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PubMedDate not supplied

Recent advances in the genetics of osteoporosis.

It has been known for over 20 years that osteoporosis is highly influenced by genetic factors. Bone mineral density (BMD) has also been shown to be highly heritable. Other known risk factors for osteoporotic fractures such as reduced bone quality, femoral neck geometry and bone turnover are now also known to be heritable. Susceptibility to osteoporosis is mediated, in all likelihood, by multiple genes each having small effect. Different approaches are being used currently to identify the many genes responsible. These include linkage studies in man and experimental animals as well as candidate gene studies and alterations in gene expression. Linkage studies have identified multiple quantitative trait loci (QTL) for regulation of BMD and, with twin studies, have indicated that the effects of these loci are partly site-dependent and sex-specific. On the whole, the genes responsible for BMD regulation at these QTL have not yet been isolated. Most studies have used the candidate gene approach. The vitamin D receptor gene (VDR), the collagen type I alpha 1 gene (COLIA1) and estrogen receptor gene (ER) alpha have been most widely investigated and found to play a role in regulating BMD, but the effects are modest and together probably account for less than 5% of the heritable contribution to BMD. Genes may vary in their influence of particular intermediate phenotypes, and we now know that not all genes influencing BMD will be important in fracture. In addition, the study of other diseases such as osteoarthritis and metabolic bone syndromes may prove fruitful in highlighting genes which overlap to osteoporosis as well. As large scale genetic testing becomes more cost-effective, recent findings have illustrated the potential of novel approaches. These include combining large multi-national populations for candidate gene analysis, meta-analyses, DNA pooling studies and gene expression studies.

Animals↗

PubMed2026

Integrated analysis uncovers exogenous induction and molecular regulation of erinacine A accumulation in Hericium erinaceus.

Erinacine A, a cyathane-type diterpenoid mainly from Hericium erinaceus mycelia, exhibits prominent neurotrophic and neuroprotective activities, making it a promising candidate for managing neurodegenerative diseases. However, its low abundance and unclear genetic regulatory mechanisms hinder its application as a nutraceutical. This study aimed to decipher its regulatory mechanisms and enhance production. Four exogenous inducers were screened, with salicylic acid (SA) and ergosterol (ERG) significantly increasing erinacine A content by 62.21% and 146.70% at 20 days, respectively. Transcriptome and WGCNA of inducer-treated sample identified darkorange and magenta modules associated with erinacine A biosynthesis, with the eri gene cluster enriched in the darkorange module and eriG and eriF as hub genes. Forward genetic analysis via QTL mapping of the HeD127 dikaryon population revealed significant phenotypic variation in erinacine A content (0.341-13.085 mg/g) and identified two loci (erA-1 and erA-2) explaining 18.63% of phenotypic variation. Integrating these forward and reverse genetic analyses revealed that salicylic acid and ergosterol synergistically regulate core carbon metabolic pathways to augment acetyl-CoA supply for the mevalonate pathway, suppressed competitive metabolism, enhanced diterpene skeleton construction and structural modification. These results deepen our understanding of the genetic and molecular basis governing accumulation of erinacine A, and facilitate its application in neuroprotective pharmaceuticals.

Diterpenes↗

PubMed2006

Causal inference of regulator-target pairs by gene mapping of expression phenotypes.

BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. RESULTS: Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. CONCLUSION: By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.

Algorithms↗

PubMed2006

Functional mapping - how to map and study the genetic architecture of dynamic complex traits.

The development of any organism is a complex dynamic process that is controlled by a network of genes as well as by environmental factors. Traditional mapping approaches for analysing phenotypic data measured at a single time point are too simple to reveal the genetic control of developmental processes. A general statistical mapping framework, called functional mapping, has been proposed to characterize, in a single step, the quantitative trait loci (QTLs) or nucleotides (QTNs) that underlie a complex dynamic trait. Functional mapping estimates mathematical parameters that describe the developmental mechanisms of trait formation and expression for each QTL or QTN. The approach provides a useful quantitative and testable framework for assessing the interplay between gene actions or interactions and developmental changes.

