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PubMed2001

Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice.

Genetic heterogeneity underlies many phenotypic variations observed in circadian rhythmicity. Continuous distributions in measures of circadian behavior observed among multiple inbred strains of mice suggest that the inherent contributions to variability are polygenic in nature. To identify genetic loci that underlie this complex behavior, we have carried out a genome-wide complex trait analysis in 196 (C57BL/6J X BALB/cJ)F(2) hybrid mice. We have characterized variation in this panel of F(2) mice among five circadian phenotypes: free-running circadian period, phase angle of entrainment, amplitude of the circadian rhythm, circadian activity level, and dissociation of rhythmicity. Our genetic analyses of these phenotypes have led to the identification of 14 loci having significant effects on this behavior, including significant main effect loci that contribute to three of these phenotypic measures: period, phase, and amplitude. We describe an additional locus detection method, genome-wide genetic interaction analysis, developed to identify locus pairs that may interact epistatically to significantly affect phenotype. Using this analysis, we identified two additional pairs of loci that have significant effects on dissociation and activity level; we also detected interaction effects in loci contributing to differences of period, phase, and amplitude. Although single gene mutations can affect circadian rhythms, the analysis of interstrain variants demonstrates that significant genetic complexity underlies this behavior. Importantly, most of the loci that we have detected by these methods map to locations that differ from the nine known clock genes, indicating the presence of additional clock-relevant genes in the mammalian circadian system. These data demonstrate the analytical value of both genome-wide complex trait and epistatic interaction analyses in further understanding complex phenotypes, and point to promising approaches for genetic analysis of such phenotypes in other mammals, including humans.

Animals↗
PubMed2003

Set association analysis of SNP case-control and microarray data.

Common heritable diseases ("complex traits") are assumed to be due to multiple underlying susceptibility genes. While genetic mapping methods for Mendelian disorders have been very successful, the search for genes underlying complex traits has been difficult and often disappointing. One of the reasons may be that most current gene-mapping approaches are still based on conventional methodology of testing one or a few SNPs at a time. Here, we demonstrate a simple strategy that allows for the joint analysis of multiple disease-associated SNPs in different genomic regions. Our set-association method combines information over SNPs by forming sums of relevant single-marker statistics. As previously hypothesized, we show here that this approach successfully addresses the "curse of dimensionality" problem--too many variables should be estimated with a comparatively small number of observations. We also report results of simulation studies showing that our method furnishes unbiased and accurate significance levels. Power calculations demonstrate good power even in the presence of large numbers of nondisease associated SNPs. We extended our method to microarray expression data, where expression levels for large numbers of genes should be compared between two tissue types. In applications to such data, our approach turned out to be highly efficient.

Case-Control Studies↗
PubMed2004

An algorithm for molecular dissection of tumor progression.

The volumetric growth of tumor cells as a function of time is most often likely to be a complex trait, controlled by the combined influences of multiple genes and environmental influences. Genetic mapping has proven to be a powerful tool for detecting and identifying specific genes affecting complex traits, i.e., quantitative trait loci (QTL), based on polymorphic markers. In this article, we present a novel statistical model for genetic mapping of QTL governing tumor growth trajectories in humans. In principle, this model is a combination of functional mapping proposed to map function-valued traits and linkage disequilibrium mapping designed to provide high resolution mapping of QTL by making use of recombination events created at a historic time. We implement an EM-simplex hybrid algorithm for parameter estimation, in which a closed-form solution for the EM algorithm is derived to estimate the population genetic parameters of QTL including the allele frequencies and the coefficient of linkage disequilibrium, and the simplex algorithm incorporated to estimate the curve parameters describing the dynamic changes of cancer cells for different QTL genotypes. Extensive simulations are performed to investigate the statistical properties of our model. Through a number of hypothesis tests, our model allows for cutting-edge studies aimed to decipher the genetic mechanisms underlying cancer growth, development and differentiation. The implications of our model in gene therapy for cancer research are discussed.

Algorithms↗
PubMed2005

Fine mapping of a major locus on chromosome 10 for exploratory and fear-like behavior in mice.

