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From rice fields to genes: How to use quantitative traits to improve crop yields?
As the world faces the threat of population growth and climate change, increasing crop yields has become a pressing goal of agricultural research. Complex traits, phenotypes controlled by multiple genes, are important for understanding how crops respond to environmental changes and thereby improving yield. This article will explore how to address this challenge by quantifying traits.
Overview of Complex Traits
Complex traits are also called polygenic traits and are usually affected by multiple genes and environmental factors. For example, the yield and color of crops are complex traits, and the expression of these traits often shows continuous changes. In contrast to Mendelian laws of inheritance, these traits cannot be explained by the transmission of a single gene.
The vast majority of traits are complex traits and cover all aspects of crops.
Analysis of Quantitative Traits
In research, quantitative trait analysis is widely used to explore how genes affect crop yield. These traits can be divided into the following categories:
1. Quantitative traitsQuantitative traits can often be assessed as continuous values and are influenced by multiple genes. For example, the number of grains in rice or the yield in corn are both quantitative traits, and these crops exhibit different yields due to genetic and environmental influences. By analyzing these traits, researchers can understand how genes interact to improve crop yields.
2. Defining characteristics
Compared to quantitative traits, finite traits are expressed as integers, such as the number of eggs a chicken lays per week. This trait has a more significant adaptive response to the environment. By analyzing its performance in different environments, scientists are able to grasp the key factors of crop growth.
3. Threshold traitsThreshold traits are traits related to health conditions, such as the occurrence of a disease. Such traits are often driven by multiple genetic and environmental factors. For example, the development of diabetes is often influenced by many genes and can vary with lifestyle changes.
Methods for finding complex traits
The researchers used a variety of methods to find genes associated with the quantified traits. Here are the main methods:
1. Twin studiesTwin studies analyze the effects of environmental factors on crop traits by observing the performance of monozygotic twins (identical genes) and dizygotic twins (different genes). This method can effectively identify the heritability of various traits.
2. Quantitative trait loci (QTL) mapping
QTL mapping is a technique for analyzing the association between genes and traits. By genotyping a group of crop genotypes that behave differently and comparing their phenotypes, researchers can identify genetic regions that may influence specific traits. Although this process is complex, it reveals the key technology to increase crop yields.
3. Genome-wide association study (GWAS)QTL mapping can identify genetic regions and guide future research focus.
GWAS detects variations in all gene loci by comparing a population that expresses a specific trait with a population that does not have that trait. This approach not only helps identify genetic variants, but also understands the role of environmental factors in crop performance.
Exploration of gene organization structure
Gene organization is the process of understanding all the genetic factors that influence a particular trait. By constructing mathematical models and statistical analyses, researchers can gain a deeper understanding of how multiple genes work together and simultaneously assess the effects of genes under different environments.
ConclusionThe study of complex traits reveals the complexity of the interaction between genes and the environment.
Although extensive research has revealed many genes and their interactions that influence crop yield, many challenges remain, particularly in determining how these genes specifically affect performance. With the global need to increase food production becoming increasingly urgent, can we find more effective ways to exploit these complex traits to improve crop yields?