Animals↗

PubMed2005

The genetic dissection of immune response using gene-expression studies and genome mapping.

Functional genomics has been applied to the genetic dissection of immune response in different ways: (1) experimental crosses between lines that differ in their (non-) specific immune response have been used to detect quantitative trait loci (QTL) underlying these differences. (2) The measurement of gene expression levels for thousands of genes using microarrays or oligonucleotide chips to identify differential expression with regard to antigen challenge: (a) before and after infection, (b) resistant versus susceptible lines, or (c) combinations of both. Interpretation of QTL results is hampered by the fact that confidence regions of the QTL are large and can contain hundreds of potential candidate genes for the QTL. At the same time, the microarray experiments tend to show large numbers of differentially expressed genes without identifying the relationships between these genes. In the recently proposed 'genetical genomics' framework, members of a segregating population are characterised for genome-wide molecular markers and for gene expression levels. This facilitates the mapping of expression-QTL (eQTL): loci in the genome that control the expression of genes. Initial applications of this approach are critically reviewed and potential applications of this approach with regard to immune response are presented.

Animals↗

PubMed2025

RcAP2L-RcAS1 complex modulates petal number in roses by targeting RcAGL80 promoter.

Double flower, which is one of the most important characteristics of ornamental plants, is closely related to their ornamental and commercial value. The double-flower trait in rose was mainly due to the increase in petal number caused by stamen petalization. However, the mechanism regulating petal number is not clear. In this study, the Rosa chinensis "Zhaiye Tengben Yuejihua" × R. chinensis "Old Blush" population was used for QTL detection and NGS-based BSA analysis to identify candidate genes related to petal number. It was found that RcAP2L and RcAS1 were highly expressed in double-flower rose, while RcAGL80 was highly expressed in single-flower rose. Silencing RcAP2L and RcAS1 reduced the petal number by inhibiting homeotic conversion of stamens to petals, separately. However, silencing RcAGL80 increased the petal number by promoting homeotic conversion of stamens to petals. The results of Y2H and BiFC assays showed that RcAP2L interacted with RcAS1. The dual-luciferase assay showed that RcAP2L was bound to the promoter of RcAGL80 and suppressed RcAGL80 transcription. In total, we found a new function of AS1 in specifying flower organ identity, and a new pathway for regulating the number of petals by RcAS1, RcAGL80, and RcAP2L, which provides new information for elucidating the mechanism of the formation of double flower in rose.

Rosa↗

PubMedDate not supplied

Effect of p53 alleles on placental weight.

The relationship between three p53 polymorphisms (BstUI and MspI RFLPs in exon 4 and intron 6, respectively, and a 16-bp duplication in intron 3) in placental tissue and placental weight was examined in an attempt to elucidate the effect of p53 alleles on non-malignant growth. Placental tissue is expressing the fetal genotype. Using the quantitative trait loci approach, allelic frequencies of the three p53 polymorphisms and ten alleles at other loci (ABO, PLAP, GC and ACP1) were compared for the high (> or = 700 g) and low (< 400 g) tails (+/- 1.4 SD) of the placental weight distribution in a Swedish sample of newborns. Significant associations were found in the three p53 polymorphisms examined but not for the other loci, suggesting that non-malignant cell growth may be influenced by polymorphic p53 variants. High placental weight was associated with increased frequencies of the 16-bp duplication (A2 allele), the codon 72 BstUI A1 (pro) allele and the MspI A1 allele. These three alleles were in strong linkage disequilibria, and high placental weight was therefore associated with the 2-1-1 haplotype. The fact that the strongest associations were found with intronic markers suggests linkage disequilibrium with growth-promoting sites at the p53 molecule as the most likely mechanism, although a direct functional involvement of the codon 72 pro/arg substitution in normal cell growth cannot be excluded.

