65 research outputs found
A molecular genome scan and positional candidate gene analysis for important economic traits in the pig
Meat quality contributes considerably to the profit involved in pork production. The main objectives of this study were to identify the chromosomal locations for growth, body composition and meat quality related traits in pigs and to identify genes that control quantitative traits of economic importance. Genome scans were employed to identify these chromosomal regions. A three-generation resource family was developed using two Berkshire grand sires and nine Yorkshire grand dams. Data for 40 traits (growth, body composition and meat quality) were collected from 525 F2 progeny. Linkage analysis and regression interval mapping based on 125 microsatellite markers were used for QTL detection. Significance thresholds were determined by permutation tests. Significant QTL at the chromosome wise 5% level were detected for a total of over 100 growth (chromosomes 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, X), backfat (chromosomes 1, 4, 5, 6, 7, 10, 13, 14) and meat quality traits (chromosomes 1, 2, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 17, 18, X). Five genes (ACACB, PPP1CC, GPR49, DUSP6, ATP2B1 ) from human chromosome 12 were chosen as possible candidate genes for QTL detected on chromosome 5. These genes were successfully mapped by physical and genetic methods. Two genes (DUSP6, ATP2B1) were considered for further positional candidate gene analysis. A ATP2B1-Afl III polymorphism revealed significant associations with growth, body composition, and meat quality traits (glycogen content and potential, and ham and loin pH) in the BxY F2 population, but only with light reflectance of ham in a commercial line. A DUSP6-Pst1 Polymorphism was associated with fat traits, which was consistent in the QTL analysis and association studies in the BxY family and in two commercial lines. The identified ATP2B1 and DUSP6 polymorphisms could potentially be used as a markers to track associated QTL and to discover the causative DNA differences
Steady State In A Markov Process
It is well known that a Markov process whose transition matrix is regular approaches a steady-state distribution, or equilibrium distribution. To find these steady-state probabilities requires the solution of a system of linear homogenous equations. However, the matrix of this system is singular and thus the system has infinitely many solutions. This obstacle is overcome by replacing one of the equations of the linear homogenous system by the linear non-homogeneous equation that simply expresses the requirement that the steady-state probabilities sum to one. But which equation of the original system should be chosen to be the one replaced. This brief article demonstrates that any of the equations of the original linear system can be selected as the one to be replaced; no matter which one is selected for replacement; the revised linear system will have the same unique solution
Recovery of Low-Rank Matrices under Affine Constraints via a Smoothed Rank Function
In this paper, the problem of matrix rank minimization under affine
constraints is addressed. The state-of-the-art algorithms can recover matrices
with a rank much less than what is sufficient for the uniqueness of the
solution of this optimization problem. We propose an algorithm based on a
smooth approximation of the rank function, which practically improves recovery
limits on the rank of the solution. This approximation leads to a non-convex
program; thus, to avoid getting trapped in local solutions, we use the
following scheme. Initially, a rough approximation of the rank function subject
to the affine constraints is optimized. As the algorithm proceeds, finer
approximations of the rank are optimized and the solver is initialized with the
solution of the previous approximation until reaching the desired accuracy.
On the theoretical side, benefiting from the spherical section property, we
will show that the sequence of the solutions of the approximating function
converges to the minimum rank solution. On the experimental side, it will be
shown that the proposed algorithm, termed SRF standing for Smoothed Rank
Function, can recover matrices which are unique solutions of the rank
minimization problem and yet not recoverable by nuclear norm minimization.
