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At least 883 records · Page 49Linked to original sources
PubMed2000

Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach.

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

Animals↗
PubMed2006

On locating multiple interacting quantitative trait loci in intercross designs.

A modified version (mBIC) of the Bayesian Information Criterion (BIC) has been previously proposed for backcross designs to locate multiple interacting quantitative trait loci. In this article, we extend the method to intercross designs. We also propose two modifications of the mBIC. First we investigate a two-stage procedure in the spirit of empirical Bayes methods involving an adaptive (i.e., data-based) choice of the penalty. The purpose of the second modification is to increase the power of detecting epistasis effects at loci where main effects have already been detected. We investigate the proposed methods by computer simulations under a wide range of realistic genetic models, with nonequidistant marker spacings and missing data. In the case of large intermarker distances we use imputations according to Haley and Knott regression to reduce the distance between searched positions to not more than 10 cM. Haley and Knott regression is also used to handle missing data. The simulation study as well as real data analyses demonstrates good properties of the proposed method of QTL detection.

Algorithms↗
PubMed2004

Quantitative trait loci affecting natural variation in Drosophila longevity.

Limited life span and senescence are universal phenomena, controlled by genetic and environmental factors whose interactions both limit life span and generate variation in life span between individuals, populations and species. To understand the genetic architecture of longevity it is necessary to know what loci affect variation in life span, what are the allelic effects at these loci and what molecular polymorphisms define quantitative trait locus (QTL) alleles. Here, we used quantitative complementation tests to determine whether genes that regulate longevity also contribute to naturally occurring variation in Drosophila life span. Inbred strains derived from a natural population were crossed to stocks containing null mutations (m) or deficiencies (Df) uncovering the candidate genes, maintained over a Balancer (Bal) chromosome. We measured the life span of the resulting F(1) genotypes, +(i)/m (Df) and +(i)/Bal, where +(i) denotes one of the i natural alleles. Failure of the QTL alleles to complement the candidate gene mutation is indicated by a significant cross (mutant versus wild-type allele of the candidate gene) by inbred line interaction term from analysis of variance of life span. Failure to complement indicates a genetic interaction between the candidate gene allele and the naturally occurring life span QTL, and implicates the candidate gene as potential cause of variation in longevity. Of the 16 candidate regions and genes tested, Df(2L)c17, Df(3L)Ly, Df(3L)AC1 and Df(3R)e-BS2 showed significant failure to complement wild-type alleles in both sexes, and an Alcohol dehydrogenase mutant failed to complement in females. Several genes that regulate life span (e.g., Superoxide dismutase, Catalase, and rosy) complemented the life span effects of wild-derived alleles, suggesting little natural variation affecting longevity at these loci, at least in this sample of alleles. Quantitative complementation tests are therefore useful for identifying QTL contributing to segregating genetic variation in life span in nature.

Aging↗
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↗
PubMed1998

Quantitative trait locus mapping in dairy cattle by means of selective milk DNA pooling using dinucleotide microsatellite markers: analysis of milk protein percentage.

"Selective DNA pooling" accomplishes quantitative trait locus (QTL) mapping through densitometric estimates of marker allele frequencies in pooled DNA samples of phenotypically extreme individuals. With poly(TG) microsatellites, such estimates are confounded by "shadow" ("stutter") bands. A correction procedure was developed on the basis of an observed linear regression between shadow band intensity and allele TG repeat number. Using this procedure, a selective DNA pooling study with respect to milk protein percentage was implemented in Israel-Holstein dairy cattle. Pools were prepared from milk samples of high and low daughters of each of seven sires and genotyped with respect to 11 markers. Highly significant associations with milk protein percentage were found for 5 of the markers; 4 of these markers confirmed previous reports. Selective DNA pooling accessed 80.6 and 48.3%, respectively, of the information that would have been available through individual selective genotyping or total population genotyping. In effect, the statistical power of 45,600 individual genotypings was obtained from 328 pool genotypings. This methodology can make genome-wide mapping of QTL accessible to moderately sized breeding organizations.

Animals↗
PubMed2005

Two quantitative trait loci for prepulse inhibition of startle identified on mouse chromosome 16 using chromosome substitution strains.

