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PubMed2001

Optimum study designs.

Because simplistic designs will lead to prohibitively large sample sizes, the optimization of genetic study designs is critical for successfully mapping genes for complex diseases. Creative designs are necessary for detecting and amplifying the usually weak signals for complex traits. Two important outcomes of a study design--power and resolution--are implicitly tied together by the principle of uncertainty. Overemphasis on either one may lead to suboptimal designs. To achieve optimality for a particular study, therefore, practical measures such as cost-effectiveness must be used to strike a balance between power and resolution. In this light, the myriad of factors involved in study design can be checked for their effects on the ultimate outcomes, and the popular existing designs can be sorted into building blocks that may be useful for particular situations. It is hoped that imaginative construction of novel designs using such building blocks will lead to enhanced efficiency in finding genes for complex human traits.

Case-Control Studies↗
PubMed2005

Epistasis in the expression of relevant traits in cassava (Manihot esculenta Crantz) for subhumid conditions.

There is limited knowledge on the inheritance of agronomic traits in cassava and the importance of epistasis for most crops. A nine-parent diallel study was conducted in subhumid environments. Thirty clones were obtained from each F1 cross. Each clone was represented by six plants, which were distributed in three replications at two locations. Therefore the same 30 genotypes of each F1 cross were planted in the three replications at the two locations. Analysis of variance suggested significant genetic effects for all variables analyzed (reaction to thrips, fresh root and foliage yields, harvest index, dry matter content, and root dry matter yield). Significant epistatic effects were observed for all variables, except harvest index. Dominance variance was always significant, except for dry matter content and dry matter yield. Additive variance was significant only for reaction to thrips. Results suggested that dominance plays an important role in complex traits such as root yield. The significance of epistasis can help us understand the difficulties of quantitative genetics models and QTLs in satisfactorily explaining phenotypic variation in traits with complex inheritance. Significant epistasis would justify the production of inbred parental lines to fix favorable allele combinations in the production of hybrid cassava cultivars.

Analysis of Variance↗
PubMed2026

Integrating GWAS and Transcriptome Analysis Identifies Candidate Genes for Kernel Starch Quality Traits in Maize.

Maize (Zea mays L.) starch quality is a complex trait with significant implications for grain processing and industrial applications. However, the genetic basis underlying starch quality, particularly for gelatinization and thermodynamic properties, remains poorly understood. In this study, we evaluated 12 starch quality traits, including seven gelatinization characteristics, four thermodynamic traits, and kernel starch content (KSC) in a diverse panel of 335 maize inbred lines. Considerable phenotypic variation was observed for all traits. A total of 228 quantitative trait loci (QTLs) were significantly associated with 12 starch quality traits through genome-wide association studies (GWAS). By integrating a dynamic transcriptome analysis of two maize inbred lines with contrasting starch quality, we identified 60 candidate genes. One gene, waxy1, encoding a starch synthase, was found to be associated with enthalpy of gelatinization (ΔHgel) and pasting temperature (Ptemp). Six variants in waxy1 contributed to natural variation in ΔHgel and Ptemp, and a cost-effective InDel and two PARMS-based molecular markers were developed and validated in 144 maize inbred lines, enabling efficient marker-assisted selection. Our findings provide key genes and molecular markers for high-quality maize breeding with improved starch properties.

Zea mays↗
PubMed2000

Epistatic quantitative trait loci for alcohol preference in mice.

Alcohol consumption is a complex trait, responding to the influence of various genes and environmental influences acting in a quantitative fashion. Various studies in alcohol consumption processes have identified quantitative trait locus (QTL) regions across the mouse genome that appear to contribute to this phenotype. The purpose of this study was to examine the influence of interactions between alleles at different loci, a phenomenon known as epistasis, on previously identified QTLs for alcohol consumption in mice. A multiple regression model was developed and applied to test for the significance of the interaction between two QTLs and to quantify this interaction. Our results indicate the presence of epistasis between loci on mouse chromosomes 2 and 3 accounting for 7-8% of the variation in alcohol preference, respectively.