Advanced intercross lines (AIL) and interval-specific congenic strains (ISCS) were used to fine map previously coarsely defined quantitative trait loci (QTL) on Chromosomes 1, 10, and 19, influencing behaviors in the open Field (OF) and light-dark (LD) paradigms in mice. F12(A x B) AIL mice (N = 1130) were phenotyped, genotyped, and mapped. The ISCS were studied only in the telomeric Chromosome 10 region of interest, containing the exploratory and excitability QTL1 (Exq1). The Chromosome 10 Exq1 and Chromosome 19 Exq4 loci mapped robustly in the AIL. The most significant QTL findings (2.0 LOD score intervals; peak; LOD score) came from the TD15 and LD transitions traits, yielding estimated intervals of 2.2 cM for Exq1 (71.3-73.5 cM; peak 72.3 cM; LOD 11.9) and 9.0 cM for Exq4 (29.0-38.2 cM; peak 34 cM; LOD 4.2). The replicated QTLs on Chromosome 1 failed to map in this AIL population. The ISCS data confirmed Exq1 loci in general. However, the ISCS data were complex and less definitive for localizing the Exq1 loci. These exploratory and fear-like behaviors result from inheriting "many small things," namely, QTL explaining 2%-7% of the phenotypic variance. These results highlight the challenges of positionally cloning loci of small effect for complex traits. In particular, fine-mapping success may depend on the genetic architecture underlying complex traits.

Animals↗
PubMed2003

Quantitative analysis of bristle number in Drosophila mutants identifies genes involved in neural development.

BACKGROUND: The identification of the function of all genes that contribute to specific biological processes and complex traits is one of the major challenges in the postgenomic era. One approach is to employ forward genetic screens in genetically tractable model organisms. In Drosophila melanogaster, P element-mediated insertional mutagenesis is a versatile tool for the dissection of molecular pathways, and there is an ongoing effort to tag every gene with a P element insertion. However, the vast majority of P element insertion lines are viable and fertile as homozygotes and do not exhibit obvious phenotypic defects, perhaps because of the tendency for P elements to insert 5' of transcription units. Quantitative genetic analysis of subtle effects of P element mutations that have been induced in an isogenic background may be a highly efficient method for functional genome annotation. RESULTS: Here, we have tested the efficacy of this strategy by assessing the extent to which screening for quantitative effects of P elements on sensory bristle number can identify genes affecting neural development. We find that such quantitative screens uncover an unusually large number of genes that are known to function in neural development, as well as genes with yet uncharacterized effects on neural development, and novel loci. CONCLUSIONS: Our findings establish the use of quantitative trait analysis for functional genome annotation through forward genetics. Similar analyses of quantitative effects of P element insertions will facilitate our understanding of the genes affecting many other complex traits in Drosophila.

Animals↗
PubMed2001

Genome partitioning and whole-genome analysis.

Standard DNA marker-based approaches to mapping genes that influence complex traits typically consider a limited number of hypotheses. Most of these hypotheses concentrate on the effect of a single individual locus (or relatively few loci) on the trait of interest. Although of tremendous importance scientifically, such hypotheses do not accommodate the full range of genetic phenomena that may contribute to phenotypic expression. We present novel approaches to complex trait analysis that make as complete use of marker information as is possible. The proposed methodologies can be used to entertain a wide variety of hypotheses, including those that engage, for example, the contribution of a particular chromosome, genome-wide heterozygosity, and multiple genomic regions, to phenotypic expression. We consider a number of possible extensions of the proposed methods as well as their limitations. Although we discuss many methodological details in the context of quantitative trait locus mapping involving sampling units such as human pedigrees and hybrids resulting from crosses between inbred strains of model organisms, our procedures can be easily adapted to standard sibpair and other sampling unit-based designs. Ultimately, the proposed approaches not only have the potential to increase power to identify individual loci that harbor trait-influencing genes, but also present a framework for testing a number of hypotheses about the nature of the genetic determinants of phenotypes in general.

Alleles↗
PubMed2002

Backward Haplotype Transmission Association (BHTA) algorithm - a fast multiple-marker screening method.

The mapping of complex traits is one of the most important and central areas of human genetics today. Recent attention has been focused on genome scans using a large number of marker loci. Because complex traits are typically caused by multiple genes, the common approaches of mapping them by testing markers one after another fail to capture the substantial information of interactions among disease loci. Here we propose a backward haplotype transmission association (BHTA) algorithm to address this problem. The algorithm can administer a screening on any disease model when case-parent trio data are available. It identifies the important subset of an original larger marker set by eliminating the markers of least significance, one at a time, after a complete evaluation of its importance. In contrast with the existing methods, three major advantages emerge from this approach. First, it can be applied flexibly to arbitrary markers, regardless of their locations. Second, it takes into account haplotype information; it is more powerful in detecting the multifactorial traits in the presence of haplotypic association. Finally, the proposed method can potentially prove to be more efficient in future genomewide scans, in terms of greater accuracy of gene detection and substantially reduced number of tests required in scans. We illustrate the performance of the algorithm with several examples, including one real data set with 31 markers for a study on the Gilles de la Tourette syndrome. Detailed theoretical justifications are also included, which explains why the algorithm is likely to select the 'correct' markers.

