Mapping of quantitative trait loci affecting behaviour in swine

Behavioural indices in vertebrates are under genetic control at least to some extent. In spite of significant behavioural problems in farm animals, information on the genetic background of behaviour is sparse. The aim of this study was to map QTL for behavioural indices in swine under healthy conditions and after infection with Sarcocystis miescheriana , as behaviour can be significantly influenced by disease . This well-described parasite model subsequently leads to acute (day 14 p.i.), subclinical (day 28 p.i.) and chronic disease (day 42 p.i.), allowing the study and comparison of the behaviour of pigs under four different states of health or disease. The study was based on a well-described Pietrain/Meishan F₂ family that has recently allowed the detection of QTL for disease resistance. We have mapped six genome-wide significant and 24 chromosome-wide significant QTL for six behavioural indices in swine. Six of these QTL (i.e. 20% of total QTL) showed effects on behavioural traits of the healthy pigs (day 0). Some of them (QTL on SSC11 and 18) lost influence on behavioural activities during disease, while the effects of others (QTL on SSC5, SSC8) partly remained during the whole experiment, although with different effects on the distinct behavioural indices. The disease model has been of high relevance to detect effects of gene loci on behavioural indices. Considering the importance of segregating alleles and environmental conditions that allow the identification of the phenotype, we conclude that there are indeed QTL with interesting effects on behavioural indices in swine.     

Mapping of multiple quantitative trait loci affecting bovine spongiform encephalopathy

A whole-genome scan was conducted to map quantitative trait loci (QTL) for BSE resistance or susceptibility. Cows from four half-sib families were included and 173 microsatellite markers were used to construct a 2835-cM (Kosambi) linkage map covering 29 autosomes and the pseudoautosomal region of the sex chromosome. Interval mapping by linear regression was applied and extended to a multiple-QTL analysis approach that used identified QTL on other chromosomes as cofactors to increase mapping power. In the multiple-QTL analysis, two genome-wide significant QTL (BTA17 and X/Y(ps)) and four genome-wide suggestive QTL (BTA1, 6, 13, and 19) were revealed. The QTL identified here using linkage analysis do not overlap with regions previously identified using TDT analysis. One factor that may explain the disparity between the results is that a more extensive data set was used in the present study. Furthermore, methodological differences between TDT and linkage analyses may affect the power of these approaches.     

Aschoff's rule in retinally degenerate mice

Both retinally degenerate and wildtype mice lengthened the period of their free-running circadian rhythms and reduced the amount of wheel running when exposed to increasing levels of constant illumination, in accordance with Aschoff's rule. Decreased locomotor activity may contribute toward lengthening of period in bright light. However, the known effects of activity on free-running period are small compared to those obtained by changing illumination. This suggests that Aschoff's rule in mice is not dependent on changes in nonphotic input, but results from a direct effect of light on the circadian system. The sparing of Aschoff's rule in retinally degenerate mice is further evidence that circadian photoreception depends on mechanisms other than rods and cones.     

Mapping liver fat female-dependent quantitative trait loci in collaborative cross mice

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18–20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1–Flal3, referring to Fatty Liver Accumulation Locus 1–3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.     

Mapping quantitative trait loci in oligogenic models

We discuss strategies for mapping quantitative trait loci with emphasis on certain issues of study design that have recently received attention: e.g. genotyping only selected pedigrees and the comparative value of large pedigrees versus sib pairs. We use a standard variance components model and a parametrization of the genetic effects in which the 'segregation' parameters are locally orthogonal to the 'linkage' parameters. This permits simple explicit expressions for the expectation of the score statistic, which we use to compare the power of different strategies. We also discuss robustness of the score statistic.     