Alcohol Drinking↗
PubMed2004

A genomewide search using an original pairwise sampling approach for large genealogies identifies a new locus for total and low-density lipoprotein cholesterol in two genetically differentiated isolates of Sardinia.

A powerful approach to mapping the genes for complex traits is to study isolated founder populations, in which genetic heterogeneity and environmental noise are likely to be reduced and in which extended genealogical data are often available. Using graph theory, we applied an approach that involved sampling from the large number of pairwise relationships present in an extended genealogy to reconstruct sets of subpedigrees that maximize the useful information for linkage mapping while minimizing calculation burden. We investigated, through simulation, the properties of the different sets in terms of bias in identity-by-descent (IBD) estimation and power decrease under various genetic models. We applied this approach to a small isolated population from Sardinia, the village of Talana, consisting of a unique large and complex pedigree, and performed a genomewide search through variance-components linkage analysis for serum lipid levels. We identified a region of significant linkage on chromosome 2 for total serum cholesterol and low-density lipoprotein (LDL) cholesterol. Through higher-density mapping, we obtained an increased linkage for both traits on 2q21.2-q24.1, with a LOD score of 4.3 for total serum cholesterol and of 3.9 for LDL cholesterol. A replication study was performed in an independent and larger set from a genetically differentiated isolated population of the same region of Sardinia, the village of Perdasdefogu. We obtained consistent linkage to the region for total serum cholesterol (LOD score 1.4) and LDL cholesterol (LOD score 2.2), with a level of concordance uncommon for complex traits, and refined the location of the quantitative-trait locus. Interestingly, the 2q21.1-22 region has also been linked to premature coronary heart disease in Finns, and, in the adjacent 2q14 region, significant linkage with triglycerides has been reported in Hutterites.

Cholesterol↗
PubMed1997

Empirical evaluation of genome scans for linkage of a quantitative trait associated with a complex disorder.

Nonparametric sib-pair analysis (Haseman-Elston) was used to search for evidence of linkage between a putative locus for a complex quantitative trait Q1 and genome-wide markers (367 markers from 10 chromosomes) for the first 100 replicates of nuclear family data. The characteristics of the statistically positive linkage results [the magnitude of p-values (p), the number of supporting flanking markers, and the percentage of positive replicates] were compared for true linkage (major and minor genes) and false positive evidence for linkage. Discriminant analysis was used to evaluate which characteristics of these statistically positive linkage results are good indicators to discriminate true linkage from false positive evidence for linkage. Sensitivity and false positive rates of several proposed criteria for linkage, as well as the criteria based on our results were evaluated. The relationship between the map location of the marker with the lowest p-value and the map location of the true underlying gene was also evaluated, which provided useful information for fine mapping and replication studies.

Age Distribution↗
PubMed2001

Evidence for a major gene influence on abdominal fat distribution: the Minnesota Breast Cancer Family Study.

Abdominal fat has been shown to be an important risk factor for many chronic conditions, including diabetes, heart disease, and breast cancer. The objective of this study was to provide evidence for a major gene influence on the ratio of waist to hip circumference (WHR), a measurement commonly used in large scale studies to indicate the presence of abdominal fat. Segregation analysis was conducted on three subsets of families from the Minnesota Breast Cancer Family Study. One analysis was conducted among families with WHR measurements on all women. Two additional analyses were conducted on subsets of women stratified on menopausal status. Multiple regression analysis was used to identify factors associated with WHR expressed as a continuous trait. Complex segregation analyses were performed on the continuous trait of WHR and the covariates identified in the regression analysis. In the analysis of all women, all hypotheses were rejected. Among premenopausal women, the environmental hypothesis with no heterogeneity between generations fit the data best (P = 0.85). However, among postmenopausal women, the requirements for conclusion of the presence of a major gene were met. All non-Mendelian hypotheses were rejected (P < 0.0001), but the additive hypothesis was not rejected (P = 0.19) and provided the best fit to the data. The putative major gene identified by this model accounted for 42% of total phenotypic variance in WHR among these postmenopausal women. The allele for high WHR had a frequency of 27%. These findings support the hypothesis that the distribution of abdominal fat in postmenopausal women is under genetic control.

