Genome-wide analysis of population structure, effective population size and inbreeding in Iranian and exotic horses.

Population structure and genetic diversity are the key parameters to study the breeding history of animals. This research aimed to provide a characterization of the population structure and to compare the effective population size (Ne), LD decay, genetic diversity, and genomic inbreeding in Iranian native Caspian (n = 38), Turkmen (n = 24) and Kurdish (n = 29) breeds and some other exotic horses consisting of Arabian (n = 24), Fell pony (n = 21) and Akhal-Teke (n = 20). A variety of statistical population analysis techniques, such as principal component analysis (PCA), discriminant analysis of principal component (DAPC) and model-based method (STRUCTURE) were employed. The results of the population analysis clearly demonstrated a distinct separation of native and exotic horse breeds and clarified the relationships between studied breeds. The effective population size (Ne) for the last six generations was estimated 54, 49, 37, 35, 27 and 26 for the Caspian, Kurdish, Arabian, Turkmen, Akhal-Teke and Fell pony breeds, respectively. The Caspian breed showed the lowest LD with an average r2 value of 0.079, while the highest was observed in Fell pony (0.148). The highest and lowest average observed heterozygosity were found in the Kurdish breeds (0.346) and Fell pony (0.290) breeds, respectively. The lowest genomic inbreeding coefficient based on run of homozygosity (FROH) and excess of homozygosity (FHOM) was in the Caspian and Kurdish breeds, respectively, while based on genomic relationship matrix) FGRM) and correlation between uniting gametes) FUNI) the lowest genomic inbreeding coefficient was found in the Kurdish breed. The estimation of genomic inbreeding rates in the six breeds revealed that FROH yielded lower estimates compared to the other three methods. Additionally, the Iranian breeds displayed lower levels of inbreeding compared to the exotic breeds. Overall, the findings of this study provide valuable insights for the development of effective breeding management strategies aimed at preserving these horse breeds.

Genome-wide study of linkage disequilibrium, population structure, and inbreeding in Iranian indigenous sheep breeds.

Knowledge of linkage disequilibrium (LD), genetic structure and genetic diversity are some key parameters to study the breeding history of indigenous small ruminants. In this study, the OvineSNP50 Bead Chip array was used to estimate and compare LD, genetic diversity, effective population size (Ne) and genomic inbreeding in 186 individuals, from three Iranian indigenous sheep breeds consisting of Baluchi (n = 96), Lori-Bakhtiari (n = 47) and Zel (n = 47). The results of principal component analysis (PCA) revealed that all animals were allocated to the groups that they sampled and the admixture analysis revealed that the structure within the populations is best explained when separated into three groups (K = 3). The average r2 values estimated between adjacent single nucleotide polymorphisms (SNPs) at distances up to 10Kb, were 0.388±0.324, 0.353±0.311, and 0.333±0.309 for Baluchi, Lori-Bakhtiari and Zel, respectively. Estimation of genetic diversity and effective population size (Ne) showed that the Zel breed had the highest heterozygosity and Ne, whereas the lowest value was found in Baluchi breed. Estimation of genomic inbreeding using FROH (based on the long stretches of consecutive homozygous genotypes) showed the highest inbreeding coefficient in Baluchi and the lowest in Zel breed that could be due to higher pressure of artificial selection on Baluchi breed. The results of genomic inbreeding and Ne showed an increase in sharing haplotypes in Baluchi, leading to the enlargement of LD and the consequences of linkage disequilibrium and haplotype blocks confirmed this point. Also, the persistence of the LD phase between Zel and Lori-Bakhtiari was highest indicating that these two breeds would be combined in a multi-breed training population in genomic selection studies.

De-oiled soy lecithin positively influenced growth performance, nutrient digestibility, histological intestinal alteration, and antioxidant status in turkeys fed with low energy diets.

