Quantitative trait loci that harbor genes regulating muscle size in (MRL/MPJ x SJL/J) F(2) mice

The genetic mechanisms that determine muscle size have not been elucidated, even though it is a key musculoskeletal parameter that reflects muscle strength. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F₂ intercross 7-week-old mice to identify quantitative trait loci (QTL) involved in the determination of muscle size. Significant QTL were identified for muscle size and body length. Muscle size (adjusted by body length) QTL were identified on chromosomes 7, 9, 11, 14 (two QTL) and 17, which together explained 19.2% of phenotypic variance in F₂ mice, while body length QTL were located on chromosome 2 (two QTL), 9, 11 and 17 which accounted for 28.3% of phenotypic variance in F₂ mice. Three significant epistatic interactions between different QTL positions from muscle size and body length were identified (P <0.01) on chromosomes 2, 9, 14 and 17, which explained 16.1% of the variance in F₂ mice. Electronic Publication     

Identification of putative QTL that underlie yield in interspecific soybean backcross populations

Glycine soja, the wild progenitor of soybean, is a potential source of useful genetic variation in soybean improvement. The objective of our study was to map quantitative trait loci (QTL) from G. soja that could improve the crop. Five populations of BC₂F₄-derived lines were developed using the Glycine max cultivar IA2008 as a recurrent parent and the G. soja plant introduction (PI) 468916 as a donor parent. There were between 57 and 112 BC₂F₄-derived lines in each population and a total of 468 lines for the five populations. The lines were evaluated with simple sequence repeat markers and in field tests for yield, maturity, plant height, and lodging. The field testing was done over 2 years and at two locations each year. Marker data were analyzed for linkage and combined with field data to identify QTL. Using an experimentwise significance threshold of P=0.05, four yield QTL were identified across environments on linkage groups C2, E, K, and M. For these yield QTL, the IA2008 marker allele was associated with significantly greater yield than the marker allele from G. soja. In addition, one lodging QTL, four maturity QTL, and five QTL for plant height were identified across environments. Of the 14 QTL identified, eight mapped to regions where QTL with similar effects were previously mapped. Many regions carrying the yield QTL were also significant for other traits, such as plant height and lodging. When the significance threshold was reduced and the data were analyzed with simple linear regression, four QTL with a positive allele for yield from G. soja were mapped. One epistatic interaction between two genetic regions was identified for yield using an experimentwise significance threshold of P=0.05. Additional research is needed to establish whether multiple trait associations are the result of pleiotropy or genetic linkage and to retest QTL with a positive effect from G. soja.Communicated by H.C. Becker     

Chromosome substitution strains: some quantitative considerations for genome scans and fine mapping

A chromosome substitution strain (CSS) is an inbred strain in which one chromosome has been substituted from a different inbred strain by repeated backcrossing. A complete CSS set has one strain representing each chromosome against a uniform background, thus allowing genome-wide scans to be carried out for quantitative trait loci (QTLs) influencing any trait of interest. A one-way ANOVA by strain is first carried out, followed by planned comparisons using Dunnetts method. A QTL is detected and mapped to a chromosome when a significant difference is observed in a background strain vs CSS comparison. The most efficient ratio of background to CSS mice in any one comparison is 4.5:1, and the threshold for p < .05 genome-wide significance is estimated to be p = .003 to .004, a much less stringent criterion than any other mammalian mapping population. The use of false discovery rates tends to further reduce threshold stringency. Comparisons are made to the widely used conventional F₂ intercross, and both advantages and disadvantages are noted. The proportion of the trait variance due to a QTL is often much larger than the same QTL in an F₂, and the number of generations to attain fine mapping is greatly reduced. To serve as guidelines for planning experiments, methods to estimate sample sizes for QTL detection are presented for the initial genome scan and for subsequent fine mapping.     

