QTL Mapping Using RIL Population

Genetic maps are an excellent tool for the analysis of important traits, the development of which is the result of the combined expression of several genes, enabling the genomic localization of the factors determining them. Such features, characterized by a normal distribution of values, are referred to as quantitative or polygenic. The analysis of their genetic background using a chromosome map is called the mapping of quantitative traits loci (QTL). QTL analysis is a statistical method of determining the genetic association of phenotypic data (trait measurements) with genotypic data (DNA markers assigned to linkage groups).There are numerous tools developed for QTL mapping. This chapter introduces Windows QTL Cartographer with Composite Interval Mapping (CIM) method, which estimates the QTL position by combining interval mapping with multiple regression. The genotypic and phenotypic data used in the exemplary QTL mapping procedure were obtained for the recombinant inbred line (RIL) population of rye. Plant height, assessed in three seasons, was the exemplary trait under study.

Unravelling of the genetic diversity, population structure, and phylogenetic relationships of the indigenous horse breeds of Pakistan

The current study aimed to evaluate the genetic diversity, level of admixture, and phylogenetic relationship, of the Pakistani horse breeds, along with their morphological characterization. Data for the body measurements showed that Morna horses had the highest values of body height, body length, chest girth, leg length, and head length, whereas the Baluchi horses had the lowest values for these traits. For the genetic diversity 64 animals, 15/breed except for Baluchi(14) and Topras(05), were genotyped by using the 17-plex equine genotyping kit. The AMOVA results showed that 13% of genetic diversity was explained by breed differences, whereas 27% and 60% came from among and within individuals respectively. The highest values of genetic diversity parameters including Na(7.29±0.29), Ne(5.73±0.28), Ho(0.74±0.05) and He(0.82±0.01) were observed for Morna, whereas their lowest values were found for Topras. However, the highest value of inbreeding coefficient (Fis) was found for Baluchi and the lowest for KB horses. Among the markers, CA425 was found as the most polymorphic and ASB23 as the least polymorphic and highly fixed marker. Results of structure analysis revealed that, except Topras, each local horse breed had very admixed genetic structure perhaps due to their continuous crossing with other breeds in the past. Moreover, the structure analysis also showed that Morna and Shiean breeds had mixing of each other which was also confirmed by the lowest value of their pairwise Fst values and likewise the phylogenetic analysis also showed their close genetic relationship with each other. The phylogenetic analysis also revealed that Shiean breed had close genetic relationship with Arabian horses. Collectively, our data showed that Morna is the largest and genetically most diverse horse breed of Pakistan, whereas Baluchi horses are the smallest in size and have the highest values of inbreeding coefficient. And the phylogeny analysis showed that Shiean breed had close genetic relationship with the Arabian.

DeepGA for automatically estimating fetal gestational age through ultrasound imaging

Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to assess fetal growth. However, this procedure incurs considerable overhead and suffers from bias caused by the operators, yielding suboptimal estimations. To address this challenge, we develop an automatic DeepGA model to achieve fully automatic GA prediction in an end-to-end manner. Our model uses a deep segmentation model (DeepSeg) to accurately identify and segment three critical tissues, including the cranium, abdomen, and femur, in which their morphology is automatically extracted. After that, we are able to directly estimate the GA via a deep regression model (DeepReg). We evaluate DeepGA on a large dataset, including 10,413 ultrasound images from 7113 subjects. It achieves superior performance over the traditional measurement approach, with a mean absolute estimation error (MAE) of 5 days. Our DeepGA model is a novel automatic solution on the basis of artificial intelligence learning that can help radiologists improve the performance of GA estimation in various clinical scenarios, thereby enhancing the efficiency of prenatal examinations.

Biometric measurement with a commercially available swept-source optical coherence tomography in myopia model species

Background

Biometric parameters play an important role in studies on myopia. This study aimed to explore the application of a commercially available two-dimensional visualized swept-source optical coherence tomography (OCT) system, for in vivo biometric measurement in multiple myopia model species.

Methods

In this study, chickens, guinea pigs, and C57BL/6 mice underwent eye imaging with the commercially available OCT (CASIA2), and the original images were used to calculate the central corneal thickness, anterior chamber depth (ACD), lens thickness (LT), vitreous chamber depth (VCD), and axial length (AL). The retinal thickness and choroidal thickness were also calculated in chicken eyes. The repeatability of the biometric measurement outcomes was analyzed.

Results

Excellent repeatable AL measurements were obtained for all three species, with an intraclass correlation coefficient (ICC) of ≥0.941 and a within-subject standard deviation of ≤0.055. Excellent repeatability was found in chicken eyes for ACD, LT, and VCD, with an ICC of ≥0.932; in guinea pig eyes for ACD and VCD, with an ICC of ≥0.934; and in mouse eyes for LT, with an ICC of ≥0.941.

Conclusions

It is effective to use commercially available OCT to measure biometric parameters in chickens, guinea pigs, and C57BL/6 mice. This methodology could potentially increase the accuracy and efficiency of future myopia animal experiments.

A Prenatal Ultrasound Scanning Approach: One-Touch Technique in Second and Third Trimesters

This study was aimed at evaluating the performance of the innovative technique Smart Fetus (SF) developed to recognize the planes and obtain the basic biometric measurements of fetuses automatically. This prospective study included 1005 uncomplicated singleton pregnancies undergoing routine examinations. For every pregnancy, planes, including the transverse section of the thalami, transverse section of the abdomen and longitudinal section of the femur, were acquired, and standard biometric measurements, including biparietal diameter, head circumference, abdominal circumference and femur length, were obtained using SF and traditional ultrasound technique (TUT). The accuracy, reproducibility and time required for the analysis of SF were compared with those of TUT. In 998 of 1005 cases (99.30%), SF successfully acquired the sections and made all measurements. The agreement between the techniques was high for all measurements. The time to obtain sections and measure biometric parameters or solely measure biometric parameters was significantly shorter with SF than with TUT. No significant differences were found in SF repeated measurements obtained by two independent observers. The SF technique helped in the acquisition of reliable standard sections and biometric measurements and saved time. It might serve as a novel ultrasound scanning approach and improve workflow efficiency.