Approaches to interval mapping of QTL in a multigeneration pedigree: the example of porcine chromosome 4

Quantitative trait loci (QTLs) have been mapped in many studies of F2 populations derived from crosses between diverse lines. One approach to confirming these effects and improving the mapping resolution is genetic chromosome dissection through a backcrossing programme. Analysis by interval mapping of the data generated is likely to provide additional power and resolution compared with treating data marker by marker. However, interval mapping approaches for such a programme are not well developed, especially where the founder lines were outbred. We explore alternative approaches to analysis using, as an example, data from chromosome 4 in an intercross between wild boar and Large White pigs where QTLs have been previously identified. A least squares interval mapping procedure was used to study growth rate and carcass traits in a subsequent second backcross generation (BC2). This procedure requires the probability of inheriting a wild boar allele for each BC2 animal for locations throughout the chromosome. Two methods for obtaining these probabilities were compared: stochastic or deterministic. The two methods gave similar probabilities for inheriting wild boar alleles and, hence, gave very similar results from the QTL analysis. The deterministic approach has the advantage of being much faster to run but requires specialized software. A QTL for fatness and for growth were confirmed and, in addition, a QTL for piglet growth from weaning at 5 weeks up to 7 weeks of age and another for carcass length were detected.     

猪2号染色体遗传连锁图谱的构建与QTL定位分析

构建了猪2号染色体的遗传连锁图谱,并进一步进行了重要生产性状数量性状位点的定位,结果表明,7个微卫星位点均为中高度多态性位点,多态信息含量为0．40182－0．58477,可以满足遗传连锁图谱构建的要求,构建的资源家系遗传连锁图谱总长152．9cM,位点的排列顺序与USDA结果一致,但除了Sw2516与Sw1201标记区间外,所有标记区间距离均大于USDA图谱,将连锁图谱与性状记忆结合起来,进一步进行了猪数量性状位点定位的研究,在2号染色体发现了显著影响活体估测瘦肉率等活体估测性状的QTLs,此外还发现眼肌高度和背最长肌大理石纹的QTLs,其中影响活体估测瘦肉率的QTL达到了染色体显著的水平（P＜0．01）,且解释性状的表型变异达21．55％,影响眼肌高度和背最长肌大理石纹的QTLs分别可以解释10．12％和10．97％的表型变异,影响活体估测性状的QTLs加性效应与显性效应作用方向相反,影响眼肌高度的QTL加性效应与显性效应相同,在大白猪中具有增效等位基因,定位的QTLs效应较大,为在群体中开展分子标记辅助育种奠定了理论基础。     

Identification of a 3.7-Mb region for a marbling QTL on bovine chromosome 4 by identical-by-descent and association analysis

QTL mapping for growth and carcass traits was performed using a paternal half-sib family composed of 325 Japanese Black cattle offspring. Nine QTL were detected at the 1% chromosome-wise significance level at a false discovery rate of less than 0.1. These included two QTL for marbling on BTA 4 and 18, two QTL for carcass weight on BTA 14 and 24, two QTL for longissimus muscle area on BTA 1 and 4, two QTL for subcutaneous fat thickness on BTA 1 and 15 and one QTL for rib thickness on BTA 6. Although the marbling QTL on BTA 4 has been replicated with significant linkages in two Japanese Black cattle sires, the three Q (more marbling) haplotypes, each inherited maternally, were apparently different. To compare the three Q haplotypes in more detail, high-density microsatellite markers for the overlapping regions were developed within the 95% CIs (65 markers in 44–78 cM). A detailed haplotype comparison indicated that a small region (<3.7 Mb) around 46 cM was shared between the Qs of the two sires, whose dams were related. An association of this region with marbling was shown by a regression analysis using the local population, in which the two sires were produced and this was confirmed by an association study using a population collected throughout Japan. These results strongly suggest that the marbling QTL on BTA 4 is located in the 3.7-Mb region at around 46 cM.     

Confirmation of QTL on porcine chromosomes 1 and 8 influencing leukocyte numbers, haematological parameters and leukocyte function

A genome wide search in European Wild Boar x Swedish Yorkshire (W x Y) inter-cross pigs has earlier identified quantitative trait loci (QTL) for leucocyte number and function on porcine chromosomes 1 and 8 (SSC 1 and 8). To verify the involvement of these chromosomal regions in the regulation of haematocrit (Hem) and haemoglobin (Hb) levels, leucocyte numbers and in vitro leukocyte functions (mitogen induced proliferation and IL-2 production, virus induced interferon-alpha production and neutrophil phagocytosis), animals of different genetic backgrounds were analysed. The animals comprised a back-cross sire family (n=47) of W x Y pigs and six crossbred [Y x Landrace (L)] sire families (n=191). They were genotyped for 16 genetic markers and an interval analysis was performed. On SSC1, a QTL close to S0082 on the q-arm that influenced numbers of white blood cells in L x Y pigs and numbers of band neutrophils and CD8(+) cells in W x Y pigs was identified (P相似文献   

