ORCID Profile
0000-0002-3512-2257
Current Organisations
University of South Australia
,
Australian Centre for Precision Health (ACPreH)
,
Chittagong Veterinary and Animal Science University
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Publisher: Wiley
Date: 07-12-2022
Abstract: Ultra‐flexible stretchable organic light‐emitting diodes (OLEDs) are emerging as a basic component of flexible electronics and human‐machine interfaces. However, the brightness and efficiency of stretchable OLEDs remain still far inferior to their rigid counterparts, owing to the scarcity of satisfactory stretchable electroluminescent materials. Herein, we explore a general concept based on the self‐confinement effect to dramatically improve the stretchability of elastomers, without affecting electroluminescent properties. The balanced rigid/flexible chain dynamics under self‐confinement significantly reduces the modulus of the elastomers, resulting in the maximum strain reaching 806 %. Ultra‐flexible stretchable OLEDs have been constructed based on the resulting ISEEs, achieving unprecedented high‐performance non‐blended stretchable OLEDs. The results suggest an effective molecular design strategy for highly deformable stretchable displays and flexible electronics.
Publisher: Cold Spring Harbor Laboratory
Date: 03-08-2023
DOI: 10.1101/2023.08.01.551571
Abstract: Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong’s method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and R 2 . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong’s method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and R 2 values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European in iduals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.
Publisher: Springer Science and Business Media LLC
Date: 09-02-2023
DOI: 10.1038/S41467-023-36281-X
Abstract: Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is erse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.
Publisher: Cold Spring Harbor Laboratory
Date: 20-09-2021
DOI: 10.1101/2021.09.16.460619
Abstract: Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is erse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.
Publisher: Science Publishing Group
Date: 2016
Publisher: Elsevier BV
Date: 02-2023
Publisher: Indonesian Center for Animal Research and Development (ICARD)
Date: 07-2019
Abstract: class="abstrak2" The study was carried out at Chittagong district of Bangladesh with a predesigned well-structured questionnaire to know the baseline information of indigenous sheep and effects of protein supplementations on fertility. Three iso-caloric but different graded levels of protein containing rations were supplied to the three different groups of sheep in three locations. The morphometric traits of sheep such as hair length, ear length, tail length, body length and quantitative trait, body weight in the location 3were higher than the other two locations. Hair length of male (1.91±0.01cm) was longer than female whereas the average body length, tail length and body weight of females were higher than the males. All the correlation values was positive, where the highest value was observed among the body weight, body length and withers height (r=0.73) and the lowest value was observed in between chest girth and ear length (r=0.25). Considering the qualitative traits percentage of plain coat color, non-pigmented skin color, brown coat color and semi-pendulous ear found maximum than others and the values were 54.21%, 69.16%, 45.79%, 57.01%, respectively. The semen volume, sperm counts, percentages of normal and viable sperm were higher in treatment 2 than the other two groups. The present study concluded that there is an influence of protein supplementation on reproductive performance especially semen profile in ram and this outcome will create a new horizon of sheep production in Bangladesh.
Publisher: Cold Spring Harbor Laboratory
Date: 03-07-2022
DOI: 10.1101/2022.07.03.498620
Abstract: The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped in iduals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP. Despite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor ( α ), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.
Publisher: Cold Spring Harbor Laboratory
Date: 02-02-2023
DOI: 10.1101/2023.01.31.23285307
Abstract: While cholesterol is essential for human life, a high level of cholesterol is closely linked with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have been successful to identify genetic variants associated with cholesterol, which have been conducted mostly in white European populations. Consequently, it remains mostly unknown how genetic effects on cholesterol vary across ancestries. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries for cholesterol. We find significant genetic heterogeneity between ancestries for total- and LDL-cholesterol. Furthermore, we show that single nucleotide polymorphisms (SNPs), which have concordant effects across ancestries for cholesterol, are more frequently found in the regulatory region, compared to the other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog ( www.ebi.ac.uk/gwas/ details are in web resources section). These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings
Publisher: Agricultural Research Communication Center
Date: 29-03-2022
Abstract: Background: Growth of animals is important for milk and meat production. In developing countries record keeping is difficult and usually complete recording cannot be obtained from the cattle farming. Mathematical models are used to predict values from incomplete or partially recorded data and reduces the confusion for calculating the yield. Therefore, a study was conducted to know the growth of cattle by comparing three non-linear models and predicts the mature live weight. Mehods: The live weight of cattle at 15 day intervals from 15 to 365 days was recorded and calculated the yearly live weight and weight gains of three genotypes (Red Chittagong cattle, Non-descriptive deshi and their cross) of cattle. Three nonlinear model (Brody, Gompertz and von Bertalanffy model) was fitted to weight-age data of 120 female cattle from 15 to 365 days in three locations. Result: The average live weight and weight gain of three genotype cattle ranging from 198.5 to 207.89 kg and 306 to 361 g/day, respectively. The brody model provides better goodness of fit than other models in all genotypes and Gompertz and the von Bertalanffy model showed better matched between the observed and estimated weights
Publisher: Cold Spring Harbor Laboratory
Date: 21-07-2023
DOI: 10.1101/2023.07.20.549816
Abstract: The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an in idual’s genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing GxE PRS models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose a novel GxE PRS model that correctly incorporates the GxE component to analyze complex traits and diseases. Through extensive simulations, we demonstrate that our proposed model outperforms existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed model to real data, and report significant GxE effects. Specifically, we highlight the impact of our model on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index (BMI) by alcohol intake frequency (ALC). In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension (HYP) by waist-to-hip ratio (WHR). These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxE PRS, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, while providing a practical tool to support further research in this area.
