scholarly journals Multiple Marker Mapping of Quantitative Trait Loci in a Cross Between Outbred Wild Boar and Large White Pigs

Genetics ◽  
1998 ◽  
Vol 149 (2) ◽  
pp. 1069-1080 ◽  
Author(s):  
Sara A Knott ◽  
Lena Marklund ◽  
Chris S Haley ◽  
Kjell Andersson ◽  
William Davies ◽  
...  
Keyword(s):  
Growth Rate ◽  
Wild Boar ◽  
Quantitative Trait ◽  
Early Growth ◽  
Abdominal Fat ◽  
Chromosome 4 ◽  
Trait Variation ◽  
Large White ◽  
Intestinal Length ◽  
Trait Locus

Abstract A quantitative trait locus (QTL) analysis of growth and fatness data from a three generation pig experiment is presented. The population of 199 F2 animals was derived from a cross between wild boar and Large White pigs. Animals were typed for 240 markers spanning 23 Morgans of 18 autosomes and the X chromosome. A series of analyses are presented within a least squares framework. First, these identify chromosomes containing loci controlling trait variation and subsequently attempt to map QTLs to locations within chromosomes. This population gives evidence for a large QTL affecting back fat and another for abdominal fat segregating on chromosome 4. The best locations for these QTLs are within 4 cM of each other and, hence, this is likely to be a single QTL affecting both traits. The allele inherited from the wild boar causes an increase in fat deposition. A QTL for intestinal length was also located in the same region on chromosome 4 and could be the same QTL with pleiotropic effects. Significant effects, owing to multiple QTLs, for intestinal length were identified on chromosomes 3 and 5. A single QTL affecting growth rate to 30 kg was located on chromosome 13 such that the Large White allele increased early growth rate, another QTL on chromosome 10 affected growth rate from 30 to 70 kg and another on chromosome 4 affected growth rate to 70 kg.

scholarly journals Combined Analyses of Data From Quantitative Trait Loci Mapping Studies: Chromosome 4 Effects on Porcine Growth and Fatness

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1369-1378 ◽  
Author(s):  
Grant A Walling ◽  
Peter M Visscher ◽  
Leif Andersson ◽  
Max F Rothschild ◽  
Lizhen Wang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Birth Weight ◽  
Wild Boar ◽  
Population Data ◽  
Study Data ◽  
Joint Analysis ◽  
Chromosome 4 ◽  
European Wild Boar ◽  
Mapping Populations ◽  
Combined Data

Abstract For many species several similar QTL mapping populations have been produced and analyzed independently. Joint analysis of such data could be used to increase power to detect QTL and evaluate population differences. In this study, data were collated on almost 3000 pigs from seven different F2 crosses between Western commercial breeds and either the European wild boar or the Chinese Meishan breed. Genotypes were available for 31 markers on chromosome 4 (on average 8.3 markers per population). Data from three traits common to all populations (birth weight, mean backfat depth at slaughter or end of test, and growth rate from birth to slaughter or end of test) were analyzed for individual populations and jointly. A QTL influencing birth weight was detected in one individual population and in the combined data, with no significant interaction of the QTL effect with population. A QTL affecting backfat that had a significantly greater effect in wild boar than in Meishan crosses was detected. Some evidence for a QTL affecting growth rate was detected in all populations, with no significant differences between populations. This study is the largest F2 QTL analysis achieved in a livestock species and demonstrates the potential of joint analysis.

Identification of a quantitative trait locus on rat chromosome 4 that is strongly linked to femoral neck structure and strength

Bone ◽  
2006 ◽  
Vol 39 (1) ◽  
pp. 93-99 ◽  
Author(s):  
I. Alam ◽  
Q. Sun ◽  
L. Liu ◽  
D.L. Koller ◽  
T. Fishburn ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Femoral Neck ◽  
Quantitative Trait ◽  
Chromosome 4 ◽  
Trait Locus

scholarly journals Gpr63 is a novel modifier of microcephaly in Ttc21b mouse mutants

2018 ◽  
Author(s):  
J. Snedeker ◽  
WJ Gibbons ◽  
D.R. Prows ◽  
R.W. Stottmann
Keyword(s):  
Quantitative Trait ◽  
Genetic Background ◽  
Chromosome 6 ◽  
Chromosome 4 ◽  
Genetic Modifiers ◽  
Causal Gene ◽  
Mouse Mutants ◽  
Signaling Center ◽  
Trait Locus ◽  
G Protein Coupled

