Is there a biological explanation for a 0.5 difference in allele size with PCR product?


I am currently working on a set of diversity, this diversity in interspecific (within the same genus). I am using SSR markers, the primers were designed on one species and are working really well within the species (lot of diversity and difference between allele of 1 at least).
When trying them on the other species, I observe the normal non-amplification which could be due to mutation in the primer DNA sequence. But, for some genotypes I have been observing a new allele just between the two older ones (like the new one is 185.5 and the olders were 185 and 186.


Is that kind of observation frequent when using primers designed on other species?
I understand how a 1 pb difference could occurCACACA==>CACCACA, but what could the biological explanations be for a 0.5 pb difference between alleles?

In my experience, working with a similar approach in Campylobacter jejuni, the base pair measurements from these techniques are imprecise and need to be carefully calibrated. I am unsurprised to see 0.5 base pair differences, this can be seen been runs and even, in the worse case situation, between even and odd wells in the same plate(!).

I would use Sanger sequencing to determine the sequence you are getting in one of your isolates, and then use this as a calibration control included in every run.

CH 16

It increases the number of heterozygotes in a population.

It is more likely to occur in a large population than in a small population.

It increases the frequency of rare alleles in a population.

It is a form of genetic drift.

Founding members contain a tiny fraction of the alleles found in the original population.

It produces a high frequency of some rare alleles in a small isolated population.

Disruptive selection leads to polymorphism, favoring different forms of the same species.

Directional selection occurs when one extreme phenotype is favored over another different extreme phenotype.

Stabilizing selection favors an intermediate phenotype over either of the extreme phenotypes.

Directional selection leads to improved selection in a stable environment.

frequency dependent selection.

both the Founder Effect and the bottleneck effect are examples of disruptive selection

both the Founder Effect and the bottleneck effect result from increase gene flow

both the Founder Effect and the bottleneck effect result from mutation

all male and female English sparrows that reside in your community

all of the human population of a rural western town

all of the corn plants in a cornfield

all of the variable-colored ladybird beetles of the species Harmonia axyridis in a forest

0.25 homozygous dominant, 0.50 heterozygous, and 0.25 homozygous recessive, and a 0.75 dominant allele frequency and a 0.25 recessive allele frequency.

0.50 homozygous dominant, 0.25 heterozygous, and 0.25 homozygous recessive, and a 0.5 allele frequency for each allele.

0.75 homozygous dominant and 0.25 homozygous recessive, and a 3-to-1, right-to-left handed allele ratio in the population.

0.25 homozygous dominant, 0.50 heterozygous, and 0.25 homozygous recessive, and a 0.5 allele frequency for each allele.

Is there a biological explanation for a 0.5 difference in allele size with PCR product? - Biology

Allele frequencies (or percentages, if you prefer) in a population will remain in Hardy-Weinberg Equilibrium (HWE) from generation to generation if the following assumptions are met:

  1. Natural selection is not occurring
  2. Migration (Gene Flow) is not occurring
  3. Mutation is not occurring
  4. Genetic Drift is not occurring (drift is less likely in populations of large size)
  5. Mating occurs at random

Although these assumptions are rarely true in the natural world, they allow us to calculate an expected allele frequency. Significant differences between the observed and expected frequencies indicate that "something" (i.e. one or more of the above) is going on, and therefore tell us that "microevolution" is occurring.

Calculating Expected Allele and Genotype frequencies:

In the simplest possible situation we have a single gene with only two alleles. These alleles might be A and a, or A1 and A2. Let's say that A or A1= tall, and a or A2= short. Don't worry for now whether the alleles are dominant and recessive or co-dominant. They will have frequencies p and q in a population. (Because there are only two possibilities and they have to add up to 100%, p + q = 1.)

If we know the allele frequencies, we can predict the genotype frequencies. The expected genotype frequencies of the two alleles are calculated as shown. This ought to look familiar: it's our old friend the Punnet's Square. Allele A or A1 has a frequency of p, and allele a or A2 has a frequency of q. Multiply the allele frequencies to the get the probability of each genotype.

p q A1 p p 2 pq A2 q pq q 2

In other words, p 2 + pq + pq + q 2 = 1, or 100%. The expected frequencies of the genotypes are therefore:

Genotype Expected Frequency
AA or A1A1 p * p = p 2
Aa or A1A2 pq + pq (or 2pq)
aa or A2A2 q * q = q 2

Let's take a look at some graphs of this to make it a little easier to see. For values of p from 0 to 1, in intervals of 0.1, here's what we get:

Red represents the frequency of the AA or A1A1 genotype, green is the Aa or A1A2 genotype, and blue is the aa or A2A2 genotype.