1. The purpose of this research was to investigate the effects of supplementing an emulsifier (de-oiled soybean lecithin (DSL)) in a low metabolisable energy (ME) diet on growth performance, nutrient digestibility, carcase characteristics, intestinal morphology, blood metabolites, and antioxidant status in growing turkeys.2. A total of 480 one-day-old turkeys were assigned to one of four dietary treatments with of eight replicates of 15 birds each. Experimental treatments included a basal diet (BE) with commercially recommended levels of ME, a reduced energy diet (RE) with 0.42 MJ/kg reduction in dietary ME content, the RE diet + 1 g/kg DSL (DSL-1), and RE + 2 g/kg DSL (DSL-2).3. After 112 days, the body weight, average daily gain, and feed:gain in turkeys fed the supplemented for BE diets were better (P < 0.05) than in those fed RE, and those fed diet DSL-2 had the best performance. Although the RE diet decreased abdominal fat and relative liver weight (P < 0.05), compared to the BE diet, and supplementation with either level of DSL did not influence these variables.4. There were linear increases (P < 0.05) in fat digestibility, nitrogen-corrected apparent ME, and duodenal villus height, villus height/crypt depth ratio, and villus surface area in LE diet supplemented with DSL. From the jejunal morphology, crypt depth was decreased by DSL-supplemented diets (P < 0.05).5. Serum triglyceride, total cholesterol, and malondialdehyde concentrations were lower, whereas the serum superoxide dismutase activity was greater for the DSL-2 group compared to the BE and RE groups (P < 0.05).6. The findings suggested that, while low-ME diets impaired turkey growth performance, dietary supplementation of DSL could reverse such impacts of these diets. The DSL-supplemented diet at the inclusion level of 2 g/kg was advantageous over both BE and RE diets in terms of intestinal morphology, lipid profile, and antioxidant status in growing turkeys.

Genetic diversity in Iranian indigenous sheep vis-à-vis selected exogenous sheep breeds and wild mouflon.

The heterogeneity of climate and different agro-ecological conditions in Iran have resulted in development of 27 indigenous sheep breeds. Wild Asiatic mouflon (Ovis orientalis) is believed to be the ancestor of Iranian sheep. Evaluation of genetic diversity and population structure within and among domestic breeds has important implications for animal breeding programs and genetic resources management. Based on 50K SNP genotype data, we studied the genetic diversity of five indigenous Iranian sheep breeds: Afshari (n = 37), Moghani (n = 34), Qezel (n = 35), Zel (n = 46) and Lori-Bakhtiari (n = 46), and Asiatic mouflon (n = 8) sampled from Iran. Furthermore, genetic diversity and the breed admixture of Iranian sheep were assessed on a larger geographic scale using a reference panel comprising: three indigenous Afghan breeds - Arabi (n = 15), Balouchi (n = 15) and Gadik (n = 15); three indigenous breeds from Turkey and Cyprus - Cyprus Fat Tail (n = 30), Karakas (n = 18) and Norduz (n = 20); and three commercial European breeds - Suffolk (n = 19), Comisana (n = 24) and Engadine Red Sheep (n = 24). The results revealed that the investigated breeds are divided into five genetically distinct clusters according to their geographic origin. Afshari was closest to the local mouflon population and showed signs of mouflon admixture. Qezel was identified as a hybrid sheep breed. Much evidence supported the Afghan breeds being identical. Inbreeding values, which were estimated based on ROHs, were highest for Suffolk (FROH  = 0.0544) and lowest for Balouchi (FROH  = 0.0078). In conclusion, analysis of selected breeds from neighboring countries along with Asiatic mouflon gave a deeper insight into the evolutionary history and origin of Iranian sheep with important implications for future breed management.

Assessing selection signatures within and between selected lines of dual-purpose black and white and German Holstein cattle.