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek's disease

Marek's disease (MD) is a lymphoproliferative disease of chickens that costs the poultry industry approximately $1 billion annually. Genetic resistance to MD is gaining increased attention to augment vaccinal control as disease outbreaks occur more frequently. Previously, analysis of a 272 F2 White Leghorn resource population measured for many MD traits and genotyped for 78 microsatellite markers revealed two and four quantitative trait loci (QTL) with significant and suggestive association, respectively, to one or more MD associated traits. Additional genetic markers have since been scored on the MD resource population to increase QTL resolution and genome coverage. Saturation of four of the QTL regions with 17 markers revealed five new QTL while 32 markers extended the genome coverage by 400 + CM and uncovered three more QTL. QTL analysis by single-point and interval mapping algorithms agreed well when marker saturation was approximately 20 CM or less. Currently 127 genetic markers cover approximately 68% of the genome that contain up to 14 MD QTL associated to one or more MD trait; seven at the significant level and seven at the suggestive level. Individually each QTL accounts for 2-10% of the variation and, in general, resistance was dominant although the resistant allele may come from either parental line. This study suggests that a limited number of genomic regions play a major role in the genetic control of MD resistance. Markers linked to these loci may be useful for selection of MD resistant stock by the poultry industry following verification of the association within their breeding populations.     

SNPs in the porcine GOT1 gene improve a QTL for serum aspartate aminotransferase activity on SSC14

Clinical–chemical traits are essential parameters to quantify the health status of individuals and herds, but the knowledge about their genetic architecture is sparse, especially in swine. We have recently described three QTL for serum aspartate aminotransferase activity (sAST), and one of these maps to a region on SSC14 where the aspartate aminotransferase coding gene GOT1 is located. This QTL was only apparent under the acute burden of a model disease. The aim of the present study was to characterize GOT1 as a candidate gene and to test the effects of different GOT1 SNPs as potential quantitative trait nucleotides (QTNs) for sAST. Nine SNPs within GOT1 were identified, and SNP c.‐793C>G significantly increased the QTL effects and narrowed the confidence interval from 90 to 15 cM. Additionally, we found a significant association of SNP c.‐793C>G in a commercial outbred line, but with reversed phase. We conclude that GOT1 is a putative candidate gene for the sAST QTL on SSC14, and that SNP c.‐793C>G is close to the responsible QTN.     

Genetics of serum and muscle lipids in pigs

Pork meat is one of the most important sources of animal protein in the human diet. Its nutritional properties are partly determined by intramuscular fat content and composition, with existing general consensus about the detrimental effects of cholesterol and saturated fat on cardiovascular health in humans. Because of their physiological resemblance, pigs can be also used as a valuable animal model to study the genetics of human diseases such as atherosclerosis, obesity and dyslipidaemias. Heritability estimates and QTL maps of porcine muscle and serum lipid traits evidence that a considerable amount of genetic variance determining these phenotypes exists, but its molecular basis remains mostly unknown. The recent advent of high‐throughput genotyping and sequencing technologies has revolutionised the field of animal genomics. With these powerful tools, finding needles in the genomic haystack has become increasingly feasible. However, these methodological advances should not be deemed as magic bullets. The goal of identifying the many polymorphisms that shape the variability of lipid phenotypes is so challenging that success can be achieved only under the scope of large international consortia.     

Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar x Large White intercross

Here, we analysed quantitative trait loci (QTL) for fatty acid composition, one of the factors affecting fat quality, in a Japanese wild boar x Large White cross. We found 25 significant effects for 17 traits at 13 positions at the 5% genome-wise level, of which 16 effects for 12 traits at 10 positions were significant at the 1% level. QTL for saturated fatty acids (SFA) in back fat were mapped to swine (Sus scrofa) chromosomes (SSC) 1p, 9 and 15. QTL for unsaturated fatty acids in back fat were mapped to SSC1p, 1q, 4, 5, 9, 15 and 17. Using a regression model that fits back fat thickness as a covariate, two of the QTL for linoleic acid content on SSC4 and SSC17 were not significant, but one QTL for total SFA composition was detected on SSC5 with correction for back fat thickness. Wild boar alleles at six of seven QTL tended to increase SFAs and to decrease unsaturated fatty acids. QTL for fatty acid composition in perirenal fat were mapped on SSC2, 3, 4, 5, 6, 14, 16 and X. QTL for melting point (in back fat samples) were mapped on SSC1, 2 and 15. Wild boar alleles in QTL on SSC1 and SSC15 were associated with elevated melting points whereas those on SSC2 were associated with lower melting point measurements.     