Algorithms↗
PubMed2006

Dynamic genetic interactions determine odor-guided behavior in Drosophila melanogaster.

Understanding the genetic architecture of complex traits requires identification of the underlying genes and characterization of gene-by-gene and genotype-by-environment interactions. Behaviors that mediate interactions between organisms and their environment are complex traits expected to be especially sensitive to environmental conditions. Previous studies on the olfactory avoidance response of Drosophila melanogaster showed that the genetic architecture of this model behavior depends on epistatic networks of pleiotropic genes. We performed a screen of 1339 co-isogenic p[GT1]-element insertion lines to identify novel genes that contribute to odor-guided behavior and identified 55 candidate genes with known p[GT1]-element insertion sites. Characterization of the expression profiles of 10 p[GT1]-element insertion lines showed that the effects of the transposon insertions are often dependent on developmental stage and that hypomorphic mutations in developmental genes can elicit profound adult behavioral deficits. We assessed epistasis among these genes by constructing all possible double heterozygotes and measuring avoidance responses under two stimulus conditions. We observed enhancer and suppressor effects among subsets of these P-element-tagged genes, and surprisingly, epistatic interactions shifted with changes in the concentration of the olfactory stimulus. Our results show that the manifestation of epistatic networks dynamically changes with alterations in the environment.

Animals↗
PubMed2002

Generalized T2 test for genome association studies.

Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.

Algorithms↗
PubMed2004

Phenotypic characterization and genealogical tracing in an Afrikaner schizophrenia database.

Founder populations hold tremendous promise for mapping genes for complex traits, as they offer less genetic and environmental heterogeneity and greater potential for genealogical research. Not all founder populations are equally valuable, however. The Afrikaner population meets several criteria that make it an ideal population for mapping complex traits, including founding by a small number of initial founders that likely allowed for a relatively restricted set of mutations and a large current population size that allows identification of a sufficient number of cases. Here, we examine the potential to conduct genealogical research in this population and present initial results indicating that accurate genealogical tracing for up to 17 generations is feasible. We also examine the clinical similarities of schizophrenia cases diagnosed in South Africa and those diagnosed in other, heterogeneous populations, specifically the US. We find that, with regard to basic sample descriptors and cardinal symptoms of disease, the two populations are equivalent. It is, therefore, likely that results from our genetic study of schizophrenia will be applicable to other populations. Based on the results presented here, the history and current size of the population, as well as our previous analysis addressing the extent of background linkage disequilibrium (LD) in the Afrikaners, we conclude that the Afrikaner population is likely an appropriate founder population to map genes for schizophrenia using both linkage and LD approaches.

Adult↗
PubMed2003

Age-related hearing impairment (ARHI): environmental risk factors and genetic prospects.

This paper reviews what is known about the environmental risk factors and medical conditions that contribute to age-related hearing impairment (ARHI), and evaluates which analyses could be performed to identify genetic factors that are involved. Although hearing acuity declines with aging in everybody, the variation in hearing thresholds is large. Part of this variation can be explained by medical conditions, and by a different exposure to environmental factors. In particular, many studies have been dedicated to the influence of occupational noise on hearing. The importance of other environmental risk factors is less clear and often controversial. In contrast, almost nothing is known about the genetic compound of ARHI. Heritability estimates have shown that approximately half of the variance in ARHI is due to heritable factors, which indicates that ARHI is a complex trait, influenced by an interplay between genetics and environment. Although several genes for monogenic hearing impairment have been identified in mouse and man, no susceptibility genes for ARHI have been identified yet. The methodology to dissect the genetics of complex traits is still developing. Possible study designs to unravel the genetics of ARHI are discussed.

Age of Onset↗
PubMed2005

Identification of co-regulated transcripts affecting male body size in Drosophila.