Alleles↗

PubMed2004

A mechanistic model for genetic machinery of ontogenetic growth.

Two different genetic mechanisms can be proposed to explain variation in growth trajectories. The allelic sensitivity hypothesis states that growth trajectory is controlled by the time-dependent expression of alleles at the deterministic quantitative trait loci (dQTL) formed during embryogenesis. The gene regulation hypothesis states that the differentiation in growth process is due to the opportunistic quantitative trait loci (oQTL) through their mediation with new developmental signals. These two hypotheses of genetic control have been elucidated in the literature. Here, we propose a new statistical model for discerning these two mechanisms in the context of growth trajectories by integrating growth laws within a QTL-mapping framework. This model is developed within the maximum-likelihood context, implemented with a grid approach for estimating the genomic positions of the deterministic and opportunistic QTL and the simplex algorithm for estimating the growth curve parameters of the genotypes at these QTL and the parameters modeling the residual (co)variance matrix. Our model allows for extensive hypothesis tests for the genetic control of growth processes and developmental events by these two types of QTL. The application of this new model to an F(2) progeny in mice leads to the detection of deterministic and opportunistic QTL on chromosome 1 for mouse body mass growth. The estimates of QTL positions and effects from our model are broadly in agreement with those by traditional interval-mapping approaches. The implications of this model for biological and biomedical research are discussed.

Algorithms↗

PubMed2005

[Progress in porky genes and transcriptome and discussion of relative issues].

To date, research on molecular base of porky molecular development was mainly involved in muscle growth and meat quality. Some functional genes including Hal gene and RN gene and some QTLs controlling or associated with porky growth and quality were detected through candidate gene approach and genome-wide scanning. Genic transcriptome pertinent to porcine muscle and adipose also came into study. At the same time, these researches have befallen some shortcomings to some extent. Research from molecular quantitative genetics showed shortcomings that single gene was devilishly emphasized and co-expression pattern of multi-genes was ignored. Research applying transcriptome analysis tool also met two of limitations, one was the singleness of type of molecular experimental techniques, and another was that genes of muscle and adipose were artificially divided into unattached two parts. Thus, porky genes were explored by parallel genetics based on systemic views and techniques to specially reveal the interactional mechanism of porky genes respectively controlling muscle and adipose, which would be important issues of genes and genome researches on porky development in the near future.

Animals↗

PubMed2003

The genetics of obesity in Mexican Americans: the evidence from genome scanning efforts in the San Antonio family heart study.

Recent estimates indicate that approximately 18% of the population in the United States can be considered obese (defined as a body mass index [BMI] > or = 30), and this rate is even higher among ethnic populations such as Mexican Americans. This figure becomes very significant given the strong evidence for obesity as a major risk factor for a variety of chronic diseases including type 2 diabetes mellitus and coronary heart disease. The search for genes involved in the expression of obesity has been one of the focal points of the San Antonio Family Heart Study (SAFHS), a large, family-based study to examine the genetics of risk for atherosclerosis in Mexican Americans. To date, our genome scanning effort has reported two quantitative trait loci (QTLs) with pronounced effects on the expression of a variety of obesity--related phenotypes (e.g., leptin levels, fat mass, and BMI) located on chromosomes 2 and 8. We are currently working to further refine these signals and to identify the genes and allelic variants involved. Here, we summarize the latest results from our ongoing efforts to identify obesity genes in the San Antonio Family Heart Study.

Adult↗

PubMed2005

Genetic interactions between polymorphisms that affect gene expression in yeast.

Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs. Interacting loci have been identified in many organisms. However, the prevalence of interactions, and the nucleotide changes underlying them, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes--the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

Crosses, Genetic↗

PubMed1999

Mapping quantitative trait loci for complex binary traits in outbred populations.