ABSTRACTThe primary cilium is a critical signaling center for proper embryonic development. Previous studies have demonstrated that mice lacking Ttc21b have impaired retrograde trafficking within the cilium and multiple organogenesis phenotypes, including microcephaly. Interestingly, the severity of the microcephaly in Ttc21baln/aln homozygous null mutants is considerably affected by the genetic background. Ttc21baln/aln mutants on an FVB/NJ background develop a forebrain significantly smaller than mutants on a C57BL/6J background. We performed a Quantitative Trait Locus (QTL) analysis to identify potential genetic modifiers and identified two regions linked to differential forebrain size: modifier of alien QTL1 (Moaq1) on chromosome 4 at 27.8 Mb and Moaq2 on chromosome 6 at 93.6 Mb. These QTLs were validated by constructing congenic strains. Further analysis of Moaq1 identified a brain specific orphan G-protein coupled receptor (GPCR), Gpr63, as a candidate gene. We identified a SNP between the FVB and B6 strains in Gpr63, which creates a missense mutation predicted to be deleterious in the FVB protein. We first demonstrated that Gpr63 can localize to the cilium and then used CRISPR-Cas9 genome editing to create FVB congenic mice with the B6 sequence of Gpr63 and a deletion allele leading to a truncation of the GPR63 C-terminal tail. These alleles genetically interact with Ttc21baln/aln, validating Gpr63 as a forebrain modifier of Ttc21b and strongly supporting Gpr63 as the variant causal gene (i.e., the quantitative trait gene, QTG) for Moaq1.

scholarly journals Limited genetic parallels underlie convergent evolution of quantitative pattern variation in mimetic butterflies

2020 ◽  
Author(s):  
Hannah E. Bainbridge ◽  
Melanie N. Brien ◽  
Carlos Morochz ◽  
Patricio A. Salazar ◽  
Pasi Rastas ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Qtl Analysis ◽  
Quantitative Trait ◽  
Convergent Evolution ◽  
Genetic Basis ◽  
Major Effect ◽  
Multivariate Approach ◽  
Trait Variation ◽  
Heliconius Melpomene ◽  
Trait Locus

AbstractMimetic systems allow us to address the question of whether the same genes control similar phenotypes in different species. Although widespread parallels have been found for major effect loci, much less is known about genes that control quantitative trait variation. In this study, we identify and compare the loci that control subtle changes in the size and shape of forewing pattern elements in two Heliconius butterfly co-mimics. We use quantitative trait locus (QTL) analysis with a multivariate phenotyping approach to map the variation in red pattern elements across the whole forewing surface of Heliconius erato and Heliconius melpomene. These results are compared to a QTL analysis of univariate trait changes, and show that our resolution for identifying small effect loci is improved with the multivariate approach. QTL likely corresponding to the known patterning gene optix were found in both species but otherwise, a remarkably low level of genetic parallelism was found. This lack of similarity indicates that the genetic basis of convergent traits may not be as predictable as assumed from studies that focus solely on Mendelian traits.

scholarly journals Haplotype analysis within quantitative trait locus affecting intramuscular fat content on porcine chromosome

2011 ◽  
Vol 56 (No. 12) ◽  
pp. 521-528
Author(s):  
S. Sato ◽  
C. Ohnishi ◽  
Y. Uemoto ◽  
E. Kobayashi
Keyword(s):  
Quantitative Trait ◽  
Haplotype Analysis ◽  
Intramuscular Fat ◽  
Fat Content ◽  
Porcine Chromosome ◽  
Large White ◽  
Positional Candidate ◽  
Synonymous Substitutions ◽  
Trait Locus ◽  
Intramuscular Fat Content

Previous results of fine mapping for quantitative trait loci affecting intramuscular fat content identified a 3.0-Mb chromosome interval on porcine chromosome 7, which contains at least 9 genes, based on the pig genome assembly. Therefore, we proposed these nine genes (LOC100154481, LOC100155711, LOC100155276, SPATA7, PTPN21, ZCH14, EML5, TTC8, and FOXN3) as positional candidate genes. The coding exons of the nine genes were characterized, and 45 polymorphisms were detected in F<sub>2</sub> Duroc &times; Meishan population. Within the nine genes, 10 non-synonymous substitutions and 1 insertion were genotyped among three European breeds (Landrace, Large White, and Duroc) and 1 Chinese breed (Meishan). Genotyping data was used to perform the haplotype analysis. Polymorphisms were found in all the studied genes, except ZCH14. We surveyed the frequency of 33 haplotypes that formed non-synonymous substitutions in four breeds. One of them was distributed widely in the Landrace, Large White, and Meishan breeds, but not in Duroc. Each breed had different major haplotypes. &nbsp;

scholarly journals GROWTH AND SOME CARCASS CHARACTERISTICS OF GROWING PIGS FED FULL-FAT SOYBEAN-BASED CONTAINING THREE PROTEIN SUPPLEMENTS

2021 ◽  
Vol 25 (1) ◽  
pp. 46-51
Author(s):  
A. O. FANIMO
Keyword(s):  
Growth Rate ◽  
Processing Time ◽  
Fish Meal ◽  
General Trend ◽  
Carcass Characteristics ◽  
Abdominal Fat ◽  
Growing Pigs ◽  
Large White ◽  
Protein Supplements ◽  
Better Than

Thirty-six large White X landrace growing pigs were randomly in a 3 x 3 factorial arrangement. The two dietary variables were protein source (Fish meal FM Chicken offal meal-COM and full-fat soybean - FFSB) and processing time of FFSB (30, 60, 90 minutes). Pigs on 90 minutes processed FFSB performed slightly better than those on 60 and 30 minutes as regard growth rate and feed to gain ration. The diets had no significant effect on slaughtering performance. However, the general trend showed a numerical increase in estimated loin eye area, back fat and abdominal fat with increased processing time of FFSR. Chicken offal meal also slightly increased the backfat and abdominal fat  

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