Abdomen↗
PubMed2005

A rice quantitative trait locus for salt tolerance encodes a sodium transporter.

Many important agronomic traits in crop plants, including stress tolerance, are complex traits controlled by quantitative trait loci (QTLs). Isolation of these QTLs holds great promise to improve world agriculture but is a challenging task. We previously mapped a rice QTL, SKC1, that maintained K(+) homeostasis in the salt-tolerant variety under salt stress, consistent with the earlier finding that K(+) homeostasis is important in salt tolerance. To understand the molecular basis of this QTL, we isolated the SKC1 gene by map-based cloning and found that it encoded a member of HKT-type transporters. SKC1 is preferentially expressed in the parenchyma cells surrounding the xylem vessels. Voltage-clamp analysis showed that SKC1 protein functions as a Na(+)-selective transporter. Physiological analysis suggested that SKC1 is involved in regulating K(+)/Na(+) homeostasis under salt stress, providing a potential tool for improving salt tolerance in crops.

Base Sequence↗
PubMed2003

Progress in deciphering the genetics of multiple sclerosis.

PURPOSE OF REVIEW: Multiple sclerosis is the most common neurological disease affecting young adults. The aetiology is unknown, although many clues point to an autoimmune inflammatory nature. Family studies of multiple sclerosis have shown familial aggregation, and therefore suggest that the disease entails a genetic component that has been widely studied. Some of the studies from the extensive literature in the field of multiple sclerosis genetics published in the past year are discussed here. RECENT FINDINGS: A number of the recent publications considered in this review have reconfirmed the well-known association with the major histocompatibility complex as well as identifying that there are at least two important loci within this region. These findings add further complexity to the role of the major histocompatibility complex in multiple sclerosis. Links to other diseases have been few for multiple sclerosis, but the association with diabetes in the Sardinian population and, perhaps, a 'protective' effect of Down's syndrome can now be added. Numerous candidate genes for susceptibility and disease-modifying effect have also been studied in the literature, but with few replications. Associations with components of the endocrine and the neuro-endocrine system have also been considered in this review along with the potential value of microarray analysis. SUMMARY: Multiple sclerosis is a complex trait that is associated with the major histocompatibility complex, although the form of this association may not be as straightforward as previously thought. Recent findings raise the possibility of an association with haplotype blocks rather than with single alleles. The finding of allelic heterogeneity within the major histocompatibility complex, as with the Sardinians, adds an additional layer of complexity. Genome scans for this and other autoimmune diseases have often been notably disappointing despite many claims for linkages. The reasons for the difficulty may encompass locus heterogeneity, small effects and phenocopies, among others. A variety of attempts to study more restricted populations are in progress, including rare individual pedigrees with high recurrence risk.

Diabetes Mellitus↗
PubMed2000

Developing expressed sequence tags (ESTs) from polymorphic transcript-derived fragments (TDFs) in cassava (Manihot esculenta Crantz).

We applied the cDNA-AFLP (amplified fragment length polymorphism) technique to mRNA from the parents of a cassava (Manihot esculenta) genetic mapping population, and obtained more than 500 transcript-derived fragments (TDFs) that were unique in either parent. A subset of 50 TDFs were cloned and sequenced. Sequence alignment of the expressed sequence tags (ESTs) revealed mostly genes of unknown function. Six of the TDFs were mapped on to the cassava genetic map. We also demonstrated by genetic mapping of the TDFs, as RFLP (restriction fragment length polymorphism) markers, that TDFs are more polymorphic than random cDNAs. Generation of ESTs as differentially expressed sequences, in time or between different varieties, is proposed as a way of developing ESTs around specific traits for the candidate locus approach to mapping complex traits.