A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population

We performed a genome-wide QTL scan for production traits in a line cross between Duroc and Pietrain breeds of pigs, which included 585 F(2) progeny produced from 31 full-sib families genotyped with 106 informative microsatellites. A linkage map covering all 18 autosomes and spanning 1987 Kosambi cM was constructed. Thirty-five phenotypic traits including body weight, growth, carcass composition and meat quality traits were analysed using least square regression interval mapping. Twenty-four QTL exceeded the genome-wide significance threshold, while 47 QTL reached the suggestive threshold. These QTL were located at 28 genomic regions on 16 autosomal chromosomes and QTL in 11 regions were significant at the genome-wide level. A QTL affecting pH value in loin was detected on SSC1 between marker-interval S0312-S0113 with strong statistical support (P < 3.0 x 10(-14)); this QTL was also associated with meat colour and conductivity. QTL for carcass composition and average daily gain was also found on SSC1, suggesting multiple QTL. Seventeen genomic segments had only a single QTL that reached at least suggestive significance. Forty QTL exhibited additive inheritance whereas 31 QTL showed (over-) dominance effects. Two QTL for trait backfat thickness were detected on SSC2; a significant paternal effect was found for a QTL in the IGF2 region while another QTL in the middle of SSC2 showed Mendelian expression.     

How replicable are mRNA expression QTL?

Applying quantitative trait analysis methods to genome-wide microarray-derived mRNA expression phenotypes in segregating populations is a valuable tool in the attempt to link high-level traits to their molecular causes. The massive multiple-testing issues involved in analyzing these data make the correct level of confidence to place in mRNA abundance quantitative trait loci (QTL) a difficult problem. We use a unique resource to directly test mRNA abundance QTL replicability in mice: paired recombinant inbred (RI) and F₂ data sets derived from C57BL/6J (B6) and DBA/2J (D2) inbred strains and phenotyped using the same Affymetrix arrays. We have one forebrain and one striatum data set pair. We describe QTL replication at varying stringencies in these data. For instance, 78% of mRNA expression QTL (eQTL) with genome-wide adjusted p ≤ 0.0001 in RI data replicate at a genome-wide adjusted p < 0.05 or better. Replicated QTL are disproportionately putatively cis-acting, and approximately 75% have higher apparent expression levels associated with B6 genotypes, which may be partly due to probe set generation using B6 sequence. Finally, we note that while trans-acting QTL do not replicate well between data sets in general, at least one cluster of trans-acting QTL on distal Chr 1 is notably preserved between data sets.     

Detection of quantitative trait loci for body weight at 10 weeks from Philippine wild mice

A genome-wide scan for quantitative trait loci (QTLs) controlling body weight at 10 weeks after birth was carried out in a population of 387 intersubspecific backcross mice derived from a cross between C57BL/6J inbred mice (Mus musculus domesticus) and wild mice (M. m. castaneus) captured in the Philippines, in order to discover novel QTLs from the wild mice that have about 60% lower body weight than C57BL/6J. By interval mapping, we detected four QTLs: a highly significant QTL on Chromosome (Chr) 2, which was common in both sexes; two significant QTLs on Chr 13, one male-specific and the other female-specific; and a suggestive male-specific QTL on X Chr. By composite interval mapping, we confirmed the presence of the three QTLs on Chrs 2 and 13, but not of the male-specific X-linked QTL. The composite interval mapping analysis newly identified three QTLs: a significant male-specific QTL on Chr 11 and two highly significant female-specific QTLs on Chrs 9 and X. Individual QTLs explained 3.8–11.6% of the phenotypic variance, and all the QTL alleles derived from the wild mice decreased body weight. A two-way analysis of variance revealed a significant epistatic interaction between the Chr 2 QTL and the background marker locus D12Mit4 on Chr 12 only in males. The interaction effect unexpectedly increased body weight. The chromosomal region containing the Chr 2 QTL did not coincide with those of growth or fatness QTLs mapped in previous studies. These results suggest that a population of wild mice may play an important role as new sources of valuable QTLs. Received: 14 January 2000 / Accepted: 14 April 2000     