Publisher: Oxford University Press (OUP)
Date: 03-06-2022
DOI: 10.1093/TAS/TXAC072
Abstract: A study was carried out to know the impact of protein supplementation on fertility and expressions of the fertility gene BMP1R. Three International Organization for Standardization (ISO), isocaloric but different levels of protein supplement ration (11.70% crude protein [CP] for control/To, 12.99% CP for T1, and 13.86% CP for T2) were fed to three different groups of sheep. DNA was extracted from the whole blood s le for polymerase chain reaction (PCR) of the BMP1R fertility gene, and purified PCR products were sequenced by a Sanger sequencer. Sequence alignment, pair, and multi-alignment comparison of the BMP1R gene of the species were done with MEGA6. The semen volume (1.0 mL), sperm counts (4.2 × 107 million), and percentage of normal (94.3%) and viable sperm (3.7%) were higher in treatment 2 than in the other two groups. The semen volume (1.0 mL), sperm counts (4.2 × 107 million), and the percentage of normal (94.3%) and viable sperm (3.7%) were higher in treatment 2 than in the other two groups. Ewes treated with supplemented, protein concentrate reached the conception at an earlier age (treatment 1, 9.5 ± 0.16 mo and treatment 2, 10.3 ± 0.04 mo) than control (9.8 ± 0.15 mo). The lambing interval varied, from 198 to 202 d. Lamb’s birth weights in three treated groups were ranging from 1.2 to 1.39 kg. The designated sequences of BMP1R gene revealed 100% homology with the sequence of Kazakh sheep. The present study indicated that the influence of nutrition on reproductive performance and genomic study will be helpful for the genetic improvement of low-productive sheep.
Publisher: Cold Spring Harbor Laboratory
Date: 10-06-2022
DOI: 10.1101/2022.06.08.495250
Abstract: The coefficient of determination ( R 2 ) is a well-established measure to indicate the predictive ability of polygenic scores (PGS). However, the s ling variance of R 2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGS based on different discovery s les, the s ling covariance of R 2 is necessary to test the difference between them. Here, we show how to estimate the variance and covariance of R 2 values to assess the 95% CI and p-value of the R 2 difference. We apply this approach to real data to predict into 28,880 European participants using UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGS compared to BBJ PGS (p-value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGS significantly improves the predictive ability, compared to a model of UKBB PGS only (p-value 3.5e-05 for cholesterol and 1.3e-28 for BMI). The proposed approach can also be applied to testing a significant difference between R 2 values across different p-value thresholds. We also show that the predictive ability of regulatory SNPs is significantly enriched than non-regulatory SNPs for cholesterol (p-value 2.6e-19 for UKBB and 8.7e-08 for BBJ). We suggest that the proposed approach (available in R package ‘r2redux’) should be used to test the statistical significance of difference between pairs of PGS, which may help to draw a correct conclusion about the predictive ability of PGS.
Publisher: Agricultural Research Communication Center
Date: 02-05-2022
Abstract: Background: Scanty reports are available in the literature about the coat colour inheritance and its related genes in cattle. Therefore, an experiment was conducted to know the expression of coat colour gene, MC1R in cattle. Methods: DNA was extracted from 85 whole blood s les of Red Chittagong cattle (RCC), Non-descriptive deshi and their crossbred of which 75 positive s les were sequenced by Sanger sequencer. Sequence alignment, pair and multi-alignment comparison of the MC1R gene of different genotype and a phylogenic tree constructed by MEGA6 software. Result: RCC has a dominant R gene for red colour. Point mutation was observed at 954 bp and substitution (395G→A) was found in the MC1R gene of RCC genotype. The evolutionary history of branching pattern showed the relatedness of MC1R nucleotide sequences of cattle and this gene have been regulating coat colour inheritance of cattle.
Publisher: Frontiers Media SA
Date: 08-06-2023
DOI: 10.3389/FGENE.2023.1104906
Abstract: The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped in iduals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter & . The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.
Publisher: Wageningen Academic Publishers
Date: 31-12-2022
Publisher: The Korean Society of Animal Reproduction and Biotechnology (KSARB)
Date: 30-09-2016
Publisher: Informa UK Limited
Date: 2020
Publisher: Agricultural Research Communication Center
Date: 12-11-2020
DOI: 10.18805/IJAR.B-1285
Abstract: Background: An adaptive meat and egg type indigenous chicken is crucial for countries those depends on rural poultry production for meeting the protein requirements of the peoples. Genetic characterizations of native chickens have been documented, however, no study has observed the plumage colouration and its potential role in production traits. Thus, the aim of the current study was to know the effect of PME17, plumage colour gene ersity on production performance of indigenous chicken varieties.Methods: The plumage colours, comb and body shape of chickens corresponds with the live weight and egg production (clutch size) and the egg characteristics were recorded. Gel electrophoresis and polymerase chain reactions (PCR) were performed from blood cell DNA following standard protocols. The PCR products were sequenced using Sanger sequencing and for molecular analysis MEGA6 software were used. Result: Highest live weight (1400±25.4 g) and egg production (15.3±0.9 /number /clutch) was obtained in spotted-single-round chicken than other varieties. Both external and internal egg characteristics differed between varieties and spotted- single-round variety found to be best than other varieties. The sequence of PMEL17 gene was 99% homology with the sequence of Gallus gallus and Gallus gallus domesticus. A mutation was observed at 91bp nucleotide in brownish and at 64bp positional nucleotide and in black-white chicken variety.
Location: Bangladesh
No related grants have been discovered for Md. Moksedul Momin.