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Sourav PanNovember 4, 2024

Explain why observed ratios often differ from expected ratios, especially when there are small numbers of offspring

Explain why observed ratios often differ from expected ratios, especially when there are small numbers of offspring

Sourav Pan
Sourav PanNovember 4, 2024

Answered step-by-step

Observed ratios of phenotypes in genetic crosses often differ from expected ratios due to several factors, particularly when the number of offspring is small. Here are the primary reasons for these discrepancies:

1. Sampling Effects

  • When dealing with a small number of offspring, random sampling can lead to results that do not accurately reflect the expected ratios. For example, if you expect a 3:1 ratio in a monohybrid cross but only have a few offspring, it’s possible to observe 2 tall and 1 short plant purely by chance. This random fluctuation can result in significant deviations from the expected ratios.

2. Survival Rates of Genotypes

  • Not all genotypes have equal survival rates. Some combinations of alleles may lead to traits that are less viable or less likely to survive to adulthood. For instance, if a certain genotype is lethal during embryonic development, those individuals will not be counted in the final phenotype tally, skewing the observed ratios.

3. Environmental Influences

  • Environmental factors can affect the expression of traits, leading to variations in phenotype that are not accounted for by genotype alone. For example, if a plant’s growth is stunted due to poor soil conditions, it may not reach its expected height, impacting the observed phenotype ratios.

4. Genetic Drift

  • In small populations, genetic drift can cause allele frequencies to fluctuate randomly over generations. This randomness can lead to unexpected ratios as certain alleles become more or less common purely by chance rather than through selection or other evolutionary processes.

5. Limited Sample Size

  • Small sample sizes inherently have higher variability and lower reliability in reflecting true population parameters. The smaller the sample size, the greater the impact of random chance on the observed results. Larger sample sizes tend to yield results that align more closely with expected ratios due to averaging effects.

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