Quantitative trait loci (QTL) for lean body mass and body length in MRL/MPJ and SJL/J F(2) mice

Studies on the genetic mechanisms involved in the regulation of lean body mass (LBM) in mammals are minimal, although LBM is associated with a competent immune system and an overall good (healthy) body functional status. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F₂ intercross to identify the quantitative trait loci (QTL) involved in the regulation of LBM. We hypothesized that additional QTL can be identified using a different mouse cross (MRL/SJL cross). Ten QTL were identified for LBM on chromosomes (chrs) 2, 6, 7, 9,13 and 14. Of those ten, QTL on chrs 6, 7 and 14 were exclusive to LBM, while QTL on chrs 4 and 11 were exclusively body length. LBM QTL on chrs 2 and 9 overlap with those of size. Altogether, the ten LBM QTL explained 41.2% of phenotypic variance in F₂ mice. Five significantly interacting loci that may be involved in the regulation of LBM were identified and accounted for 24.4% of phenotypic variance explained by the QTL. Five epistatic interactions, contributing 22.9% of phenotypic variance, were identified for body length. Interacting loci on chr 2 may influence LBM by regulating body length. Therefore, epistatic interactions as well as single QTL effects play an important role in the regulation of LBM. Electronic Publication     

Genotype × diet interactions in mice predisposed to mammary cancer. I. Body weight and fat

High dietary fat intake and obesity may increase susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F₂ mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F₂ mice either a very-high-fat or a matched-control-fat diet and measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL × diet interaction effects. Here we describe development of the F₂ population (n = 615) which resulted from a cross between the polygenic obesity model M16i and FVB/NJ-TgN (MMTV-PyMT)^634Mul, effects of diet on growth and body composition, and QTL and QTL × diet and/or gender interaction effects for growth and obesity-related phenotypes. We identified 38 QTL for body composition traits that were significant at the genome-wide 0.05 level, likely representing nine distinct loci after accounting for pleiotropic effects. QTL × diet and/or gender interactions were present at 15 of these QTL, indicating that such interactions play a significant role in defining the genetic architecture of complex traits such as body weight and obesity.     

Quantitative trait locus (QTL) analysis of wood quality traits in <Emphasis Type="Italic">Eucalyptus nitens</Emphasis>

To identify the chromosomal regions affecting wood quality traits, we conducted a genome-wide quantitative trait locus (QTL) analysis of wood quality traits in Eucalyptus nitens. This information is important to exploit the full potential of the impending Eucalyptus genome sequence. A three generational mapping population consisting of 296 progeny trees was used to identify QTL associated with several wood quality traits in E. nitens. Thirty-six QTL positions for cellulose content, pulp yield, lignin content, density, and microfibril angle (MFA) were identified across different linkage groups. On linkage groups (LG)2 and 8, cellulose QTL cluster with pulp yield and extractives QTL while on LG4 and 10 cellulose and pulp yield QTLs cluster together. Similarly, on LG4, 5, and 6 QTL for lignin traits were clustered together. At two positions, QTL for MFA, a physical trait related to wood stiffness, were clustered with QTL for lignin traits. Several cell wall candidate genes were co-located to QTL positions affecting different traits. Comparative QTL analysis with Eucalyptus globulus revealed two common QTL regions for cellulose and pulp yield. The QTL positions identified in this study provide a resource for identifying wood quality genes using the impending Eucalyptus genome sequence. Candidate genes identified in this study through co-location to QTL regions may be useful in association studies.     