The aim of this study was to detect selection signatures considering cows from the German Holstein (GH) and the local dual-purpose black and white (DSN) population, as well as from generated sub-populations. The 4654 GH and 261 DSN cows were genotyped with the BovineSNP50 Genotyping BeadChip. The geographical herd location was used as an environmental descriptor to create the East-DSN and West-DSN sub-populations. In addition, two further sub-populations of GH cows were generated, using the extreme values for solutions of residual effects of cows for the claw disorder dermatitis digitalis. These groups represented the most susceptible and most resistant cows. We used cross-population extended haplotype homozygosity methodology (XP-EHH) to identify the most recent selection signatures. Furthermore, we calculated Wright's fixation index (FST ). Chromosomal segments for the top 0.1 percentile of negative or positive XP-EHH scores were studied in detail. For gene annotations, we used the Ensembl database and we considered a window of 250 kbp downstream and upstream of each core SNP corresponding to peaks of XP-EHH. In addition, functional interactions among potential candidate genes were inferred via gene network analyses. The most outstanding XP-EHH score was on chromosome 12 (at 77.34 Mb) for DSN and on chromosome 20 (at 36.29-38.42 Mb) for GH. Selection signature locations harbored QTL for several economically important milk and meat quality traits, reflecting the different breeding goals for GH and DSN. The average FST value between GH and DSN was quite low (0.068), indicating shared founders. For group stratifications according to cow health, several identified potential candidate genes influence disease resistance, especially to dermatitis digitalis.

Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods.

1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

Detecting selection signatures in three Iranian sheep breeds.

The objective of genome mapping is to achieve valuable insight into the connection between gene variants (genotype) and observed traits (phenotype). Part of that objective is to understand the selective forces that have operated on a population. Finding links between genotype-phenotype changes makes it possible to identify selective sweeps by patterns of genetic variation and linkage disequilibrium. Based on Illumina 50KSNP chip data, two approaches, XP-EHH (cross-population extend haplotype homozygosity) and FST (fixation index), were carried out in this research to identify selective sweeps in the genome of three Iranian local sheep breeds: Baluchi (n = 86), Lori-Bakhtiari (n = 45) and Zel (n = 45). Using both methods, 93 candidate genomic regions were identified as harboring putative selective sweeps. Bioinformatics analysis of the genomic regions showed that signatures of selection related to multiple candidate genes, such as HOXB9, HOXB13, ACAN, NPR2, TRIL, AOX1, CSF2, GHR, TNS2, SPAG8, HINT2, ALS2, AAAS, RARG, SYCP2, CAV1, PPP1R3D, PLA2G7, TTLL7 and C20orf10, that play a role in skeletal system and tail, sugar and energy metabolisms, growth, reproduction, immune and nervous system traits. Our findings indicated diverse genomic selection during the domestication of Iranian sheep breeds.

Quantitative analysis of left ventricular performance from sequences of cardiac magnetic resonance imaging using active mesh model.

In this study, the local and global left ventricular function are estimated by fitting three-dimensional active mesh model (3D-AMM) to the initial sparse displacement which is measured from an establishing point correspondence procedure. To evaluate the performance of the algorithm, eight image sequences were used and the results were compared with those reported by other researchers. The findings were consistent with previously published values and the clinical evidence as well. The results demonstrated the superiority of the novel strategy with respect to formerly presented algorithm reported by author et al. Furthermore, the results are comparable to the current state-of-the-art methods.

A novel approach in R peak detection using Hybrid Complex Wavelet (HCW).

In this letter, by design of Complex Morlet Wavelet and Complex Frequency B-Spline Wavelet and linearly combining them, a novel approach, Hybrid Complex Wavelet, has been proposed to identify and detect the components of ECG signal such as QRS complex and R peak. By train and test of implementing the proposed method on both clinically recorded signals from 40 patients and 30 signals of MIT BIH database, we reached better recognition accuracy in comparison to other well-known approaches.

Continuous detection of motor imagery in a four-class asynchronous BCI.

Asynchronous Brain Computer Interface (BCI) is an important class of BCI systems that has not received enough attention from the BCI community. In this work we introduce for the first time a system for classification of four different motor imageries in the context of an asynchronous BCI system which distinguishes between periods of movement imagination occurrence and idling or resting periods of ongoing EEG signal as well as classifying the 4 class motor imageries. We used two multi class extensions of the method of Common Spatial Patterns (CSP) for feature extraction and LDA, SVM, and MDA well known classifiers for combination purposes. We have applied our procedure to data set IIIa from BCI Competition III [2]. Offline evaluation of a prototype system demonstrated true positive rates in the range of 56%-88% with corresponding false positive rates in the range of 18%-9%.

A comparison approach toward finding the best feature and classifier in cue-based BCI.

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain-Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.