Mapping quantitative trait loci in tetraploid populations

Knowledge of quantitative trait locus (QTL) mapping in polyploids is almost void, albeit many exquisite strategies of QTL mapping have been proposed and extensive investigations have been carried out in diploid animals and plants. In this paper we develop a simple algorithm which uses an iteratively reweighted least square method to map QTLs in tetraploid populations. The method uses information from all markers in a linkage group to infer the probability distribution of QTL genotype under the assumption of random chromosome segregation. Unlike QTL mapping in diploid species, here we estimate and test the compound 'gametic effect', which consists of the composite 'genic effect' of alleles and higher-order gene interactions. The validity and efficiency of the proposed method are investigated through simulation studies. Results show that the method can successfully locate QTLs and separates different sources (e.g. additive and dominance) of variance components contributed by the QTLs.     

Mapping quantitative trait loci affecting chicken body size traits via genome scanning

Body size traits reflect the condition of body development, are always mentioned when a breed is described, and are also targets in breeding programmes. In chicken, there are several reports focused on body size traits, such as shank length, tibia length or bone traits. However, no study was carried out on chest width (CW), chest depth (CD), body slope length (BL) and head width (HW) traits. In this study, genome scans were conducted on an F₂ resource population (238 F₂ individuals from 15 full‐sib families derived from an intercross of the White Plymouth Rock with the Silkies Fowl) to identify quantitative trait loci (QTL) associated with CW, CD, BL and HW from 7 to 12 weeks of age. In total, 21 significant or suggestive QTL were found that affected four body size traits. Four QTL reached 1% genome‐wide significance level: at 297 cM on GGA3 (associated with CW at 9 weeks of age), between 155 and 184 cM on GGA1 (affecting BL traits at 9 and 10 weeks of age), at 22 cM on GGA2 (related with BL traits at 12 weeks of age) and at 36 cM on GGA1 (for HW trait at 8 weeks of age).     

Mapping quantitative trait loci affecting female reproductive traits on porcine chromosome 8

An understanding of the genetic control of porcine female reproductive performance would offer the opportunity to utilize natural variation and improve selective breeding programs through marker-assisted selection. The Chinese Meishan is one of the most prolific pig breeds known, farrowing three to five more viable piglets per litter than the European Large White breed. This difference in prolificacy is attributed to the Meishan's superior prenatal survival levels. The present study utilized a three-generation cross in which the founder grandparental animals were purebred Meishan and Large White pigs in a scan for quantitative trait loci (QTL) on porcine chromosome 8 (SSC8) associated with reproductive performance. Reproductive traits, including number of corpora lutea (ovulation rate), teat number, litter size, and prenatal survival, were recorded for as many as 220 F2 females. Putative QTL for the related traits of litter size and prenatal survival were identified at the distal end of the long arm of SSC8. A physiological candidate gene, SPP1, was found to lie within the 95% confidence interval of these QTL. A suggestive QTL for teat number was revealed on the short arm of SSC8. The present study demonstrates, to our knowledge, the first independent confirmation of QTL for fecundity on SSC8, and these QTL regions provide a crucial starting point in the search for the causal genetic variants.     

Mapping quantitative trait loci for anxiety in chromosome substitution strains of mice

Anxious behavior in the mouse is a complex quantitative phenotype that varies widely among inbred mouse strains. We examined a panel of chromosome substitution strains bearing individual A/J chromosomes in an otherwise C57BL/6J background in open-field and light-dark transition tests. Our results confirmed previous reports of quantitative trait loci (QTL) on chromosomes 1, 4, and 15 and identified novel loci on chromosomes 6 and 17. The studies were replicated in two separate laboratories. Systematic differences in the overall activity level were found between the two facilities, but the presence of the QTL was confirmed in both laboratories. We also identified specific effects on open-field defecation and center avoidance and distinguished them from overall open-field activity.     

Mapping quantitative trait loci with censored observations

The existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and is fully observed. These assumptions may not be satisfied when the phenotype pertains to the survival time or failure time, which has a skewed distribution and is usually subject to censoring due to random loss of follow-up or limited duration of the experiment. In this article, we propose an interval-mapping approach for censored failure time phenotypes. We formulate the effects of QTL on the failure time through parametric proportional hazards models and develop efficient likelihood-based inference procedures. In addition, we show how to assess genome-wide statistical significance. The performance of the proposed methods is evaluated through extensive simulation studies. An application to a mouse cross is provided.     