Quantitative trait loci for production traits in pigs: a combined analysis of two Meishan x Large White populations

Combined analysis of data from two or more resource populations can improve the power and accuracy of QTL mapping and allow some cross-validation of results. In this study, we performed a genome-wide scan using combined data from two F(2) populations derived from a cross between Large White and Chinese Meishan pigs. A total of 739 pigs were included in the analysis. In total 187 markers were genotyped in the two populations, including 115 markers genotyped in both populations, and these markers covered 2282 cM of the pig genome with an average of 13.58 cM between markers. Seven traits (teat number, birth weight, weaning weight, test-end weight, fat depth at shoulder, fat depth at mid back and fat depth at loin) were analysed for both individual populations and the combined population. There were 9 (2, 10), 1 (4, 4) and 14 (5, 18) QTL that achieved 1% genome-wide, 5% genome-wide and suggestive significance levels respectively in population 1 (population 2, combined population). Additive effects of QTL detected in the two populations at all significance levels were largely consistent suggesting that the QTL represent real genetic effects, but this was not the case for dominance or imprinting effects. There were also a number of significant interactions between detected QTL effects and population.     

Mapping quantitative trait loci for bovine ovulation rate

An elite, three-generation family from the USDA Meat Animal Research Center twinning population was examined for evidence of ovulation rate quantitative trait loci (QTL). This work was both a continuation of previously reported results suggesting evidence for ovulation rate QTL on bovine Chromosome (Chr) 7 and an extension of a genome-wide search for QTL. Additional markers were typed on Chr 7 to facilitate interval mapping and testing of the hypothesis of one versus two QTL on that chromosome. In addition, 14 other informative markers were added to a selective genotyping genome screening of this family, and markers exhibiting nominal significance were used to identify chromosomal regions that were then subjected to more exhaustive analysis. For Chr 7, a total of 12 markers were typed over a region spanning the proximal two-thirds of the chromosome. Results from interval mapping analyses indicated evidence suggestive of the presence of QTL (nominal P < 0.00077) within this region. Subsequent analysis with a model postulating two QTL provided evidence (P < 0.05) for two rather than one QTL on this chromosome. Preliminary analysis with additional markers indicated nominal significance (P < 0.05) for regions of Chrs 5, 10, and 19. Each of these regions was then typed with additional markers for the entire three-generation pedigree. Significant evidence (P < 0.000026) of ovulation rate QTL was found for Chrs 5 and 19, while support on Chr 10 failed to exceed a suggestive linkage threshold (P > 0.00077). Received: 14 May 1999 / Accepted: 14 October 1999     

Quantitative trait loci for yield components in oil palm (Elaeis guineensis Jacq.)

The development of an oil palm RFLP marker map has enabled marker-based QTL mapping studies to be undertaken. Information from 153 RFLP markers was used in combination with phenotypic data from an F₂ population to estimate the position and effects of quantitative trait loci (QTLs) for traits including yield of fruit and its components and measures of vegetative growth. The mapping population consisted of 84 palms segregating for the major gene influencing shell thickness. Marker data were analysed to produce a linkage map consisting of 22 linkage groups. The QTL mapping analysis was carried out by interval mapping and single-marker analysis for the unlinked markers; significance thresholds were generated by permutation. Using both single-marker and interval-mapping analysis significant marker associated QTL effects were found for 11 of the 13 traits analysed. The results of interval-mapping analysis of fruit weight, petiole cross section and rachis length, and ratios of shell:fruit, mesocarp:fruit and kernel:fruit indicated significant (P<0.05) QTLs at the genome-wide threshold. The putative QTLs were associated with between 8.2% and 44.0% of the phenotypic variation, with an average of 27% for the single-marker analysis and 19% for the interval-mapping analysis. The higher percentage of phenotypic variation explained in the single-marker analysis, when compared to the interval-mapping analysis, is likely to be due to the lower stringency associated with the single-marker analysis. Large dominance deviations were associated with a sizeable proportion of the putative QTLs. The ultimate objective of mapping QTLs in commercial populations is to utilise novel breeding strategies such as marker-assisted selection (MAS). The potential impact of MAS in oil palm breeding programmes is discussed. Received: 26 June 2000 / Accepted: 24 October 2000     