Prepulse inhibition (PPI) of acoustic startle is a genetically complex quantitative phenotype of considerable medical interest due to its impairment in psychiatric disorders such as schizophrenia. To identify quantitative trait loci (QTL) involved in mouse PPI, we studied mouse chromosome substitution strains (CSS) that each carry a homologous chromosome pair from the A/J inbred strain on a host C57BL/6J inbred strain background. We determined that the chromosome 16 substitution strain has elevated PPI compared to C57BL/6J (P = 1.6 x 10(-11)), indicating that chromosome 16 carries one or more PPI genes. QTL mapping using 87 F(2) intercross progeny identified two significant chromosome 16 loci with LODs of 3.9 and 4.7 (significance threshold LOD is 2.3). The QTL were each highly significant independently and do not appear to interact. Sequence variation between B6 and A/J was used to identify strong candidate genes in the QTL regions, some of which have known neuronal functions. In conclusion, we used mouse CSS to rapidly and efficiently identify two significant QTL for PPI on mouse chromosome 16. The regions contain a limited number of strong biological candidate genes that are potential risk genes for psychiatric disorders in which patients have PPI impairments.

Animals↗
PubMed2004

Dissecting quantitative trait loci into opposite blood pressure effects on Dahl rat chromosome 8 by congenic strains.

OBJECTIVE: Our previous linkage analyses showed that there was likely a quantitative trait locus (QTL) for blood pressure (BP) on chromosome 8 (Chr 8) in the strain comparison between the Dahl salt-sensitive (S) and the Lewis (LEW) rats. The current work is to delineate the chromosome interval harboring this QTL by using congenic strains with different chromosome substitutions. METHODS: Two congenic strains were produced by replacing different segments of the S rats with the homologous segments of the LEW rats. A genome-wide marker screening was utilized to accelerate this process. The two strains generated are designated as C8S.L1 and C8S.L2, respectively. BPs of the rats were measured by telemetry. RESULTS: C8S.L1 showed a BP lower than that of S rats. In contrast, C8S.L2 did not have chromosome overlaps with C8S.L1, but unexpectedly, exhibited a BP-raising effect, higher than that of S rats. CONCLUSION: There are at least two QTLs present in a section of Chr 8 that possess opposite BP effects. The current congenic work reveals not only the presence of QTLs, but the complexity of QTLs on BP. The novel congenic strain with hypertension more severe than S provides a new model for studies in elucidating physiological mechanisms controlling BP.

Animals↗
PubMed2006

Distinct quantitative trait loci for kidney, cardiac, and aortic mass dissociated from and associated with blood pressure in Dahl congenic rats.

Blood pressure (BP) is largely determined by quantitative trait loci (QTLs) in Dahl salt-sensitive (DSS) rats. Little is known about QTLs controlling kidney (K), cardiac (C), and aortic (A) mass (i.e. Km, Cm, and Am, respectively) of DSS rats independent of BP. Their identification can facilitate our understanding of end organ damage. In this work, 36 congenic strains were employed to define QTLs for Km, Cm, and Am either independent of or associated with BP. Five new QTLs, i.e., KmQTLs, that influence Km independent of Cm, Am, and BP were defined. Four new CakmQTLs were defined for Cm, Am, and Km independent of BP. Among them, the CakmC10QTL1 interval contained 13 genes and undefined loci, and none was known to influence the phenotypes in question, paving the way for a novel gene discovery. Among 17 individual QTLs for BP, 14 also affected Cm, Km, and Am, i.e., they are BpcakmQTLs. In contrast, one BpQTL had no effect on Cm, Am, and Kam. Therefore, BP and Cm, Am, and Km have distinct and shared genetic determinants. The discovery of individual Km and Cakm QTLs will likely facilitate the identification of mechanisms underlying renal, cardiac, and/or aortic hypertrophy independent of hypertension.

Animals↗
PubMed1999

Congenic substitution mapping excludes Sa as a candidate gene locus for a blood pressure quantitative trait locus on rat chromosome 1.

