensate for deleterious mutations cause deviations from the expectations of mutation-selection balance by a simple factor that depends on the epimutation and back-epimutation rates. We also numerically analyze several sets of fitness parameters for which large deviations from mutation-selection balance occur. Together, these results demonstrate that transient epigenetic regulation may be an important factor in the maintenance of both epigenetic and genetic variation in populations.
TogoVar ( https://togovar.org ) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.
To examine whether cultivation reduced genetic variation in the important Chinese medicinal plant Rheum tanguticum, the levels and distribution of genetic variation were investigated using ISSR markers. Fifty-eight R. tanguticum individuals from five cultivated populations were studied. Thirteen primers were used and a total of 320 DNA bands were scored. High levels of genetic diversity were detected in cultivated R. tanguticum (PPB = 82.19, H = 0.2498, HB = 0.3231, I = 0.3812) and could be explained by the outcrossing system, as well as long-lived and human-mediated seed exchanges. Analysis of molecular variance (AMOVA) showed that more genetic variation was found within populations (76.1%) than among them (23.9%). This was supported by the coefficient of gene differentiation (Gst = 0.2742) and Bayesian analysis (θ B = 0.1963). The Mantel test revealed no significant correlation between genetic and geographic distances among populations (r = 0.1176, p = 0.3686). UPGMA showed that the five cultivated populations were separated into three clusters, which was in good accordance with the results provided by the Bayesian software STRUCTURE (K = 3). A short domestication history and no artificial selection may be an effective way of maintaining and conserving the gene pools of wild R. tanguticum.
Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across ~ 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.
Studies have demonstrated that natural variation in the expression level of genes at baseline is extensive, and the determinants of this variation can be mapped by a genetic-linkage approach. In this study, we used lymphoblastoid cells to explore the variation in radiation-induced transcriptional changes. We found that, among normal individuals, there is extensive variation in transcriptional response to radiation exposure. By studying monozygotic twins, we demonstrated that there is evidence of a heritable component to this variation. The postradiation variation in the expression level of several genes, including the ferredoxin reductase gene (FDXR) and the cyclin-dependent kinase inhibitor 1A gene (CDKN1A), is significantly greater (P<.001) among twin pairs than within twin pairs. The induction of FDXR by radiation showed a bimodal distribution. Our findings have important implications for understanding the genetic basis of radiation response, which has remained largely unknown due to the lack of family material needed for genetic studies. Our approach, which uses expression phenotypes in cell lines, allows us to expose cells from family members to radiation. Similar study design can be applied to dissect the genetic basis of other complex human traits.
Variation at the ABO locus was one of the earliest sources of data in the study of human population identity and history, and to this day remains widely genotyped due to its importance in blood and tissue transfusions. Here, we look at ABO blood type variants in our archaic relatives: Neanderthals and Denisovans. Our goal is to understand the genetic landscape of the ABO gene in archaic humans, and how it relates to modern human ABO variation. We found two Neanderthal variants of the O allele in the Siberian Neanderthals (O1 and O2), one of these variants is shared with an European Neanderthal, who is a heterozygote for this O1 variant and a rare cis-AB variant. The Denisovan individual is heterozygous for two variants of the O1 allele, functionally similar to variants found widely in modern humans. Perhaps more surprisingly, the O2 allele variant found in Siberian Neanderthals can be found at low frequencies in modern Europeans and Southeast Asians, and the O1 allele variant found in Siberian and European Neanderthal is also found at very low frequency in modern East Asians. Our genetic distance analyses suggest both alleles survive in modern humans due to inbreeding with Neanderthals. We find that the sequence backgrounds of the surviving Neanderthal-like O alleles in modern humans retain a higher sequence divergence than other surviving Neanderthal genome fragments, supporting a view of balancing selection operating in the Neanderthal ABO alleles by retaining highly diverse haplotypes compared with portions of the genome evolving neutrally.
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.
P2X7 is a nonselective cation channel activated by extracellular ATP. P2X7 activation contributes to the proinflammatory response to injury or bacterial invasion and mediates apoptosis. Recently, P2X7 function has been linked to chronic inflammatory and neuropathic pain. P2X7 may contribute to pain modulation both by effects on peripheral tissue injury underlying clinical pain states, and through alterations in central nervous system processing, as suggested by animal models. To further test its role in pain sensitivity, we examined whether variation within the P2RX7 gene, which encodes the P2X7 receptor, was associated with experimentally induced pain in human patients. Experimental pain was assessed in Tromsø 6, a longitudinal and cross-sectional population-based study (N = 3016), and the BrePainGen cohort, consisting of patients who underwent breast cancer surgery (N = 831). For both cohorts, experimental pain intensity and tolerance were assessed with the cold-pressor test. In addition, multisite chronic pain was assessed in Tromsø 6 and pain intensity 1 week after surgery was assessed in BrePainGen. We tested whether the single-nucleotide polymorphism rs7958311, previously implicated in clinical pain, was associated with experimental and clinical pain phenotypes. In addition, we examined effects of single-nucleotide polymorphisms rs208294 and rs208296, for which previous results have been equivocal. Rs7958311 was associated with experimental pain intensity in the meta-analysis of both cohorts. Significant associations were also found for multisite pain and postoperative pain. Our results strengthen the existing evidence and suggest that P2X7 and genetic variation in the P2RX7-gene may be involved in the modulation of human pain sensitivity.