Factor analysis is an analytic approach that describes the covariation among a set of genes through the estimation of 'factors', which may be, for example, transcription factors, microRNAs (miRNAs), and so on, by which the genes are co-regulated. Factor analysis gives a direct mechanism by which to relate gene networks to complex traits. Using simulated data, we found that factor analysis clearly identifies the number and structure of factors and outperforms hierarchical cluster analysis. Noise genes, genes that are not correlated with any factor, can be distinguished even when factor structure is complex. Applied to body size in Drosophila simulans, an evolutionarily important complex trait, a factor was directly associated with body size.

Animals↗
PubMed1999

Fat storage capacity in growth-selected and control mouse lines is associated with line-specific gene expression and plasma hormone levels.

OBJECTIVE: For a detailed understanding of the complex traits growth and fat storage, a dissection into single genetic entities is mandatory. Therefore, blood plasma concentrations of hormones and the expression of selected genes were measured in extremely differentiated mouse lines. Genes were selected as candidates which might influence the complex traits body weight and fat accumulation, and which are located in chromosomal regions recently identified to affect trait differences between the lines. SUBJECTS AND MEASUREMENTS: The mouse lines were selected for high body weight (DU6), high carcass protein content (DU6P) and unselected controls (DUKs). In the selected lines DU6 and DU6P, mean body weights at the age of six weeks were about twice as high as the DUKs, whereas total fat weight was increased 2.2-fold in DU6 mice compared to DU6P and 3.2-fold in comparison to DUKs. Blood plasma concentrations of insulin-like growth factor 1 (IGF-1), growth hormone (GH), insulin and leptin, were measured in all lines at three weeks and at six weeks of age. Expression patterns of the genes encoding growth hormone (Gh), insulin-like growth factor 1 (Igf1), lipoprotein lipase (Lpl), glycerolphosphate dehydrogenase 1 (GDC-1), and adipocyte protein 2 (Ap2) were analyzed by Northern blot hybridization. RESULTS: In line DU6, highly significant increased concentrations of insulin and leptin were observed at six weeks of age; at this stage, IGF-1 concentrations were elevated in the two selected lines compared to controls with maximal concentrations of IGF-1 and GH in DU6P. The amount of mRNA for GH in the pituitary gland, for Igf1 in the liver and for LPL in epididymal fat tissue was significantly elevated in the two selected lines compared to controls at the age of three weeks, but not at six weeks. IGF-1 and GDC-1 mRNA concentrations were significantly higher in the DU6 mice than in the DU6P (P < 0.01) and the DUKs (P < 0.001) mice examined at both ages. CONCLUSIONS: The results prove line-specific concentrations of the analyzed hormones and the transcription amounts of Gh, Igf1, GDC-1 and Lpl. The measured differences are either direct genetic effects or secondary changes, resulting from different food consumption.

Adipocytes↗
PubMed2006

Genome scan for loci predisposing to anxiety disorders using a novel multivariate approach: strong evidence for a chromosome 4 risk locus.

We conducted a 10-centimorgan linkage autosomal genome scan in a set of 19 extended American pedigrees (219 subjects) ascertained through probands with panic disorder. Several anxiety disorders--including social phobia, agoraphobia, and simple phobia--in addition to panic disorder segregate in these families. In previous studies of this sample, linkage analyses were based separately on each of the individual categorical affection diagnoses. Given the substantial comorbidity between anxiety disorders and their probable shared genetic liability, it is clear that this method discards a considerable amount of information. In this article, we propose a new approach that considers panic disorder, simple phobia, social phobia, and agoraphobia as expressions of the same multivariate, putatively genetically influenced trait. We applied the most powerful multipoint Haseman-Elston method, using the grade of membership score generated from a fuzzy clustering of these phenotypes as the dependent variable in Haseman-Elston regression. One region on chromosome 4q31-q34, at marker D4S413 (with multipoint and single-point nominal P values < .00001), showed strong evidence of linkage (genomewide significance at P<.05). The same region is known to be the site of a neuropeptide Y receptor gene, NPY1R (4q31-q32), that was recently connected to anxiolytic-like effects in rats. Several other regions on four chromosomes (4q21.21-22.3, 5q14.2-14.3, 8p23.1, and 14q22.3-23.3) met criteria for suggestive linkage (multipoint nominal P values < .01). Family-by-family analysis did not show any strong evidence of heterogeneity. Our findings support the notion that the major anxiety disorders, including phobias and panic disorder, are complex traits that share at least one susceptibility locus. This method could be applied to other complex traits for which shared genetic-liability factors are thought to be important, such as substance dependencies.

