Genetic traits can be separated into two broad categories; simple traits which have discrete
phenotypes and complex traits which have continuous phenotypes.
Complex traits are controlled by a finite number of genes; each with a discrete influence
on the overall phenotype.
However, the number of genes is so large that the impact of any one gene is overshadowed
by environmental variation to give the illusion of continuity.
To make things more difficult, the nature of independent assortment meant it was impossible
to study any complex gene in isolation.
It was like trying to pilot a 747, except you could only change dials randomly.
The challenge for geneticists was how do you make sense of the controls?
In 1988, Paterson and Lander sought to answer this question for three complex traits in
tomatoes: fruit mass, pH, and soluble solid concentration.
Their approach involved positioning quantitative trait loci (or QTLs) within the genome by
their association with genetic markers.
These markers were DNA polymorphisms in the cutting sites of common enzymes – known
as restriction fragment length polymorphisms (or RFLPs).
These markers don’t influence complex traits themselves but the frequency at which a particular
marker’s genotype corresponds to a change in complex phenotype would inform the distance
between a marker and the real QTL.
Imagine this like a gold prospector who pans the nearby rivers for flakes.
He cannot determine the gold deposit’s location directly, but with many indirect measurements
he can make deductions which greatly narrow down his search.
As with prospecting in genetics, more information leads to greater accuracy and Paterson and
Lander’s RFLP map contained 70 markers with an average density of 14.3 cM across all twelve
chromosomes.
The researchers selected parents with vastly different phenotypes to maximise the number
of detectable QTLs.
These were Lycopersicon esculentum (denoted E) and Lycopersicon chmielewski (denoted CL)
which were bred through the following backcross design to produced 237 plants.
Paterson and Lander genotyped the plants into E-type and CL-type subsets for each of the
70 markers.
Next, they partitioned the interval between two adjacent markers into several points and
estimated the probability that a QTL existed at each.
These probabilities were divided by the probability that no QTL existed for the same point, and
the logarithm of these ratios gave the set of LOD scores.
An LOD score of three was considered significant, which meant a 1000:1 chance that the proposed
QTL existed.
Finally, they estimated the effect of the most likely putative QTL using the Maximum
Likelihood method – adapted by Lander himself.
The researchers described the position and effect of fourteen QTLs spread across the
three traits.
Early experiments in complex analysis were able to prove the existence of QTLs but could
not accurately locate them or quantify their effect.
This limitation arose because these early papers relied on morphological markers, which
could be genotyped by observation of an organism.
However, these were scarce and unevenly distributed.
It was nearly impossible to find a set of these markers which covered every 50 cM linkage
region – and thus, they left blind spots in the genome.
Paterson and Lander broke through this barrier with RFLP markers.
Since these markers were simply nucleotide polymorphisms, they were much more ubiquitous
than morphological markers which required functional changes to a gene.
The abundance of information provided by RFLPs made interval mapping and Maximum Likelihoods
feasible for the first time – and Lander’s methods remain prominent in the field.
In this manuscript, QTL mapping evolved a level of detail and elegance analogous to
Thomas Morgan’s map of Drosophila, and these pioneering achievements did not go unnoticed.
Paterson and Lander produced a result which encouraged other scientists to follow suit
and jump-started research in an exciting new direction.
QTL maps were produced for many of the world’s most important crops.
And immense interest was generated in developing new marker technologies.
Which in turn facilitated more accurate analyses of increasingly complex organisms – notably
wheat and sugarcane.
Recently, SNP markers have made it possible to genotype changes in a single nucleotide,
and the genome wide study has made it possible to adapt QTL analysis to higher order mammals.
Both technologies are currently being used to understand the genetic factors which underpin
human disease.
And that brings us to an end to our story – who’d’ve thought the humble tomato
would’ve played such a role in unravelling the mysteries of genetics.