All of the above has to do with the allele and genotype frequencies we would expect to see. Next, let's look at the real world situation so we can compare.

Calculating Observed Allele and Genotype Frequencies:

In a real world population, we can only see phenotypes, not genotypes or alleles. However, in a population of genotypes AA, Aa and aa, the observed frequency of allele A equals the sum of all of the AA genotype plus half of Aa genotype (the A half). The observed frequency of allele a is therefore half of the Aa individuals (the a half) plus all of aa individuals . If you know one value, you can of course just subtract it from 1 (100%) to get the value of the other. In other words, the observed frequency of A = 100%(AA) + 50%(Aa) and a = 50%(Aa) and 100%(aa)

Phenotype Genotype Makeup Frequency
Tall AA 100% A p 2
Tall A a 50% A and 50% a 2pq
Short aa 100% a q 2
Phenotype Genotype Makeup Frequency
Tall A1A1 100% A1 p 2
Medium A1 A2 50% A1 and 50% A2 2pq
Short A2 A2 100% A2 q 2

Tip: If the alleles are codominant, each phenotype is distinct (you can distinguish between tall, medium and short) and your job is easier. If the alleles are dominant and recessive, we can't visually tell the homozygous AA from the heterozygous Aa genotypes (both are tall), so it's best to start with the homozygous recessive (short) aa individuals. Count up the aa types and you have the observed q 2 . Then, take the square root of q 2 to get q, and then subtract q from 1 to get p. Square p to get p 2 and multiply 2*p*q to get the observed heterozygous Aa genotype frequency.

If observed and expected genotype frequencies are significantly different , the population is out of HWE.

Genotype Frequencies
AA Aa aa
Genotype Frequencies
A1A1 A1A2 A2A2

Question: Why might observed and expected phenotype frequencies differ? Imagine the following scenarios where natural selection is at work. Situation one favors only one tail of the distribution. Perhaps the tallest, perhaps the shortest, but not both. This is directional selection. Now imagine that both tails of the distribution are selected against, and only the middle is favored. This is called stabilizing selection. Next imagine that the extremes on both ends are favored. This is called disruptive selection. In each of these scenarios, what would happen over time?

Before (dotted line) and after (yellow shaded area) directional selection, stabilizing selection, and disruptive selection.

One common misconception is that dominant alleles will rise in frequency and recessive alleles will decline in frequency over time. In reality, allele frequencies will not change from one generation to the next if the assumptions listed above are not violated. A good example of this is human ABO blood type. Type O blood is recessive but it remains the most common.

In the hwe.xlsx Excel Spreadsheet, there are three examples to help make this more concrete.

Example 1 : Allele A is dominant and allele a is recessive. Set the original frequencies of p (allele A) and q (allele a) at 0.6 and 0.4 in Generation 1. These are highlighted in blue. All other numbers are calculated from these two original data points. The frequency of genotype AA is determined by squaring the allele frequency A. The frequency of genotype Aa is determined by multiplying 2 times the frequency of A times the frequency of a. The frequency of aa is determined by squaring a. Try changing p and q to other values, ensuring only that p and q always equal 1. Does it make any difference in the results?

Example 2 : Alleles A 1 and A 2 are co-dominant. In this case, A 1 is at a frequency of 0.25 and A 2 is at a frequency of 0.75.

Example 3 : Alleles A and a are dominant and recessive. Note that allele A is at very low frequency despite being dominant. Does it increase in frequency?

The second sometimes confusing thing about HWE is that after all of the examples above, you may wonder if it is possible for the observed and expected frequencies to differ. Here's an example where they do:

In a population of snails, shell color is coded for by a single gene. The alleles A 1 and A 2 are co-dominant. The genotype A 1 A 1 makes an orange shell. The genotype A 1 A 2 makes a yellow shell. The genotype A 2 A 2 makes a black shell. 1% of the snails are orange, 98% are yellow, and 1% of the snails are black.

