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PubMed2001

Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology.

Gregor Mendel was either clever or lucky enough to study traits of simple inheritance in his pea plants; however, many plant characters of interest to modern geneticists are decidedly complex. Understanding the genetic basis of such complex, or quantitative, traits requires a combination of modern molecular genetic techniques and powerful statistical methods. These approaches have begun to give us insight into understanding the evolution of complex traits both in crops and in wild plants.

Genome, Plant↗
PubMed1996

Mapping economic trait loci for somatic cell score in Holstein cattle using microsatellite markers and selective genotyping.

Marker-assisted selection (MAS) uses genetic marker genotypes to predict an animal's production potential and will provide additional selection information for progeny testing. With the discovery of highly polymorphic microsatellite markers, the tools now exist to begin the search for economic trait loci (ETL), which is the first step toward MAS. The objective of this study was to identify ETL for somatic cell score in an existing Holstein population. Using the granddaughter design, sons from seven grandsire families were genotyped with 20 autosomal microsatellites from five chromosomes (4, 8, 13, 17, 23), with an emphasis on chromosome 23, which is the location of the bovine major histocompatibility complex (BoLA). Selective genotyping was used to reduce the number of genotypes required, in which the 10 highest and 10 lowest sons from the phenotypic distribution curve were tested (140 sons in seven families). One marker (513), located near BoLA, showed evidence of an ETL in three of five polymorphic families. Additional sons were genotyped from the five families to estimate the effect and to compare selective and 'complete' genotyping. Both methods detected an ETL at marker 513, but in different families. This study provides evidence of the usefulness of microsatellite markers and the granddaughter design in the detection of ETL; however, additional markers need to be evaluated to determine the usefulness of selective genotyping. Based on the results from the 20 studied markers, the most likely position of a somatic cell score ETL lies near marker 513, located on chromosome 23.

Alleles↗
PubMed1993

Evolution of endothermy in fish: mapping physiological traits on a molecular phylogeny.

Mackerels, tunas, and billfishes (suborder Scombroidei and Teleostei) provide an ideal taxonomic context in which to examine the evolution of endothermy. Multiple origins and diverse strategies for endothermy exist among these fish. Here a molecular phylogeny of the Scombroidei has been determined by direct sequencing of a portion of the mitochondrial cytochrome b gene. The distribution of endothermic species within this proposed genealogy indicates that the ability to warm the brain and retina arose independently in three lineages, each time in association with a movement into colder water. This suggests that the evolution of cranial endothermy in fish was selected in order to permit thermal niche expansion and not selected for increased aerobic capacity.

Animals↗
PubMed1996

Quantitative trait locus mapping of human blood pressure to a genetic region at or near the lipoprotein lipase gene locus on chromosome 8p22.

Resistance to insulin-mediated glucose disposal is a common finding in patients with non-insulin-dependent diabetes mellitus (NIDDM), as well as in nondiabetic individuals with hypertension. In an effort to identify the generic loci responsible for variations in blood pressure in individuals at increased risk of insulin resistance, we studied the distribution of blood pressure in 48 Taiwanese families with NIDDM and conducted quantitative sib-pair linkage analysis with candidate loci for insulin resistance, lipid metabolism, and blood pressure control. We found no evidence for linkage of the angiotensin converting enzyme locus on chromosome 17, nor the angiotensinogen and renin loci on chromosome 1, with either systolic or diastolic blood pressures. In contrast, we obtained significant evidence for linkage or systolic blood pressure, but not diastolic blood pressure, to a genetic region at or near the lipoprotein lipase (LPL) locus on the short arm of chromosome 8 (P = 0.002, n = 125 sib-pairs, for the haplotype generated from two simple sequence repeat markers within the LPL gene). Further strengthening this linkage observation, two flanking marker loci for LPL locus, D8S261 (9 cM telomeric to LPL locus) and D8S282 (3 cM centromeric to LPL locus), also showed evidence for linkage with systolic blood pressure (P = 0.02 and 0.0002 for D8S261 and D8S282, respectively). Two additional centromeric markers (D8S133, 5 cM from LPL locus, and NEFL, 11 cM from LPL locus) yielded significant P values of 0.01 and 0.001, respectively. Allelic variation around the LPL gene locus accounted for as much as 52-73% of the total interindividual variation in systolic blood pressure levels in this data set. Thus, we have identified a genetic locus at or near the LPL gene locus which contributes to the variation of systolic blood pressure levels in nondiabetic family members at high risk for insulin resistance and NIDDM.

Adult↗
PubMedDate not supplied

Mapping quantitative trait loci affecting fatness and breast muscle weight in meat-type chicken lines divergently selected on abdominal fatness.