Furthermore, it will be demonstrated that, in completing partially observed
matrices, the accuracy of SRF is considerably and consistently better than some
famous algorithms when the number of revealed entries is close to the minimum
number of parameters that uniquely represent a low-rank matrix.Comment: Accepted in IEEE TSP on December 4th, 201
Association of INOS, TRAIL, TGF-b2, TGF-b3, and IgL Genes with Response to Salmonella enteritidis in Poultry
Several candidate genes were selected, based on their critical roles in the host’s response to intracellular bacteria, to study the genetic control of the chicken response to Salmonella enteritidis (SE). The candidate genes were: inducible nitric oxide synthase (INOS), tumor necrosis factor related apoptosis inducing ligand (TRAIL), transforming growth factor b2 (TGF-b2), transforming growth factor b3 (TGF-b3), and immunoglobulin G light chain (IgL). Responses to pathogenic SE colonization or to SE vaccination were measured in the Iowa Salmonella response resource population (ISRRP). Outbred broiler sires and three diverse, highly inbred dam lines produced 508 F1 progeny, which were evaluated as young chicks for either bacterial load isolated from spleen or cecum contents after pathogenic SE inoculation, or the circulating antibody level after SE vaccination. Fragments of each gene were sequenced from the founder lines of the resource population to identify genomic sequence variation. Single nucleotide polymorphisms (SNP) were identified, then PCR-RFLP techniques were developed to genotype the F1 resource population. Linear mixedmodels were used for statistical analyses. Because the inbred dam lines always contributed one copy of the same allele, the heterozygous sire allele effects could be assessed in the F1 generation. Association analyses revealed significant effects of the sire allele of TRAIL-StyI on the spleen (P \u3c 0:07) and cecum (P \u3c 0:0002) SE bacterial load. Significant effects (P \u3c 0:04) were found on the cecum bacterial load for TGF-b3-BsrI. Varied and moderate association was found for SE vaccine antibody response for all genes. This is the first reported study on the association of SNP in INOS, TRAIL, TGF-b2, TGF-b3, and IgL with the chicken response to SE. Identification of candidate genes to improve the immune response may be useful for marker-assisted selection to enhance disease resistance
Genes for Resistance to Salmonella in Poultry
A unique chicken resource population was used to determine that variation in several genes is associated with either resistance to colonization with the food-safety pathogen, Salmonella, or with efficiency of vaccine response to this bacterium. Knowledge of genetic variation in genes, and their associations with traits related to Salmonella response, will help to improve the wholesomeness of the food supply and to improve animal health
DOA Estimation in Partially Correlated Noise Using Low-Rank/Sparse Matrix Decomposition
We consider the problem of direction-of-arrival (DOA) estimation in unknown
partially correlated noise environments where the noise covariance matrix is
sparse. A sparse noise covariance matrix is a common model for a sparse array
of sensors consisted of several widely separated subarrays. Since interelement
spacing among sensors in a subarray is small, the noise in the subarray is in
general spatially correlated, while, due to large distances between subarrays,
the noise between them is uncorrelated. Consequently, the noise covariance
matrix of such an array has a block diagonal structure which is indeed sparse.
Moreover, in an ordinary nonsparse array, because of small distance between
adjacent sensors, there is noise coupling between neighboring sensors, whereas
one can assume that nonadjacent sensors have spatially uncorrelated noise which
makes again the array noise covariance matrix sparse. Utilizing some recently
available tools in low-rank/sparse matrix decomposition, matrix completion, and
sparse representation, we propose a novel method which can resolve possibly
correlated or even coherent sources in the aforementioned partly correlated
noise. In particular, when the sources are uncorrelated, our approach involves
solving a second-order cone programming (SOCP), and if they are correlated or
coherent, one needs to solve a computationally harder convex program. We
demonstrate the effectiveness of the proposed algorithm by numerical
simulations and comparison to the Cramer-Rao bound (CRB).Comment: in IEEE Sensor Array and Multichannel signal processing workshop
(SAM), 201
Successive Concave Sparsity Approximation for Compressed Sensing
In this paper, based on a successively accuracy-increasing approximation of
the norm, we propose a new algorithm for recovery of sparse vectors
from underdetermined measurements. The approximations are realized with a
certain class of concave functions that aggressively induce sparsity and their
closeness to the norm can be controlled. We prove that the series of
the approximations asymptotically coincides with the and
norms when the approximation accuracy changes from the worst fitting to the
best fitting. When measurements are noise-free, an optimization scheme is
proposed which leads to a number of weighted minimization programs,
whereas, in the presence of noise, we propose two iterative thresholding
methods that are computationally appealing. A convergence guarantee for the
iterative thresholding method is provided, and, for a particular function in
the class of the approximating functions, we derive the closed-form
thresholding operator. We further present some theoretical analyses via the
restricted isometry, null space, and spherical section properties. Our
extensive numerical simulations indicate that the proposed algorithm closely
follows the performance of the oracle estimator for a range of sparsity levels
wider than those of the state-of-the-art algorithms.Comment: Submitted to IEEE Trans. on Signal Processin
Detection of imprinted QTL in the Berkshire x Yorkshire cross
Genome scans have enabled the detection of regions on chromosomes that contain genes that affect economic traits, so-called quantitative trait loci (QTL). An example is the genome scan that was conducted at ISU in an F2 cross between the Berkshire and Yorkshire breeds (Malek et al. 2001a,b). This study identified many QTL related to growth performance and meat quality. But this analysis only considered QTL with a Mendelian mode of expression. This implies that an effect of the Berkshire allele on the trait was assumed to be the same whether it was inherited from the F1 sire or from the F1 dam (see Figure 1). As a result, the two heterozygotes (BY and YB) are assumed to have the same effect (d). There is, however, evidence that the expression of some genes depends on their parental origin. For example, with paternal expression, a Berkshire gene for increased meat quality would only be expressed in the F2 progeny if it was inherited from the sire (Figure 2). In that case, individuals with QTL genotype BB and BY are expected to have the same genetic value (Figure 2), as do individuals with genotypes YB and YY. The inheritance mechanism for maternal expression is illustrated in Figure 3. In this case, genotype BB has the same value as YB, as do BY and YY
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