Observed frequency of A 1 allele = 0.01 + 0.5(.98) = 0.50 = 50%

p 2 = Expected frequency of A 1 A 1 = 0.25

2pq = Expected frequency of A 1 A 2 = 0.50

q 2 = Expected frequency of A 2 A 2 = 0.25

Phenotype Orange Yellow Black
Genotype A 1 A 1 A 1 A 2 A 2 A 2
Observed 1% 98% 1%
Expected 25% 50% 25%
Difference -24% +48% -24%

There are significantly fewer orange and black snails than expected, and significantly more yellow snails than expected. It appears that this is a case of stabilizing selection, since both tails appear to be strongly selected against.


Patient samples collection and DNA extraction

HER2 Ile655Val-containing genomic DNA was obtained from frozen sections of breast tissue samples collected from M. Djamil Hospital Padang, West Sumatera Province. Genomic DNA was then extracted followed manual tissue DNA extraction protocol (Pure Link Genomic DNA Mini Kit Invitrogen, Thermo Fisher Scientific Inc., USA). The types of HER2 Ile655Val alleles of this DNA have been confirmed using Sanger DNA sequencing and AS-PCR as described in the previous study [13]. These genomic DNA were used in AS-PCR for genotyping errors study and its optimization. Another clinical samples were collected and their DNA were extracted using the same protocol, then the types of SNP HER2 Ile655Val were established using AS-PCR. We collected five breast cancer samples that showed HER2 Ile655Val heterozygote. We coded these samples as number 1 to 5 for simplicity. All subjects enrolled in this experiment were approved by the local ethics committee, issued by the Ministry of Health, the Republic of Indonesia.

PCR reagents

PCR Supermix reagent for AS-PCR experiment was purchased from Invitrogen, Thermo Fisher Scientific.Inc., USA. All primers as listed in Table 1 were purchased from Integrated DNA Technologies.Inc., USA. Betaine was purchased from Sigma-Aldrich, USA. Betaine was stored as 10 M stock in dH2O at −20 °C.

Allele-specific PCR experiment for gentyping errors evaluation

Qualitative evaluation of AS-PCR for the occurance of genotyping errors was done using mixed DNA template model with varying the amount of DNA template with AA genotype from 1, 0.25, 0.06 and 0.01 ng while kept DNA template with GG genotype at 1 ng vice versa. Each of 12.5 mL reaction mixture contained 11.25 mL of PCR Super Mix buffer, primers, and templates. The PCR amplification profile was as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 20 s, annealing temperature at 54.3 °C for 20 s, and extension at 72 °C for 30 s. All PCR tubes, distilled water, pipette tips, and pipettes were pre-treated by exposing on UV-light for 15–20 min prior to use.

Quantitative evaluation of AS-PCR to evaluate the rate of genotyping errors due to ADO and LDO was done using multiple tube assay as suggested by Taberlet et al. [18]. This method offers quantification of genotyping errors not only caused by stochastic effect due to low DNA template but also quantifying errors due to PCR-related conditions when standard DNA template was used during amplification. The genotyping errors were tested for each variant of HER2 Ile655Val alleles and errors rate as a percentage was calculated based on a total number of amplicon with correct genotype divided with amplicon with incorrect genotype or no-amplification. Preferential amplification was considered as correct genotype in a case of heterozygous DNA. AS-PCR condition followed the composition as mentioned above. For evaluating the effect of DNA template amount on genotyping errors, the annealing temperature was set at 54.3 °C. While for evaluating the effect of annealing temperature on genotyping errors for each homozygous DNA, the amount of DNA template was used as much as 0.4 ng and for heterozygous DNA was mixed 1 ng each.