Quantitative trait loci (QTL) for abdominal fatness and breast muscle weight were investigated in a three-generation design performed by inter-crossing two experimental meat-type chicken lines that were divergently selected on abdominal fatness. A total of 585 F2 male offspring from 5 F1 sires and 38 F1 dams were recorded at 8 weeks of age for live body, abdominal fat and breast muscle weights. One hundred-twenty nine microsatellite markers, evenly located throughout the genome and heterozygous for most of the F1 sires, were used for genotyping the F2 birds. In each sire family, those offspring exhibiting the most extreme values for each trait were genotyped. Multipoint QTL analyses using maximum likelihood methods were performed for abdominal fat and breast muscle weights, which were corrected for the effects of 8-week body weight, dam and hatching group. Isolated markers were assessed by analyses of variance. Two significant QTL were identified on chromosomes 1 and 5 with effects of about one within-family residual standard deviation. One breast muscle QTL was identified on GGA1 with an effect of 2.0 within-family residual standard deviation.

Abdominal Fat↗
PubMed2003

Mapping quantitative trait loci for vertebral trabecular bone volume fraction and microarchitecture in mice.

UNLABELLED: BMD, which reflects both cortical and cancellous bone, has been shown to be highly heritable; however, little is known about the specific genetic factors regulating trabecular bone. Genome-wide linkage analysis of vertebral trabecular bone traits in 914 adult female mice from the F2 intercross of C57BL/6J and C3H/HeJ inbred strains revealed a pattern of genetic regulation derived from 13 autosomes, with 5-13 QTLs associated with each of the traits. Ultimately, identification of genes that regulate trabecular bone traits may yield important information regarding mechanisms that regulate mechanical integrity of the skeleton. INTRODUCTION: Both cortical and cancellous bone influence the mechanical integrity of the skeleton, with the relative contribution of each varying with skeletal site. Whereas areal BMD, which reflects both cortical and cancellous bone, has been shown to be highly heritable, little is known about the genetic determinants of trabecular bone density and architecture. MATERIALS AND METHODS: To identify heritable determinants of vertebral trabecular bone traits, we evaluated the fifth lumbar vertebra from 914 adult female mice from the F2 intercross of C57BL/6J (B6) and C3H/HeJ (C3H) progenitor strains. High-resolution microCT was used to assess total volume (TV), bone volume (BV), bone volume fraction (BV/TV), trabecular thickness (Tb.Th), separation (Tb.Sp), and number (Tb.N) of the trabecular bone in the vertebral body in the progenitors (n = 8/strain) and female B6C3H-F2 progeny (n = 914). Genomic DNA from F2 progeny was screened for 118 PCR-based markers discriminating B6 and C3H alleles on all 19 autosomes. RESULTS AND CONCLUSIONS: Despite having a slightly larger trabecular bone compartment, C3H progenitors had dramatically lower vertebral trabecular BV/TV (-53%) and Tb.N (-40%) and higher Tb.Sp (71%) compared with B6 progenitors (p < 0.001 for all). Genome-wide quantitative trait analysis revealed a pattern of genetic regulation derived from 13 autosomes, with 5-13 quantitative trait loci (QTLs) associated with each of the vertebral trabecular bone traits, exhibiting adjusted LOD scores ranging from 3.1 to 14.4. The variance explained in the F2 population by each of the individual QTL after adjusting for contributions from other QTLs ranged from 0.8% to 5.9%. Taken together, the QTLs explained 22-33% of the variance of the vertebral traits in the F2 population. In conclusion, we observed a complex pattern of genetic regulation for vertebral trabecular bone volume fraction and microarchitecture using the F2 intercross of the C57BL/6J and C3H/HeJ inbred mouse strains and identified a number of QTLs, some of which are distinct from those that were previously identified for total femoral and vertebral BMD. Identification of genes that regulate trabecular bone traits may ultimately yield important information regarding the mechanisms that regulate the acquisition and maintenance of mechanical integrity of the skeleton.

Animals↗
PubMed2005

Mapping quantitative trait loci for cross-sectional geometry at the femoral neck.