Anxiety↗
PubMed2006

Place of genotyping and phenotyping in understanding and potentially modifying outcomes in peritoneal dialysis patients.

With the landmark publication of the human genome sequence and its subsequent division into haplotype blocks, the characterization of genetic variations is becoming a feasible approach to study both the pathophysiology and risk factors of complex traits. A number of strategies are available today for identifying candidate genes or polymorphisms associated with pertinent phenotypes. For Mendelian diseases with high penetrance owing to mutations in a single gene, such as polycystic kidney disease, linkage studies have been very successful in mapping the disease loci owing to the availability of families with multiple affected members. In contrast to monogenic conditions, complex diseases such as end-stage renal disease (ESRD) and complex traits such as individual variations in membrane transport and complications during the course of peritoneal dialysis (PD) therapy have a number of competing determinants and inhibitors, both genetic and environmental. Current results reflect this complexity, with few studies showing a large effect of any single risk factor on survival or outcome on PD. However, these studies have so far been small (less than 500 patients) and have not utilized bioinformatics or novel technologies (e.g., multiplex genotyping equipment). In the following review, we outline current approaches for using genetic data in clinical studies as well as highlight some of the most promising results in ESRD patients, particularly those on PD.

Genetic Linkage↗
PubMed2002

Classical twin studies and beyond.

Twin studies have been a valuable source of information about the genetic basis of complex traits. To maximize the potential of twin studies, large, worldwide registers of data on twins and their relatives have been established. Here, we provide an overview of the current resources for twin research. These can be used to obtain insights into the genetic epidemiology of complex traits and diseases, to study the interaction of genotype with sex, age and lifestyle factors, and to study the causes of co-morbidity between traits and diseases. Because of their design, these registers offer unique opportunities for selected sampling for quantitative trait loci linkage and association studies.

Female↗
PubMed2026

A spatiotemporal resolution to genetic redundancy: MIR164 diversification coordinates development and metabolism in Brassica.

Whole-genome duplication (WGD) events create genetic redundancy, posing the evolutionary challenge of how paralogs escape functional overlap to drive innovation. Here, we demonstrate that the MIR164 family in Brassica oleracea resolves this redundancy through spatiotemporal niche partitioning. Following WGD, the family expanded to eight members, which subsequently underwent divergent selection-some preserved under purifying selection, while others showed signals of positive selection. This led to expression divergence, with Bol-MIR164a1 emerging as a key universally expressed paralog. CRISPR-Cas9 mutagenesis of Bol-MIR164a1 revealed its essential role in coordinating two pivotal traits: leaf serration and leaf coloration. Mutants exhibited enhanced leaf serration due to spatial deregulation of CUC2 at organ boundaries, concurrently with yellow-green leaves and elevated flavonoid accumulation. We mechanistically linked the metabolic phenotype to direct transactivation of the anthocyanidin reductase (ANR) promoter by NAC100, alongside its upregulation of chlorophyll catabolism genes. Our findings establish a paradigm in which spatial segregation of target gene expression domains enables a single, widely expressed miRNA paralog to resolve genetic redundancy by independently orchestrating distinct regulatory programs. This provides a fundamental framework for understanding complex trait evolution in polyploids. This allows a single miRNA locus to independently orchestrate both morphological patterning and metabolic programming, providing a fundamental framework for understanding complex trait evolution in polyploid crops.

MicroRNAs↗
PubMed2001

Genome screening for linkage disequilibrium in a Costa Rican sample of patients with bipolar-I disorder: a follow-up study on chromosome 18.

Linkage disequilibrium (LD) methods offer great promise for mapping complex traits, but have thus far been applied sparingly. In this paper we describe an LD mapping study of severe bipolar disorder (BP-I) in the genetically isolated population of the Central Valley of Costa Rica. This study provides the first complete screen of a chromosome for a complex trait using LD mapping and presents the first application of a new LD mapping statistic (ancestral haplotype reconstruction (AHR)) that evaluates haplotype sharing among affected individuals. The results of this chromosome-wide analysis are instructive for genome-wide LD mapping in isolated populations. Furthermore, the analysis continues to support a possible BP-I locus on 18pter, suggested by previous analyses in this population. Evidence for a possible BP-I locus on 18q12.2 is also described.

Bipolar Disorder↗