High-resolution physical mapping and construction of a porcine contig spanning the intramuscular fat content QTL

We previously mapped a locus for porcine intramuscular fat content (IMF) by linkage analysis to a 17.1-cM chromosome interval on Sus scrofa chromosome 7 (SSC7) flanked by microsatellite markers SW1083 and SW581. In this study, we identified 34 microsatellite markers and 14 STSs from the 17.1-cM IMF quantitative trait loci (QTL) region corresponding to HSA14q and aligned those loci using the INRA-University of Minnesota porcine radiation hybrid (IMpRH) panel. We then constructed a 5.2-Mb porcine bacterial artificial chromosome (BAC) contig of this region that was aligned using the RH panel. Finally, the IMF QTL was fine-mapped to 12.6 cM between SJ169 and MM70 at the 0.1% chromosome-wise significance level by genotyping the previously studied F2 resource family with 17 additional microsatellites. We also demonstrated that the SJ169-MM70 interval spans approximately 3.0 Mb and contains at least 12 genes: GALC, GPR65, KCNK10, SPATA7, PTPN21, FLJ11806, EML5, TTC8, CHES1, CAP2P1, CHORDC2P and C14orf143.     

Mapping QTL in the porcine MHC region affecting fatness and growth traits in a Meishan/Large White composite population

A number of studies have mapped QTL regulating porcine fatness and growth traits to the region of the major histocompatibility complex (MHC) on porcine chromosome 7 using various experimental crosses. The QTL results from crosses using the Chinese Meishan (MS) (slow growing and fat) are particularly interesting because the MS alleles have been found to be associated with increased growth rate and reduced backfat depth. We investigated these QTL further in a composite population derived previously over eight generations by intercrossing Meishan and the European Large White breeds. Genotype information from 32 markers in a 15cM target region was used in linkage and association analyses. A two‐step variance component analysis identified QTL for three growth‐related traits, explaining 19 ～ 24% of the phenotypic variance with a confidence interval of 4 cM in the target region. SNP association analyses found that ss181128966 and ss181128924 within the QTL interval were strongly associated with the growth traits. Only weak signals for an effect on backfat depth were found in the association and linkage analyses, possibly because of past directional selection in the composite population.     

QTL mapping and the genetic basis of adaptation: recent developments

Quantitative trait loci (QTL) mapping has been used in a number of evolutionary studies to study the genetic basis of adaptation by mapping individual QTL that explain the differences between differentiated populations and also estimating their effects and interaction in the mapping population. This analysis can provide clues about the evolutionary history of populations and causes of the population differentiation. QTL mapping analysis methods and associated computer programs provide us tools for such an inference on the genetic basis and architecture of quantitative trait variation in a mapping population. Current methods have the capability to separate and localize multiple QTL and estimate their effects and interaction on a quantitative trait. More recent methods have been targeted to provide a comprehensive inference on the overall genetic architecture of multiple traits in a number of environments. This development is important for evolutionary studies on the genetic basis of multiple trait variation, genotype by environment interaction, host–parasite interaction, and also microarray gene expression QTL analysis.     

Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2 population

A whole-genome scan was performed on 660 F₂ animals including 250 barrows and 410 gilts in a White Duroc × Erhualian intercross population to detect quantitative trait loci (QTL) for fatty acid composition in the longissimus dorsi muscle and abdominal fat. A total of 153 QTL including 63 genome-wide significant QTL and 90 suggestive effects were identified for the traits measured. Significant effects were mainly evident on pig chromosomes (SSC) 4, 7, 8 and X. No association was detected on SSC3 and 11. In general, the QTL detected in this study showed distinct effects on fatty acid composition in the longissimus muscle and abdominal fat. The QTL for fatty acid composition in abdominal fat did not correspond to those identified previously in backfat and the majority of QTL for the muscle fatty acid composition were mapped to chromosomal regions different from previous studies. Two regions on SSC4 and SSC7 showed significant pleiotropic effects on monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) in both longissimus muscle and abdominal fat. Another two QTL with significant multi-faceted effects on MUFA and PUFA in the longissimus muscle were found each on SSC8 and SSCX. Chinese Erhualian alleles were associated with increased ratios of MUFA to saturated fatty acid at most of the QTL detected, showing beneficial effect in terms of human health.     