Quantitative trait locus analysis of abnormal circadian period in CS mice

CS mice show a free-running period (κ) longer than 24 h and rhythm splitting in constant darkness (DD). These features in behavioral circadian rhythms are distinctive as compared with other inbred strains of mice, which exhibit robust free-running rhythms with κ shorter than 24 h. To identify the genes affecting κ, quantitative trait locus (QTL) analysis was initially conducted by using 289 F₂ mice derived from a cross between CS and C57BL/6J strain. A suggestive QTL (LOD = 3.71) with CS allele increasing κ was detected on the distal region of Chromosome (Chr) 19. Next, using 192 F₂ mice from a cross between CS and MSM strain, the presence of the QTL on Chr 19 was examined, and we confirmed the QTL at the genome-wide significant level (LOD = 4.61 with 10.4% of the total variance explained). This QTL was named long free-running period (Lfp). Three other suggestive QTLs (LOD = 3.24–4.28) were mapped to the midportion of Chr 12 in (CS×C57BL/6J)F₂ mice, and to the proximal and middle region of Chr 19 in (CS×MSM)F₂ mice, respectively, of which, CS alleles for two QTLs on Chr 19 have the effect of lengthening κ. None of these QTLs were mapped to the chromosomal regions of previously described QTLs for κ and known clock genes (Clock, mPer1, Bmal1, mCry1, mCry2, mTim, and Csnk1e). Received: 5 July 2000 / Accepted: 5 December 2000     

A paternally imprinted QTL for mature body mass on mouse Chromosome 8

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F₂ population (n ≈ 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.     

A Comparison of Multivariate Genome-Wide Association Methods

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (α = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak.     

A quantitative trait locus for live weight maps to bovine Chromosome 23

A multiple-marker mapping approach was used to search for quantitative trait loci (QTLs) affecting production, health, and fertility traits in Finnish Ayrshire dairy cattle. As part of a whole-genome scan, altogether 469 bulls were genotyped for six microsatellite loci in 12 families on Chromosome (Chr) 23. Both multiple-marker interval mapping with regression and maximum-likelihood methods were applied with a granddaughter design. Eighteen traits, belonging to 11 trait groups, were included in the analysis. One QTL exceeded experiment level and one QTL genome level significance thresholds. Across-families analysis provided strong evidence (P_experiment= 0.0314) for a QTL affecting live weight. The QTL for live weight maps between markers BM1258 and BoLA DRBP1. A QTL significant at genome level (P_genome= 0.0087) was mapped for veterinary treatment, and the putative QTL probably affects susceptibility to milk fever or ketosis. In addition, three traits exceeded the chromosome 5% significance threshold: protein percentage of milk, calf mortality (sire), and milking speed. In within-family analyses, protein percentage was associated with markers in one family (LOD score = 4.5). Received: 14 December 1998 / Accepted: 28 March 1998     

Chromosomal regions harboring genes for the work to femur failure in mice

The work to failure is defined as the maximum energy bone can absorb before breaking, and therefore is a direct test of the risk of fracture. To determine the genetic loci influencing work to failure, we have performed a high density genome-wide scan in 633 (MRL × SJL) F₂ female mice. Five loci (P <0.005) with significant effects on work to failure were found on chromosomes 2, 7, 8, 9, and X, which collectively explained around 20% variance of work to femur failure in F₂ mice. Of those, only the QTL on chromosome 9 was concordant with bone mineral density (BMD) QTLs. Eight significant interactions (P <0.01) between marker loci were identified, which accounted for an equivalent amount of F₂ variance (23%) to combined single QTL effects. Our results demonstrate that most of the genetic loci regulating work to failure are different from those for BMD in the 7-week-old female mice. If this is also true in humans, this finding will challenge the predictive value of BMD for the risk of fracture. Electronic Publication     

Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits

From simulation studies it is known that the allocation of experimental resources has a crucial effect on power of QTL detection as well as on accuracy and precision of QTL estimates. In this study, we used a very large experimental data set composed of 976 F(5) maize testcross progenies evaluated in 19 environments and cross-validation to assess the effect of sample size (N), number of test environments (E), and significance threshold on the number of detected QTL, the proportion of the genotypic variance explained by them, and the corresponding bias of estimates for grain yield, grain moisture, and plant height. In addition, we used computer simulations to compare the usefulness of two cross-validation schemes for obtaining unbiased estimates of QTL effects. The maximum, validated genotypic variance explained by QTL in this study was 52.3% for grain moisture despite the large number of detected QTL, thus confirming the infinitesimal model of quantitative genetics. In both simulated and experimental data, the effect of sample size on power of QTL detection as well as on accuracy and precision of QTL estimates was large. The number of detected QTL and the proportion of genotypic variance explained by QTL generally increased more with increasing N than with increasing E. The average bias of QTL estimates and its range were reduced by increasing N and E. Cross-validation performed well with respect to yielding asymptotically unbiased estimates of the genotypic variance explained by QTL. On the basis of our findings, recommendations for planning of QTL mapping experiments and allocation of experimental resources are given.     

A large-sample QTL study in mice: I. Growth

By use of long-term selection lines for high and low growth, a large-sample (n = ~1,000 F₂) experiment was conducted in mice to further understand the genetic architecture of complex polygenic traits. In combination with previous work, we conclude that QTL analysis has reinforced classic polygenic paradigms put in place prior to molecular analysis. Composite interval mapping revealed large numbers of QTL for growth traits with an exponential distribution of magnitudes of effects and validated theoretical expectations regarding gene action. Of particular significance, large effects were detected on Chromosome (Chr) 2. Regions on Chrs 1, 3, 6, 10, 11, and 17 also harbor loci with significant contributions to phenotypic variation for growth. Despite the large sample size, average confidence intervals of ~20 cM exhibit the poor resolution for initial estimates of QTL location. Analysis with genome-wide and chromosomal polygenic models revealed that, under certain assumptions, large fractions of the genome may contribute little to phenotypic variation for growth. Only a few epistatic interactions among detected QTL, little statistical support for gender-specific QTL, and significant age effects on genetic architecture were other primary observations from this study. Present address: (Joao L. Rocha) Sygen International, 2929 Seventh Street, Berkeley, California 94710, USA     

Detection of quantitative trait loci for androstenone, skatole and boar taint in a cross between Large White and Meishan pigs

'Boar taint' is a strong perspiration-like, urine-like unpleasant odour given off upon heating or cooking of meat from some intact (uncastrated) male pigs. Data from the F(2) generation of a Large White (LW) x Meishan (MS) crossbred population were analysed to detect quantitative trait loci (QTL) for traits associated with boar taint. Fat samples from 178 intact male pigs slaughtered at 85 +/- 5 kg were analysed for the major contributors to boar taint (androstenone, indole and skatole). Fat and lean samples from cooked meat were scored for boar, abnormal and pork flavour and odour by a trained sensory panel (SP). A scan with 117 markers covering the whole genome was performed in the F(2) individuals, together with their F(1) parents and purebred grandparents. At the 5% chromosomal significance threshold (approximately equal to the genome-wide suggestive significance threshold), QTL were detected for the laboratory estimate of androstenone on chromosomes 2, 4, 6, 7 and 9. However, only on chromosome 6 were there QTL for boar flavour (BF) traits in the same or adjacent marker intervals as a QTL for the laboratory estimate of androstenone. On chromosome 14, QTL were detected for the laboratory estimates of indole and skatole, the SP score for skatole and the scores for BF in lean and BF in fat. In all five cases, the MS allele generally increased the estimate or score, compared with the LW allele, but it appeared that desirable and undesirable alleles were present in both breeds. This locus on chromosome 14 has considerable potential for use to reduce the incidence of boar taint, especially if further research can identify the causative polymorphism or strongly associated markers.