Chromosome Mapping↗
PubMed2000

Preface and overview: genetics of SLE; a sine qua non for identification.

Clinical manifestations of systemic lupus erythematosus (SLE) are extremely diverse and variable, mainly because SLE is a multi-factorial disease. Variable combinations of contributing genes at multiple loci in individual patients probably result in diverse disease phenotypes. Genes that predispose to SLE are undoubtedly related to key events in pathogenesis, and may involve a variety of genes in immune system. These genes are currently unidentified, mostly because of the complexity of multi-factorial inheritance. Recently, the application of the polymerase chain reaction and the availability of maps of microsatellites have facilitated a genome-wide scan to define the number and locations of genes for complex traits. However, extensive genetic heterogeneities and polymorphisms and complex modes of inheritance of disease phenotypes have delayed completion of a genome-wide analysis of susceptibility loci for human SLE. Since many SLE-susceptibility genes show low penetrance, several hundred affected sibpairs are assumed to be necessary to show linkages for many of the contributing loci. In this respect, studies of polymorphisms and functions of candidate genes suggested based on studies on murine models may contribute to studies on SLE patients and their relatives. Major genetic loci have been mostly identified in SLE-prone mouse strains. Nevertheless, identity of natures, functions and roles in the pathogenesis of SLE remains undetermined. Considering the possibility of clustering of susceptibility loci in a particular chromosomal interval, the final goal for identification of susceptibility genes will largely depend on the generation of mutant SLE-prone mice with homologous recombination of the potential target gene. Extensive reviews collected here are expected to form the basis for identification of target genes and for clarification of the genetic mechanisms underlying SLE.

Animals↗
PubMed2006

A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits.

Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.

Asthma↗
PubMed1999

Estimating linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations.

Positional cloning of gene(s) underlying a complex trait requires a high-resolution linkage map between the trait locus and genetic marker loci. Recent research has shown that this may be achieved through appropriately modeling and screening linkage disequilibrium between the candidate marker locus and the major trait locus. A quantitative genetics model was developed in the present study to estimate the coefficient of linkage disequilibrium between a polymorphic genetic marker locus and a locus underlying a quantitative trait as well as the relevant genetic parameters using the sample from randomly mating populations. Asymptotic covariances of the maximum-likelihood estimates of the parameters were formulated. Convergence of the EM-based statistical algorithm for calculating the maximum-likelihood estimates was confirmed and its utility to analyze practical data was exploited by use of extensive Monte-Carlo simulations. Appropriateness of calculating the asymptotic covariance matrix in the present model was investigated for three different approaches. Numerical analyses based on simulation data indicated that accurate estimation of the genetic parameters may be achieved if a sample size of 500 is used and if segregation at the trait locus explains not less than a quarter of phenotypic variation of the trait, but the study reveals difficulties in predicting the asymptotic variances of these maximum-likelihood estimates. A comparison was made between the statistical powers of the maximum-likelihood analysis and the previously proposed regression analysis for detecting the disequilibrium.

Genetic Markers↗
PubMed2003

Quantitative trait locus mapping for atherosclerosis susceptibility.

PURPOSE OF REVIEW: Atherosclerosis is a complex trait with both environmental and genetic aspects. Although some progress has been made in defining genes associated with atherosclerosis in humans, animal models have been useful in learning about pathways and genes involved in atherogenesis. This review describes an unbiased genetic mapping method called quantitative trait locus mapping and progress in using this method to identify genes that alter atherosclerosis susceptibility in mice. RECENT FINDINGS: Approximately 10 well defined genetic loci have been described that are associated with lesion severity in diet-induced or gene knockout mouse models of atherosclerosis. Recently, two of these genetic loci were narrowed considerably by analysis of genetic recombinants within these loci. In addition, a computational method to discover quantitative trait loci has been applied to atherosclerosis. However, none of the genes responsible for these atherosclerosis quantitative trait loci has been definitively identified. The recent completion of the mouse draft genome should facilitate the task of identifying these genes. SUMMARY: Quantitative trait locus mapping studies in mouse models of atherosclerosis have defined genetic regions that alter lesion severity. The identification of the responsible genes may lead to insights into the pathogenesis of atherosclerosis as well as to candidates for human genetic association studies.