Fine mapping of QTL for prepulse inhibition in LG/J and SM/J mice using F2 and advanced intercross lines

Prepulse inhibition (PPI) of the startle response is a measure of sensorimotor gating, a process that filters out extraneous sensory, motor and cognitive information. Humans with neurological and psychiatric disorders, including schizophrenia, obsessive‐compulsive disorder and Huntington's disease, exhibit a reduction in PPI. Habituation of the startle response is also disrupted in schizophrenic patients. In order to elucidate the genes involved in sensorimotor gating, we phenotyped 472 mice from an F₂ cross between LG/J × SM/J for PPI and genotyped these mice genome‐wide using 162 single nucleotide polymorphism (SNP) markers. We used prepulse intensity levels that were 3, 6 and 12 dB above background (PPI3, PPI6 and PPI12, respectively). We identified a significant quantitative trait locus (QTL) on chromosome 12 for all three prepulse intensities as well as a significant QTL for both PPI6 and PPI12 on chromosome 11. We identified QTLs on chromosomes 7 and 17 for the startle response when sex was included as an interactive covariate and found a QTL for habituation of the startle response on chromosome 4. We also phenotyped 135 mice from an F₃₄ advanced intercross line (AIL) between LG/J × SM/J for PPI and genotyped them at more than 3000 SNP markers. Inclusions of data from the AIL mice reduced the size of several of these QTLs to less than 5 cM. These results will be useful for identifying genes that influence sensorimotor gaiting and show the power of AIL for fine mapping of QTLs.     

Mapping of QTL on chromosome X for fat deposition, muscling and growth traits in a wild boar x Meishan F2 family using a high-density gene map

Quantitative trait loci (QTL) for fat deposition, growth and muscling traits have been previously mapped on the basis of low-density linkage maps in a wild boar × Meishan F₂ family to the chromosome X region flanked by SW2456 and SW1943 . Improved QTL resolution was possible using data for F₂ animals with a marker density of 2.7 cM distance in the SW2456 to SW1943 region, including AR , SERPINA7 and ACSL4 as candidate genes. The resolution of the QTL scan was increased substantially, as evidenced by the higher F -ratio values for all QTL. Maxima of F -ratio values for fat deposition, muscling and growth traits were 28.6, 18.2 and 16.5 respectively, and those QTL positions accounted for 7.9%, 5.0% and 4.5% of the F₂ phenotypic variance (VF₂) respectively. QTL for fatness and growth and for most muscling traits mapped near ACSL4 , with the exception of the QTL for ham traits that mapped proximally, in the vicinity of AR . An analysis performed separately for F₂ male animals showed the predominant QTL affecting fat deposition traits (up to 13.6% VF₂) near AR and two QTL for muscling traits (up to 9.9% VF₂) mapped close to ACSL4 . In the F₂ female animals, QTL affecting muscling (up to 12.1% VF₂) mapped at ACSL4 and SW2456 , and QTL for fat deposition (10% VF₂) and growth (up to 10.5% VF₂) mapped at ACSL4 .     

Identification of 10 882 porcine microsatellite sequences and virtual mapping of 4528 of these sequences

A total of 10 882 porcine microsatellite repeats were identified in genomic shotgun sequences from the Sino-Danish Pig Genome Sequencing Consortium (http://www.piggenome.dk). Of these, 4528 microsatellites were placed on a pig-human comparative map by blast analysis of porcine sequences against the human genome (blast cut-off threshold =1 x 10(-5)). All microsatellite sequences placed on the comparative map are accessible at http://www.animalgenome.org/QTLdb/pig.html. These sequences increase the number of identified microsatellites in the porcine genome by several orders of magnitude. They are a new resource of microsatellite sequences for generating markers to be used in linkage studies and in fine mapping and positional cloning of quantitative trait loci.     

SNP detection and genetic mapping of porcine genes encoding enzymes in hepatic metabolic pathways and evaluation of linkage with carcass traits

We have previously identified and mapped porcine expressed sequence tags (ESTs) derived from genes that are preferentially expressed in liver. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) in porcine genes encoding enzymes in hepatic metabolic pathways and use the SNPs for mapping. Furthermore, these genes, which are involved in utilization and partitioning of nutrients, were examined for their effects on carcass and meat quality traits by linkage analyses. In total, 100 ESTs were screened for SNPs by single strand conformation polymorphism analyses across a diverse panel of animals with a 36% success rate. Twelve of 36 polymorphic loci segregated in a three-generation Duroc x Berlin Miniature Pig (F2) resource population, the DUMI resource population, and were genetically mapped. Interval mapping of the corresponding chromosomes was performed to verify mapping of the genes within quantitative trait loci (QTL) regions detected in this resource population. QTL with genome-wide significance were detected in the vicinity of GNMT, ESTL147 and HGD. These loci therefore are positional candidate genes.