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.     

Mapping quantitative trait loci with epistatic effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.     

Mapping of quantitative trait loci affecting Drechslera teres resistance in barley with molecular markers

 Resistance loci for seedling-stage resistance to net blotch disease (Drechslera teres) in barley were mapped with molecular markers in an F₂ population derived from a cross between the susceptible barley cultivar ‘Arena’ and the resistant Ethiopian landrace ‘Hor 9088’. Disease reactions were scored with first and second leaves of 2-week-old plants 7 and 9 days after inoculation with a single spore-derived isolate. For linkage analysis, 22 RFLP markers and 284 AFLP markers were used. The seven linkage groups covered 1153.3 cM with an average marker interval of 3.76 cM. The resistance was determined to be inherited in a quantitative manner. Altogether, 12 QTLs were mapped with positions depending on the leaf used for testing and the time period after infection. Heritability in the broad sense ranged between 0.21 and 0.37. Received: 26 May 1998 / Accepted: 9 June 1998     

Mapping of quantitative trait loci affecting resistance/susceptibility to Sarcocystis miescheriana in swine

The outcome of infectious diseases in vertebrates is under genetic control at least to some extent. In swine, e.g., marked differences in resistance/susceptibility to Sarcocystis miescheriana have been shown between Chinese Meishan and European Pietrain pigs, and these differences are associated with high heritabilities. A first step toward the identification of genes and polymorphisms causal for these differences may be the mapping of quantitative trait loci (QTLs). Considering clinical, immunological, and parasitological traits in the above model system, this survey represents the first QTL study on parasite resistance in pigs. QTL mapping was performed in 139 F(2) pigs of a Meishan/Pietrain family infected with S. miescheriana. Fourteen genome-wide significant QTLs were mapped to several chromosomal areas. Among others, major QTLs were identified for bradyzoite numbers in skeletal muscles (F = 17.4; p < 0.001) and for S. miescheriana-specific plasma IgG(2) levels determined 42 days p.i. (F = 20.9; p < 0.001). The QTLs were mapped to different regions of chromosome 7, i.e., to the region of the major histocompatibility complex (bradyzoites) and to an immunoglobulin heavy chain cluster, respectively. These results provide evidence for a direct and causal role for gene variants within these gene clusters (cis-acting) in differences in resistance to S. miescheriana.     

Mapping quantitative trait loci in noninbred mosquito crosses

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.     

Two quantitative trait loci affecting progressive hearing loss in 101/H mice

Although recent progress in identifying genes involved in deafness has been remarkable, the genetic basis of progressive hearing loss (or age-related hearing loss) is poorly understood because of the extreme difficulty in studying such a late-onset, complex disease in human populations. Several inbred strains of mice such as 129P1/ReJ, C57BL/6J, DBA/2J, and BALB/cByJ have been reported to exhibit age-related hearing loss and provide valuable models for human nonsyndromic progressive deafness. In this article we show that 101/H mice also exhibit progressive deafness with early onset. Linkage analysis of F₂ populations derived from crosses between the 101/H and the MAI/Pas and MBT/Pas wild-derived mice suggested at least two major quantitative trait loci (QTLs) that influence progressive hearing loss. A first QTL, designated Phl1, was mapped with a maximum LOD score of 6.7 to the centromeric region of Chromosome 17, where no deafness-related QTL has been mapped so far. A second QTL, designated Phl2, mapped to Chromosome 10 and exhibited a maximum LOD score of 5.3. The map position of Phl2 near the well-known QTL of age-related hearing loss (Ahl) suggested the possibility of allelism, although the Ahl mutation itself did not segregate in these crosses. Finally, we found some evidence of epistatic interaction between Phl1 and Phl2. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. Tomoji Mashimo, Alexandra E. Erven, and Sarah L. Spiden contributed equally to this work.     