Complex binary traits have a dichotomous phenotypic expression but do not show a simple Mendelian segregation ratio. These traits are considered to be jointly controlled by the actions of several genes and a random environmental effect. The binary phenotype and the underlying factor are assumed to be linked through a threshold model. The underlying factor, referred to as the liability, is treated as a regular but unobservable quantitative character. Mapping quantitative trait loci (QTL) can be performed directly on the liability. Methods of QTL mapping for the liability of a complex binary trait have been well developed in line-crossing experiments. However, such a method is not available in outbred populations which usually consist of many independent pedigrees (families). In this study, we develop a method to analyse jointly multiple families of an outbred population. The method is developed based on a fixed-model approach, i.e. the QTL effects, rather than the variance, are estimated and tested. After the test, the estimated effects are then converted into a single estimate of the QTL variance by taking into consideration errors in the estimated effects. The QTL effects and variance-covariance matrix of the estimates are obtained by a fast Fisher-scoring method. Monte Carlo simulations show that the method is not only powerful but also generates very accurate estimates of QTL variances.

Algorithms↗

PubMed2002

Functional genomics in rat models of hypertension: using differential expression and congenic strains to identify and evaluate candidate genes.

Hypertension is a leading contributor to cardiovascular diseases such as heart attack and stroke. Genetic and environmental factors contribute to the development of hypertension. Animal models have been developed to study the genetic contributions to blood pressure (BP) regulation and to identify chromosomal regions harboring candidate genes causative of differences in BP regulation (i.e., BP quantitative trait loci [QTL]). Advances in both mammalian genome projects and global gene expression analysis present opportunities to study functional genomics in these animal models. In this article, novel approaches for designing experiments and interpreting global gene expression data using the Dahl salt-sensitive hypertension rat model are presented. We describe two-step screening protocols that can be used to identify BP QTL candidate genes. Genetically determined expression differences are identified in the target organs of inbred strains of contrasting phenotype in the first screen. Expression patterns in a panel of congenic strains or expression differences stemming from gene x environment interactions are examined in the second screen. Chromosomal locations of these genes can then be examined to determine whether they map to BP QTL-containing regions. Another approach is to study the expression of genes identified from public databases to be located within BP QTL-containing congenic regions. Several candidate genes have been identified using these strategies.

Animals↗

PubMed1999

A genomic scan of porcine reproductive traits reveals possible quantitative trait loci (QTLs) for number of corpora lutea.

Reproductive traits have low heritabilities, are expressed in only one sex, and are not measurable until sexual maturity (Avalos and Smith, Anim Prod 44:153, 1987). Using traditional methods, selection for reproductive traits is relatively less effective than selecting for growth or carcass traits. Traits most affected by a small number of genes with major effects rather than many genes with small effects are most amenable to MAS. As part of our porcine genome scan to identify quantitative trait loci (QTLs) of economic importance in marker-assisted selective (MAS) breeding programs, we examined 8 reproductive and farrowing traits in the University of Illinois (UI) Meishan x Yorkshire Resource Family. Gilts were genotyped with 119 microsatellite markers (MS) with intervals averaging 24 cM over all 18 porcine autosomes. F-ratios supporting QTL location were calculated by the least squares regression method. Results suggestive of linkage at the 5% genome-wide level were observed for the number of stillborn piglets on Chromosome (Chr) 4 (SSC4) (p-value = 0.0001), corpora lutea on SSC8 (p-value = 0.00027), and gestation length on SSC9 (p-value = 0.00019). Results for additional loci relevant to litter size, number of corpora lutea on SSC15 and 7 (p-value = 0.0029 and 0.0028 at 107 and 150 cM, respectively), gestation length on SSC15 and 1 (p-value = 0.0017 and 0.0069 at 96 and 166 cM, respectively), uterine length on SSC7 and 5 (p-value = 0.0044 and 0.0075 at 148 and 1 cM, respectively) and piglets born per litter on SSC6 (p-value = 0.0075 at 102 cM), were not statistically significant at the 5% genome-wide level. Thus, the use of a linked marker to facilitate selection for reproductive traits has considerable potential. By using linked markers, selection can be applied to both sexes before sexual maturity, making genetic selection considerably more efficient and less costly.