Genomic DNA replicates in a choreographed temporal order that impacts the distribution of mutations along the genome. We show here that DNA replication timing is shaped by genetic polymorphisms that act in cis upon megabase-scale DNA segments. In genome sequences from proliferating cells, read depth along chromosomes reflected DNA replication activity in those cells. We used this relationship to analyze variation in replication timing among 161 individuals sequenced by the 1000 Genomes Project. Genome-wide association of replication timing with genetic variation identified 16 loci at which inherited alleles associate with replication timing. We call these "replication timing quantitative trait loci" (rtQTLs). rtQTLs involved the differential use of replication origins, exhibited allele-specific effects on replication timing, and associated with gene expression variation at megabase scales. Our results show replication timing to be shaped by genetic polymorphism and identify a means by which inherited polymorphism regulates the mutability of nearby sequences.
Over 98% of our genome is non-coding and is now recognised to have a major role in orchestrating the tissue specific and stimulus inducible gene expression pattern which underpins our wellbeing and mental health. The non-coding genome responds functionally to our environment at all levels, encompassing the span from psychological to physiological challenge. The gene expression pattern, termed the transcriptome, ultimately gives us our neurochemistry. Therefore a major modulator of mental wellbeing is how our genes are regulated in response to life experiences. Superimposed on the aforementioned non-coding DNA framework is a vast body of genetic variation in the elements that control response to challenges. These differences, termed polymorphisms, allow for a differential response from a specific DNA element to the same challenge thus potentially allowing 'individuality' in the modulation of our transcriptome. This review will focus on a fundamental mechanism defining our psychological and psychiatric wellbeing, namely how genetic variation can be correlated with differential gene expression in response to specific challenges, thus resulting in altered neurochemistry which consequently may shape behaviour.
Glycophorins are transmembrane proteins of red blood cells (RBCs), heavily glycosylated on their external-facing surface. In humans, there are four glycophorin proteins, glycophorins A, B, C and D. Glycophorins A and B are encoded by two similar genes GYPA and GYPB, and glycophorin C and glycophorin D are encoded by a single gene, GYPC. The exact function of glycophorins remains unclear. However, given their abundance on the surface of RBCs, it is likely that they serve as a substrate for glycosylation, giving the RBC a negatively charged, complex glycan "coat". GYPB and GYPE (a closely related pseudogene) were generated from GYPA by two duplication events involving a 120-kb genomic segment between 10 and 15 million years ago. Non-allelic homologous recombination between these 120-kb repeats generates a variety of duplication alleles and deletion alleles, which have been systematically catalogued from genomic sequence data. One allele, called DUP4, encodes the Dantu NE blood type and is strongly protective against malaria as it alters the surface tension of the RBC membrane. Glycophorins interact with other infectious pathogens, including viruses, as well as the malarial parasite Plasmodium falciparum, but the role of glycophorin variation in mediating the effects of these pathogens remains underexplored.
Individual differences in sensitivity to insulin contribute to disease susceptibility including diabetes and metabolic syndrome. Cellular responses to insulin are well studied. However, which steps in these response pathways differ across individuals remains largely unknown. Such knowledge is needed to guide more precise therapeutic interventions. Here, we studied insulin response and found extensive individual variation in the activation of key signaling factors, including ERK whose induction differs by more than 20-fold among our subjects. This variation in kinase activity is propagated to differences in downstream gene expression response to insulin. By genetic analysis, we identified cis-acting DNA variants that influence signaling response, which in turn affects downstream changes in gene expression and cellular phenotypes, such as protein translation and cell proliferation. These findings show that polymorphic differences in signal transduction contribute to individual variation in insulin response, and suggest kinase modulators as promising therapeutics for diseases characterized by insulin resistance.
St. Louis encephalitis virus (SLEV) has been regularly isolated throughout the Americas since 1933. Previous phylogenetic studies involving 62 isolates have defined seven major lineages (I-VII), further divided into 14 clades. In this study, 28 strains isolated in Texas in 1991 and 2001-2003, and three older, previously unsequenced strains from Jamaica and California were sequenced over the envelope protein gene. The inclusion of these new sequences, and others published since 2001, has allowed better delineation of the previously published SLEV lineages, in particular the clades of lineage II. Phylogenetic analysis of 106 isolates identified 13 clades. All 1991 and 2001-2003 isolates from Nueces, Jefferson and Harris Counties (Texas Gulf Coast) group in clade IIB with other isolates from these counties isolated during the 1980s and 1990s. This lack of evidence for introduction of novel strains into the Texas Gulf Coast over a long period of time is consistent with overwintering of SLEV in this region. Two El Paso isolates, both from 2002, group in clade VA with recent Californian isolates from 1998-2001 and some South American strains with a broad temporal range. Overall, these data are consistent with multiple introductions of SLEV from South America into North America, and provide support for the hypothesis that in most situations, SLEV circulates within a locality, with occasional incursions from other areas. Finally, SLEV has much lower nucleotide (10.1 %) and amino acid variation (2.8 %) than other members of the Japanese encephalitis virus complex (maximum variation 24.6 % nucleotide and 11.8 % amino acid).
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