Previously, linkage analysis in several experimental crosses between hypertensive rat strains and their contrasting reference strains have identified a major quantitative trait locus (QTL) for blood pressure on rat chromosome 1 (Chr 1) spanning the Sa gene locus. In this study, we report the further dissection of this Chr 1 blood pressure QTL with congenic substitution mapping. To address whether the Sa gene represents a candidate gene for the Chr 1 blood pressure QTL, congenic strains were developed by introgressing high blood pressure QTL alleles from the stroke-prone spontaneously hypertensive rat (SHRSP) into the normotensive Wistar-Kyoto (WKY-1) reference strain. Congenic animals carrying a chromosomal segment from stroke-prone spontaneously hypertensive rats between genetic markers Mt1pa and D1Rat200 (including the Sa gene locus) show a significant increase in basal systolic and diastolic blood pressure compared with their normotensive Wistar-Kyoto progenitors (P<0.001, respectively), whereas congenic animals carrying a subfragment of this Chr 1 region defined by markers Mt1pa and D1Rat57 (also spanning the Sa gene) do not show elevated basal blood pressure levels (P=0.83 and P=0.9, respectively). Similar results were obtained for NaCl-induced blood pressure values. Thus, the blood pressure QTL on Chr 1 is located centromeric to the Sa gene locus in a region that is syntenic to human chromosome 11p15.4-p15.3. This region excludes the Sa as a blood pressure-elevating candidate gene locus on the basis of congenic substitution mapping approaches.

Animals↗
PubMed2004

Quantitative trait loci that determine lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and CAST/Ei inbred mice.

To identify genetic determinants of lipoprotein levels, we are performing quantitative trait locus (QTL) analysis on a series of mouse intercrosses in a "daisy chain" experimental design, to increase the power of detecting QTL and to identify common variants that should segregate in multiple intercrosses. In this study, we intercrossed strains CAST/Ei and 129S1/SvImJ, determined HDL, total, and non-HDL cholesterol levels, and performed QTL mapping using Pseudomarker software. For HDL cholesterol, we identified two significant QTL on chromosome (Chr) 1 (Hdlq5, 82 cM, 60-100 cM) and Chr 4 (Hdlq10, 20 cM, 10-30 cM). For total cholesterol, we identified three significant QTL on Chr 1 (Chol7, 74 cM, 65-80 cM), Chr 4 (Chol8, 12 cM, 0-30 cM), and Chr 17 (Chol9, 54 cM, 20-60 cM). For non-HDL cholesterol, we identified significant QTL on Chr 8 (Nhdlq1, 34 cM, 20-60 cM) and Chr X (Nhdlq2, 6 cM, 0-18 cM). Hdlq10 was the only QTL detected in two intercrosses involving strain CAST/Ei. Hdlq5, Hdlq10, Nhdlq1, and two suggestive QTL at D7Mit246 and D15Mit115 coincided with orthologous human lipoprotein QTL. Our analysis furthers the knowledge of the genetic control of lipoprotein levels and points to the importance of Hdlq10, which was detected repeatedly in multiple studies.

ATP Binding Cassette Transporter 1↗
PubMed1999

Disequilibrium mapping of a quantitative-trait locus in an expanding population.

Linkage disequilibrium (LD) mapping can be successful if there is strong nonrandom association between marker alleles and an allele affecting a trait of interest. The principles of LD mapping of dichotomous traits are well understood, but less is known about LD mapping of a quantitative-trait locus (QTL). It is shown in this report that selective genotyping can increase the power to detect and map a rare allele of large effect at a QTL. Two statistical tests of the association between an allele and a quantitative character are proposed. These tests are approximately independent, so information from them can be combined. Analytic theory is developed to show that these two tests are effective in detecting the presence of a low-frequency allele with a relatively large effect on the character when the QTL is either already a candidate locus or closely linked to a marker locus that is in strong LD with the QTL. The latter situation is expected in a rapidly growing population in which the allele of large effect was present initially in one copy. Therefore, the proposed tests are useful under the same conditions as those for successful LD mapping of a dichotomous trait or disease. Simulations show that, for detection of the presence of a QTL, these tests are more powerful than a simple t-test. The tests also provide a basis for defining a measure of association, gamma, between a low-frequency allele at a putative QTL and a low-frequency allele at a marker locus.

Genetics, Population↗
PubMed2005

Detection of two quantitative trait loci for resistance to ascochyta blight in an intra-specific cross of chickpea (Cicer arietinum L.): development of SCAR markers associated with resistance.