Animals↗

PubMed2005

Combined genealogical, mapping, and expression approaches to identify spontaneously hypertensive rat hypertension candidate genes.

Allelic expression in genes has become recognized as a heritable trait by which phenotypes are generated. We have examined gene expression in the rat kidney using genome-wide microarray technology (Affymetrix). Gene expression was determined across 4 rat strains, 3 hypertensive spontaneously hypertensive rat (SHR) substrains (SHR-A3, SHR-B2, and SHR-C), and a normotensive strain (Wistar-Kyoto [WKY]). Expression measurements were made in multiple animals from all strains at 4 time points (4 weeks, 8 weeks, 12 weeks, and 18 weeks of age), covering the prehypertensive period in SHR (4 weeks), and the period of rapidly rising blood pressure (8 and 12 weeks) and of sustained hypertension (18 weeks). Regression analysis revealed a close relationship across all strains during the first 3 time points, after which SHR-A3 became a substantial outlier. SHR-B2 and SHR-C demonstrated a very close relationship in gene expression at all times but also showed increased differences compared with the other strains at 18 weeks of age. We identified genes that were consistently different in expression, comparing all SHR substrains at each time point with WKY. The resulting list of genes was compared with blood pressure quantitative trait loci reported for SHR to refine a number of genes consistently differentially expressed between SHR substrains and WKY, persistently differentially expressed across multiple time points, and located in SHR blood pressure-determinative regions of the genome. Genealogical relationships and SHR substrain intercrosses suggest that genes responsible for heritable hypertension in SHR are shared across SHR substrains. The present approach identifies a number of genes that may influence blood pressure in SHR by virtue of allelic effects on gene expression.

Animals↗

PubMed2006

High-resolution mapping of quantitative trait loci affecting increased life span in Drosophila melanogaster.

Limited life span and senescence are near-universal characteristics of eukaryotic organisms, controlled by many interacting quantitative trait loci (QTL) with individually small effects, whose expression is sensitive to the environment. Analyses of mutations in model organisms have shown that genes affecting stress resistance and metabolism affect life span across diverse taxa. However, there is considerable segregating variation for life span in nature, and relatively little is known about the genetic basis of this variation. Replicated lines of Drosophila that have evolved increased longevity as a correlated response to selection for postponed senescence are valuable resources for identifying QTL affecting naturally occurring variation in life span. Here, we used deficiency complementation mapping to identify at least 11 QTL on chromosome 3 that affect variation in life span between five old (O) lines selected for postponed senescence and their five base (B) population control lines. Most QTL were sex specific, and all but one affected multiple O lines. The latter observation is consistent with alleles at intermediate frequency in the base population contributing to the response to selection for postponed senescence. The QTL were mapped with high resolution and contained from 12 to 170 positional candidate genes.

Animals↗

PubMed2005

Methodological aspects of the genetic dissection of gene expression.

MOTIVATION: Dissection of the genetics underlying gene expression utilizes techniques from microarray analyses as well as quantitative trait loci (QTL) mapping. Available QLT mapping methods are not tailored for the highly automated analyses required to deal with the thousand of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression. RESULTS: The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as 'high confidence' QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. After extensive analyses of real data and additional simulated data (altogether > 300,000 genome scans) we make the following recommendations for detection of QTL for gene expression: (1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on repeatability of the trait and the number of replicates. (2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. (3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. (4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localization of gene-transcripts and QTL. (5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the Type I error. (6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarized in a possible strategy for mapping of QTL in a least squares framework. AVAILABILITY: The software used for this study is available on request from the authors.

Algorithms↗

PubMed2006

Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice.

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.

Animals↗