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     

Identification of quantitative trait loci affecting cattle temperament

In addition to its potential contribution to improving animal welfare, the study of the genetics of cattle behavior may provide more general insights into the genetic control of such complex traits. We carried out a genome scan in a Holstein x Charolais cross cattle population to identify quantitative trait loci (QTL) influencing temperament-related traits. Individuals belonging to the second-generation of this population (F(2) and backcross individuals) were subjected to 2 behavioral tests. The flight from feeder (FF) test measured the distance at which the animal moved away from an approaching human observer, whereas the social separation (SS) test categorized different activities which the animal engaged in when removed from its penmates. The entire population was genotyped with 165 microsatellite markers. A regression interval mapping analysis identified 29 regions exceeding the 5% chromosome-wide significance level, which individually explained a relatively small fraction of the phenotypic variance of the traits (from 3.8% to 8.4%). One of the significant associations influencing an FF test trait on chromosome 29 reached the 5% genome-wide significance level. Eight other QTL, all associated with an SS test trait, reached the 1% chromosome-wide significance level. The location of some QTL coincided with other previously reported temperament QTL in cattle, whereas those that are reported for the first time here may represent general loci controlling temperament differences between cattle breeds. No overlapping QTL were identified for the traits measured by the 2 different tests, supporting the hypothesis that different genetic factors influence behavioral responses to different situations.     

Mapping quantitative trait loci underlying triploid endosperm traits

Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue in cereals. Endosperm tissue determines the grain quality of cereals. Improving grain quality is one of the important breeding objectives in cereals. However, current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. In this paper, we derive for the first time the conditional probabilities of F(3) endosperm QTL genotypes given different flanking marker genotypes in F(2) plants. Using these probabilities, we develop a multiple linear regression method implemented via the iteratively reweighted least-squares (IRWLS) algorithm and a maximum likelihood method (ML) implemented via the expectation-maximization (EM) algorithm to map QTL underlying endosperm traits. We use the mean value of endosperm traits of F(3) seeds as the dependent variable and the expectations of genotypic indicators for additive and dominance effect of a putative QTL flanked by a pair of markers as independent variables for IRWLS mapping. However, if an endosperm trait is measured quantitatively using a single endosperm sample, the ML mapping method can be used to separate the two dominance effects. Efficiency of the methods is verified through extensive Monte Carlo simulation studies. Results of simulation show that the proposed methods provide accurate estimates of both the QTL effects and locations with very high statistical power. With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control.     

Mapping quantitative trait loci mediating sensitivity to etomidate

Long- and Short-Sleep (LS and SS) mice were selectively bred for differences in ethanol-induced loss of the righting reflex (LORR) and have been found to differ in LORR induced by various anesthetic agents. We used a two-stage mapping strategy to identify quantitative trait loci (QTLs) affecting duration of LORR caused by the general anesthetic etomidate and brain levels of etomidate (BEL) following regain of the righting reflex. Analysis of recombinant-inbred strains derived from a cross between LS and SS mice (LSXSS) yielded a heritability estimate of 0.23 for etomidate-induced LORR and identified one marker that showed suggestive linkage for a QTL, on mouse Chromosome (chr) 12. Mapping in an F(2) population derived from a cross between inbred LS and SS (ILS and ISS) revealed a significant QTL for etomidate-induced LORR on Chr 12, and two significant QTLs mediating BEL on Chrs 6 and 12. Several QTLs showing suggestive linkage for etomidate-induced LORR and BEL were also identified in the F(2) population. Brain levels of etomidate in the RI and F(2) mice suggested that differences in LORR were due to differential central nervous system sensitivity, rather than differential etomidate metabolism. Interestingly, the region on Chr 7 has also been identified as a region influencing ethanol-induced LORR, suggesting the possibility of a common genetic mechanism mediating etomidate and ethanol sensitivity. These QTL regions need to be further narrowed before the testing of candidate genes is feasible.