The Impacts of Modern Agriculture on Plant Genetic Diversity
Bill Pritchard, Rodomiro Ortiz, Meera Shekar in Routledge Handbook of Food and Nutrition Security, 2016
The evaluation of phenotype is essential for improving crops. Assigning a phenotype to a qualitative trait is quite straightforward, while ascertaining the phenotype of quantitative traits is more challenging because their expression is influenced by the environment. Having a wide array of genomic data and breeding strategies available, the ability to design crop cultivars basically hinges on the ability to phenotype and hence uncovering the associations worth selecting for the expected improvement. The potential for good phenotyping relies on measuring repeatedly large number of plants with reliable accuracy, i.e., high-throughput phenotyping. More and more tools (e.g., cameras, lasers, infrared thermometers etc.) have been used, as well as methods and platforms developed to facilitate the fast and objective measurement of complex traits for the detection of useful diversity that can be incorporated in modern breeding. Genetics and physiology have converged along with the increase of data analysis capacity.
ENTRIES A–Z
Philip Winn in Dictionary of Biological Psychology, 2003
Genes influence complex processes, but it is highly likely that the effects of genes on most psychological and behavioural processes (and indeed medical conditions) come about through the action not of single genes but of multiple genes. For example, it is inconceivable that INTELLIGENCE is the product of a single gene: any genetic influence on this will come about through the action of multiple genes. In this regard one refers to QUANTITATIVE TRAIT LOCI: these are genes that have varying degrees of effect in multi-gene processes and systems, each one contributing to the variability in an overall PHENOTYPE. Candidate gene analysis is the general name for the procedure that is used to identify individual genes that contribute to some particular process or condition. It is a procedure increasingly used, though still difficult to apply: the central difficulty lies in determining which genes to investigate as candidates for control of a specific process. For further discussion of this, see Plomin et al. 1997.
Genetics and exercise: an introduction
Adam P. Sharples, James P. Morton, Henning Wackerhage in Molecular Exercise Physiology, 2022
In contrast, the so-called quantitative traits, such as body height or VO2max trainability, tend to be normally distributed, and their variation in the population is typically influenced by many DNA variants, environmental factors and their interactions. The most thoroughly investigated quantitative trait is human body height. Studies involving hundreds of thousands of subjects suggest that body height is influenced by thousands of DNA variants (3). Each of the body height-influencing DNA sequence variants adds or subtracts a fraction of a millimetre to or from the individual’s height. Studying such traits requires the analysis of DNA variants across the entire genome in many thousands of subjects. The inability to meet these requirements remains a limitation of sport and exercise genetics studies to this day (4).
The diagnostic value of systemic immune-inflammation index in diabetic macular oedema
Published in Clinical and Experimental Optometry, 2022
Ahmet Elbeyli, Bengi Ece Kurtul, Sait Coskun Ozcan, Deniz Ozarslan Ozcan
All statistical analyses were performed by SPSS software (version 21.0, Inc., Armonk, NY, USA). Kolmogorov–Smirnov test was used to evaluate the normality test for the numeric variables. Normally distributed continuous data were presented as means and standard deviations (±SD) and non-normally distributed data were presented as median (inter-quartile range, IQR). Categorical variables were presented as numbers and percentages. Independent Samples t-test was used to compare the means of quantitative traits and the chi-square test was used for qualitative traits. Pearson’s correlation analysis was performed for the correlation of the SII, NLR and PLR with BCVA. The multivariate logistic regression model with a forward procedure was performed to find out independent predictors of DME. Multiple logistic regression analysis was conducted for DME using fasting blood glucose, duration of diabetes, SII, NLR and PLR as explanatory variables. Variables achieving univariate p < 0.10 were included in multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was carried out to determine the optimal cut-off values of SII, NLR and PLR. The areas under the curve (AUC) values were also calculated. A p value of less than 0.05 was accepted as statistically significant.
Terminology and Consistency
Published in The American Journal of Bioethics, 2023
The difficulty in conveying the biological basis of such polygenic testing is mentioned by Bowman-Smart et al. but is not given its full weight. The heritability of most complex traits—the fraction of the variance in the trait that can be attributed to underlying genetic factors—is often of the order of 50%, and the fraction of this that can be accounted for by molecular investigations is usually modest (15–20%), so the polygenic scores draw on only a few of the relevant influences, the rest being excluded from analysis. When offered to most pregnant women, such tests are so likely to cause confusion that it will often be unethical to make the offer at all. A paper cited by Bowman-Smart et al. acknowledges that consent will be “challenging” but sets any reservations aside, arguing that such difficulties in comprehension apply to many other important decisions, e.g. such as those about insurance policies (Chen and Waserman 2017). This is an inadequate response, when the context of pregnancy raises the stakes so high.
Human height: a model common complex trait
Published in Annals of Human Biology, 2023
Mitchell Conery, Struan F. A. Grant
Aside from exploring how well their GWAS saturated the discovery of height genetics, Yengo et al. also investigated the ability of their results to accurately predict height across ancestries using polygenic scores. Polygenic scores and risk scores predict individuals’ values and risks of complex traits and diseases respectively. Generally, these terms refer to statistical models that make genetic-based predictions using GWAS-estimated variant effect sizes (Sugrue and Desikan 2019; Wang et al. 2022), though under some definitions they may denote any genetics-based statistical or machine-learning model designed to predict phenotypes (Wand et al. 2021). Prior to the latest height GWAS, polygenic scores and risk scores had shown relatively modest success at predicting traits and disease risk for individuals drawn from the population used to create the scoring metrics (Schrodi et al. 2014; Hu et al. 2017). Though generally used on unrelated individuals, at least in the case of height polygenic scores, they can differentiate between siblings (Lello et al. 2020). It has been similarly shown that in order for these scores to obtain their maximum accuracy, they should include both coding and non-coding associated variants (Yong et al. 2020).
Related Knowledge Centers
- Classical Genetics
- DNA Sequencing
- Genetic Variation
- Mendelian Inheritance
- Polygene
- Quantitative Genetics
- Quantitative Trait Locus
- Heritability
- Locus
- Genetic Architecture