Two quantitative trait loci (QTLs), (QTL(AR1) and QTL(AR2)) associated with resistance to ascochyta blight, caused by Ascochyta rabiei, have been identified in a recombinant inbred line population derived from a cross of kabulixdesi chickpea. The population was evaluated in two cropping seasons under field conditions and the QTLs were found to be located in two different linkage groups (LG4a and LG4b). LG4b was saturated with RAPD markers and four of them associated with resistance were sequenced to give sequence characterized amplified regions (SCARs) that segregated with QTL(AR2). This QTL explained 21% of the total phenotypic variation. However, QTL(AR1), located in LG4a, explained around 34% of the total phenotypic variation in reaction to ascochyta blight when scored in the second cropping season. This LG4a region only includes a few markers, the flower colour locus (B/b), STMS GAA47, a RAPD marker and an inter-simple-sequence-repeat and corresponds with a previously reported QTL. From the four SCARs tagging QTL(AR2), SCAR (SCY17(590)) was co-dominant, and the other three were dominant. All SCARs segregated in a 1:1 (presence:absence) ratio and the scoring co-segregated with their respective RAPD markers. QTL(AR2) on LG4b was mapped in a highly saturated genomic region covering a genetic distance of 0.8 cM with a cluster of nine markers (three SCARs, two sequence-tagged microsatellite sites (STMS) and four RAPDs). Two of the four SCARs showed significant alignment with genes or proteins related to disease resistance in other species and one of them (SCK13(603)) was sited in the highly saturated region linked to QTL(AR2). STMS TA72 and TA146 located in LG4b were described in previous maps where QTL for blight resistance were also localized in both inter and intraspecific crosses. These findings may improve the precision of molecular breeding for QTL(AR2) as they will allow the choice of as much polymorphism as possible in any population and could be the starting point for finding a candidate resistant gene for ascochyta blight resistance in chickpea.

Analysis of Variance↗
PubMed2005

Detection of quantitative trait loci influencing conformation traits and calving ease in Holstein-Friesian cattle.

An extension of our previous genome scan of a North American Holstein-Friesian population was conducted to identify quantitative trait loci (QTL) affecting conformation traits. Resource families consisted of 1404 sons of 10 elite sires. Genome coverage was estimated to be 2713.5 cM (90%) for 406 markers using a granddaughter design. Regression interval mapping was used to detect QTL affecting 22 conformation traits, including body, udder, feet and legs, and dairy conformation as well as calving ease. Analysis of the families jointly identified 41 chromosome-wise significant QTL influencing conformation traits and 3 significant QTL influencing calving ease on 20 chromosomes. The false discovery rate method was used to account for multiple testing and 3/4 of the suggestive and 5/6 of significant QTL should be real effects. Fourteen of the 44 QTL were significant at the genome-wise level. Comparison of these results with other published reports identifies common QTL affecting conformation traits. Regions on 10 chromosomes appear to affect multiple traits, including conformation, milk production, and somatic cell score, within these particular US Holstein families. Additional work is needed to determine the precise locations of the QTL and select positional candidate genes influencing these traits.

Animals↗
PubMed2001

Multiple quantitative trait locus analysis of bovine chromosome 6 in the Israeli Holstein population by a daughter design.

Nine Israeli Holstein sire families with 2978 daughters were analyzed for quantitative trait loci effects on chromosome 6 for five milk production traits by a daughter design. All animals were genotyped for 2 markers. The three families with significant effects were genotyped for up to 10 additional markers spanning positions 0-122 cM of BTA6. Two sires were segregating for a locus affecting protein and fat percentage near position 55 cM with an estimated substitution effect of 0.18% protein, which is equivalent to one phenotypic standard deviation. This locus was localized to a confidence interval of 4 cM. One of these sires was also heterozygous for a locus affecting milk, fat, and protein production near the centromere. The hypothesis of two segregating loci was verified by multiple regression analysis. A third sire was heterozygous for a locus affecting milk and protein percentage near the telomeric end of the chromosome. Possible candidates for the major quantitative gene near position 55 cM were determined by comparative mapping. IBSP and SSP1 were used as anchors for the orthologous region on human chromosome 4. Twelve genes were detected within a 2-Mbp sequence. None of these genes have been previously associated with lactogenesis.