Animals↗
PubMed2006

Four linked genes participate in controlling sporulation efficiency in budding yeast.

Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four "high" sporulation alleles are derived from the "low" sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one "QTL region" that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes.

Alleles↗
PubMed2003

Multilevel modeling for the analysis of longitudinal blood pressure data in the Framingham Heart Study pedigrees.

BACKGROUND: The data arising from a longitudinal familial study have a complex correlation structure that cannot be modeled using classical methods for the analysis of familial data at a single time point. METHODS: To fit the longitudinal systolic blood pressure (SBP) pedigree data arising from the Framingham Heart Study, we proposed to use multilevel modeling. That approach was used to distinguish multiple levels of information with individual repeated measurements (Level 1) being made within individuals (Level 2), and individuals clustered within pedigrees (Level 3). Residuals from the subject-specific and pedigree-specific regression models were summed both for the mean SBP and slope of SBP change over time, in order to define two new outcomes that were then used in a genome-wide linkage analysis. RESULTS: Evidence for linkage for the two outcomes (mean SBP and slope) was found in several chromosomal regions with a maximum LOD score of 3.6 on chromosome 8 and 3.5 on chromosome 17 for the mean SBP, and 2.5 on chromosome 1 for SBP slope. However, the linkage on chromosome 8 was only detected when the sample was restricted to subjects between age 25 and 75 and with at least four exams (Cohort 1) or 3 exams (Cohort 2). DISCUSSION: Multilevel modeling is a powerful approach to detect genes involved in complex traits when longitudinal data are available. It allows for complex hierarchical data structure to be taken into account and therefore, a better partitioning of random within-individual variation from other sources of variability (genetic or nongenetic).

Adult↗
PubMed1999

Quantitative trait loci approach to the genetics of sleep in recombinant inbred mice.

Sleep is a complex trait controlled by many genes, the environment, and probably by gene-environment interactions. Among different approaches to the genetics of sleep, analysis of quantitative traits (QTL) has the advantage of being able to detect, along with major genes, minor and/or modifier genes influencing different quantitative aspects of sleep. We have used QTL analysis in two different sets of recombinant inbred (RI) strains and sought for confirmation of several localizations in eight histocompatibility congenic strains. Several QTLs were identified which influenced the amount of vigilance states. In a first RI series (seven strains) the only QTLs identified were those affecting paradoxical sleep (PS), whereas analysis in a second RI series (25 strains) revealed QTLs influencing PS, slow-wave sleep, and total sleep. Among these, a single QTL on chromosome 5 was associated with all vigilance states, suggesting the presence of a major gene influencing a basic aspect of sleep amount. Search for candidate genes around the identified QTLs indicated several immune related genes that have been implicated in sleep regulation. Transgenic animals carrying loss-of-function and/or gain-of-function mutations affecting these candidate genes should confirm these findings.

Alleles↗
PubMed2002

Complexities in the genetic dissection of quantitative trait loci.

The analysis of complex traits, including those involved in many common diseases, has encountered significant difficulties, and, despite major efforts during the past decade, has had little success. Current advances in genomics, however, promise to change this. Recently, Steinmetz et al. used a new technique to produce the first complete quantitative trait locus (QTL) analysis of a complex trait from phenotype to gene to be published entirely in a single report. This marks a significant advance over previous QTL analyses, which took several years. The work exemplifies some of the complexities of QTL mapping and demonstrates a novel method to resolve the underlying genetic architecture of QTLs in yeast. Here we discuss this work in the general context of genetic dissection of complex traits and QTLs.

Chromosome Mapping↗