Dihybrid Crosses, Linkage and Epistasis

For two independently assorting heterozygous loci AaBb, four gamete types AB, Ab, aB and ab have equal expected probabilities under the simple model. Combining two such parents gives a 9:3:3:1 phenotype ratio only when both loci show complete dominance and there is no gene interaction affecting those phenotype classes.

Probability multiplication can be more efficient than drawing sixteen boxes: for example, P(aabb)=P(aa)×P(bb)=1/4×1/4. Label assumptions. If two events are not independent, multiplying their separate probabilities without adjustment is not justified.

With codominance, both allele effects contribute recognisably to the heterozygous phenotype rather than one simply masking the other. A population may contain more than two alleles at a locus, although a typical diploid individual carries only two. ABO-style inheritance can combine multiple alleles and codominance when a question defines the symbols.

Epistasis is interaction between loci in which a genotype at one locus changes or masks expression at another. An enzyme pathway may require both steps before a pigment is produced. Work from the given biochemical rule to group genotypes into phenotypes; modified ratios are consequences of the model, not a separate list to memorise without explanation.

Loci on the same autosomal chromosome can be inherited together more often than independent assortment predicts. A double heterozygote’s allele arrangement matters: AB/ab and Ab/aB describe different combinations on homologues. Crossing over can create recombinant chromatids, with parental gamete types often more frequent for linked nearby loci.

Use observed offspring classes to identify parental and recombinant combinations under a suitable test-cross design. Recombination can approach the independent pattern for sufficiently separated loci; a count difference alone does not prove one exact physical distance without an appropriate model and enough evidence.

Try each question before opening the worked response. Use precise biological terms, connect each cause to its effect, and check units and assumptions when interpreting data.