Animals↗
PubMed2004

Validation of quantitative trait loci for Ascochyta blight resistance in pea ( Pisum sativum L.), using populations from two crosses.

Resistance to Ascochyta blight of pea was genetically characterized by mapping quantitative trait loci (QTLs) using two crosses, 3147-A26 (A26, partially resistant) x cultivar Rovar (susceptible) and 3148-A88 (A88, partially resistant) x Rovar, with the aim of developing an increased understanding of the genetics of resistance and of identifying linked molecular markers that may be used to develop resistant germplasm. Molecular linkage maps for both crosses were aligned so that the results of QTL mapping could be compared. Ascochyta blight disease severity in response to natural epidemics was measured in field trials conducted in Western Australia and New Zealand. Eleven putative QTLs for Ascochyta blight resistance were identified from the A26 x Rovar population and 14 putative QTLs from the A88 x Rovar population. Six QTLs were associated with the same genomic regions in both populations. These QTLs reside on linkage groups II, III, IV, V, and VII (two QTLs). The severity of Ascochyta blight disease symptoms on pea increases during field epidemics as plants mature; therefore, QTLs for plant reproductive maturity were mapped. Six QTLs were detected for plant maturity in the A26 x Rovar population, while five plant maturity QTLs were mapped in the A88 x Rovar population. QTLs for plant maturity coincide with Ascochyta blight resistance QTLs in four genomic regions, on linkage groups II (two regions), III, and V. The plant maturity and Ascochyta blight resistance QTLs on III were linked in repulsion phase. Therefore, the coincidence of these QTLs may be explained by linkage of distinct loci for the two traits. The QTLs on linkage groups II and V were linked in coupling phase; therefore, linked QTLs for resistance and maturity may be present in these regions, or the Ascochyta blight resistance QTLs detected in these regions are the result of pleiotropic effects of plant-maturity genetic loci.

Ascomycota↗
PubMed1994

Quantitative trait loci mapping of three loci controlling morphine preference using inbred mouse strains.

Quantitative trait loci mapping was used to identify the chromosomal location of genes which contribute to oral morphine preference (in a two-bottle choice paradigm) of C57BL/6J mice, compared to DBA/2J mice. An F2 intercross of these two strains (606 mice) was phenotyped for morphine preference and those mice demonstrating extreme values for morphine consumption (the highest and lowest 7.7%) were genotyped for 157 murine microsatellite polymorphisms. Maximum likelihood methods revealed three loci on murine chromosomes 1, 6 and 10 which are responsible for nearly 85% of the genetic variance observed between the two parental strains.

Animals↗
PubMed1990

Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato.

Quantitative trait loci (QTLs) have been mapped to small intervals along the chromosomes of tomato (Lycopersicon esculentum), by a method we call substitution mapping. The size of the interval to which a QTL can be mapped is determined primarily by the number and spacing of previously mapped genetic markers in the region surrounding the QTL. We demonstrate the method using tomato genotypes carrying chromosomal segments from Lycopersicon chmielewskii, a wild relative of tomato with high soluble solids concentration but small fruit and low yield. Different L. chmielewskii chromosomal segments carrying a common restriction fragment length polymorphism were identified, and their regions of overlap determined using all available genetic markers. The effect of these chromosomal segments on soluble solids concentration, fruit mass, yield, and pH, was determined in the field. Many overlapping chromosomal segments had very different phenotypic effects, indicating QTLs affecting the phenotype(s) to lie in intervals of as little as 3 cM by which the segments differed. Some associations between different traits were attributed to close linkage between two or more QTLs, rather than pleiotropic effects of a single QTL: in such cases, recombination should separate desirable QTLs from genes with undesirable effects. The prominence of such trait associations in wide crosses appears partly due to infrequent reciprocal recombination between heterozygous chromosomal segments flanked by homozygous regions. Substitution mapping is particularly applicable to gene introgression from wild to domestic species, and generally useful in narrowing the gap between linkage mapping and physical mapping of QTLs.

Alleles↗
PubMed1993

Interval mapping of multiple quantitative trait loci.

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.

Chromosome Mapping↗