UNLABELLED: A genome-wide linkage scan was performed in a sample of 79 multiplex pedigrees to identify genomic regions linked to femoral neck cross-sectional geometry. Potential quantitative trait loci were detected at several genomic regions, such as 10q26, 20p12-q12, and chromosome X. INTRODUCTION: Bone geometry is an important determinant of bone strength and osteoporotic fractures. Previous studies have shown that femoral neck cross-sectional geometric variables are under genetic controls. To identify genetic loci underlying variation in femoral neck cross-sectional geometry, we conducted a whole genome linkage scan for four femoral neck cross-sectional geometric variables in 79 multiplex white pedigrees. MATERIALS AND METHODS: A total of 1816 subjects from 79 pedigrees were genotyped with 451 microsatellite markers across the human genome. We performed linkage analyses on the entire data, as well as on men and women separately. RESULTS: Significant linkage evidence was identified at 10q26 for buckling ratio (LOD = 3.27) and Xp11 (LOD = 3.45) for cortical thickness. Chromosome region 20p12-q12 showed suggestive linkage with cross-sectional area (LOD = 2.33), cortical thickness (LOD = 2.09), and buckling ratio (LOD = 1.94). Sex-specific linkage analyses further supported the importance of 20p12-q12 for cortical thickness (LOD = 2.74 in females and LOD = 1.88 in males) and buckling ratio (LOD = 5.00 in females and LOD = 3.18 in males). CONCLUSIONS: This study is the first genome-wide linkage scan searching for quantitative trait loci underlying femoral neck cross-sectional geometry in humans. The identification of the genes responsible for bone geometric variation will improve our knowledge of bone strength and aid in development of diagnostic approaches and interventions for osteoporotic fractures.

Absorptiometry, Photon↗
PubMed2003

[Mapping the trait controlled by two duplicate genes in the DH or RIL population].

While there is linkage between molecular marker and trait controlled by two duplicate genes in the DH or RIL population, the recombination rate (RR) between molecular marker and one gene controlling the above trait may be estimated by the maximum likelihood method. Moreover, the standard deviation of RR was also obtained in this paper. Finally, the results from Monte Carlo simulation with 3000 replications showed that the unbiasedness of RR for various sample size and RR was good, and the variation of the estimated value of RR decreased with the increase of sample size or RR.

English Abstract↗
PubMed1999

Multiple interval mapping for quantitative trait loci.

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from approximately 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed approximately 10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0. 3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).

Chromosome Mapping↗
PubMed1999

High-resolution mapping of quantitative trait loci in outbred mice.

Screening the whole genome of a cross between two inbred animal strains has proved to be a powerful method for detecting genetic loci underlying quantitative behavioural traits, but the level of resolution offered by quantitative trait loci (QTL) mapping is still too coarse to permit molecular cloning of the genetic determinants. To achieve high-resolution mapping, we used an outbred stock of mice for which the entire genealogy is known. The heterogeneous stock (HS) was established 30 years ago from an eight-way cross of C57BL/6, BALB/c, RIII, AKR, DBA/2, I, A/J and C3H inbred mouse strains. At the time of the experiment reported here, the HS mice were at generation 58, theoretically offering at least a 30-fold increase in resolution for QTL mapping compared with a backcross or an F2 intercross. Using the HS mice we have mapped a QTL influencing a psychological trait in mice to a 0.8-cM interval on chromosome 1. This method allows simultaneous fine mapping of multiple QTLs, as shown by our report of a second QTL on chromosome 12. The high resolution possible with this approach makes QTLs accessible to positional cloning.

Animals↗
PubMed1993

Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits.

Recent advances in molecular biology have provided geneticists with ever-increasing numbers of highly polymorphic genetic markers that have made possible linkage mapping of loci responsible for many human diseases. However, nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, we explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. We compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. We also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, we also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that we consider, we find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, we also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. We also discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models.

Chromosome Mapping↗
PubMed1989

Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.

Chromosome Mapping↗
PubMed2002

A statistical model for the genetic origin of allometric scaling laws in biology.

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.

Animals↗
PubMed1994

A sib-pair approach to interval mapping of quantitative trait loci.

An interval mapping procedure based on the sib-pair method of Haseman and Elston is developed, and simulation studies are carried out to explore its properties. The procedure is analogous to other interval mapping procedures used with experimental material, such as plants and animals, and yields very similar results in terms of the location and effect size of a quantitative trait locus (QTL). The procedure offers an advantage over the conventional Haseman and Elston approach, in terms of power, and provides useful information concerning the location of a QTL. Because of its simplicity, the method readily lends itself to the analysis of selected samples for increased power and the evaluation of multilocus models of complex phenotypes.

Alleles↗
PubMed2004

Joint mapping of quantitative trait Loci for multiple binary characters.

Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.

Algorithms↗
PubMed2006

Selective transcriptional profiling for trait-based eQTL mapping.

There is currently great interest in identifying genetic loci associated with the expression of one or more genes by combining methods for the mapping of quantitative trait loci (QTL) with microarray technology. Because of the high cost of microarrays, it is useful to develop strategies for selecting a subset of individuals for expression profiling. We describe a method of selective transcriptional profiling that uses available information on individual quantitative traits and marker data to choose an optimal set of individuals for measurement with microarrays. We show how to analyse the resulting data to identify genes whose expression is associated with a QTL for a traditional trait or any other locus. Examples are used to illustrate the method and highlight some challenges associated with the analysis of selected data.

Animals↗