Allele-specific PCR experiment for genotyping errors elimination

Pre-PCR treatment by betaine and modifying AS-PCR program on mixed DNA template sample model and clinical samples that showed preferential amplification was done. In mixed DNA template model, 0.125 M and 0.25 M betaine were added into PCR solutions that contain both of SNP-known DNA templates with differing the amount of DNA template ratio. For this purpose, AS-PCR components and AS-PCR program followed the condition as mentioned in the first AS-PCR experiment. The occurrence of preferential amplifiication on betained-treated AS-PCR experiment, then was further modified by changing the AS-PCR condition as follow: initial denaturation at 96 °C for 5 min, followed by first 10 cycles of denaturation at 96 °C for 20 s or 40 s, annealing temperature at 54.3 °C for 40 s, and extension at 72 °C for 30 s. While for the next 20 cycles of AS-PCR, the same program as at the first AS-PCR experiment was used. For clinical samples, 0.5 ng of DNA template was used and the same protocol was applied to eliminate genotyping errors. Analysis of the AS-PCR products was done by electrophoresis in 3% agarose gels containing 1× TE buffer. Staining was with ethidium bromide. Pictures of the stained gels were taken with Canon IXUS camera under UV-light exposure.

Bioinformatics analysis of AS-PCR amplicon

Bioinformatics analysis of G-quadruplex sequences in HER2 Ile655Val amplicon with GG genotype used QGRS Mapper ( [32] with the optional setting: maximum lenght of the sequence was 30, minimal G-group was 2, and loop size 0 to 36.


We show that sexually antagonistic selection is theoretically able to maintain genetic variation at the Cyp6g1 locus, and these findings were confirmed in experimental fly populations. To date, only one other study has characterised the evolutionary dynamics of a specific sexually antagonistic allele [31]. That study similarly found that sexual antagonism was able to maintain genetic variation at the antagonistic locus. However, that was an artificial experimentally constructed allele, whereas in our study, we examined the impact of sexually antagonistic selection on a naturally occurring resistance allele.

At present the negative effects of DDT-R on male fitness have only been seen on one of two genetic backgrounds examined (Canton-S) [23]. However, as we show here, in principle, sexual antagonism could maintain genetic variation at the locus. Furthermore, and as noted above, the Canton-S background has not coevolved with DDT-R, so we can observe the consequences of intralocus conflict before potential modifiers evolve to offset negative fitness effects [25].

Our results have important consequences for the maintenance of genetic variation generally, as intralocus conflict is ubiquitous [7, 9, 13] and conflict resolution is difficult [12, 32]. We have assumed complete dominance of DDT-R (based on its resistance phenotypes), but sex-specific dominance patterns need further investigation as they can have major impact on the genetic architecture of intralocus conflict and may provide an additional avenue through which genetic variation can be maintained [18].

Our findings could also broadly explain the historical DDT-R allele frequency patterns seen in nature and therefore provides the first unifying explanation for a range of somewhat discordant information on DDT-R (the allele was present before DDT use, increases female fitness but did not increase in frequency until widespread DDT use). This has important implications for applied aspects of resistance, including insect pest management, and shows the potential of insect resistance systems to shed light on fundamental questions of evolutionary dynamics. Finally, we show that identifying naturally occurring sexually antagonistic alleles, and estimating selection on them is possible, despite the difficulty associated with mapping sexually antagonistic traits to specific genes [21].

Part 2: Units and dilutions

To adequately work in a genetics or molecular biology lab it is imperative to be comfortable with units of measurement and to be able to convert among units. Most of the time geneticists are working with liquids, so common units of measurement include milliliters (m) and microliters (μ). Below is a table illustrating the different notations.

Notation Factor Name Symbol
10 -1 0.1 deci d
10 -2 0.01 centi c
10 -3 0.001 milli m
10 -6 0.000001 micro μ
10 -9 0.000000001 nano n
10 -12 0.000000000001 pico p
10 1 10 deka da
10 2 100 hecto h
10 3 1000 kilo k
10 6 1000000 mega M
10 9 1000000000 giga G
10 12 1000000000000 tera T

Using the table above, make the following unit conversions. Determine this by moving the decimal point the appropriate number of places.

In addition to being able to convert among different units of measurement, geneticists also need to make what is called a working stock solution. A working stock solution is a reagent or other solution that is directly used in reactions such as PCR, which we will study later on in the course. Most often, working stock solutions are made by diluting an original stock solution that is at a concentration higher than what is needed for the experiment. By concentration we mean the amount of solutes in a given volume. The concentration of solutes in a solution is usually measured by molarity (M), which is the number of moles per liter of solution (moles/L). For example, 1 M = 1 mole/L. To determine how many grams of a substance is equal to one mole the molecular mass is needed (we will not explore this in this lab). The general formula for making a dilution solution is the following:

C1 = the original concentration of stock solution

V1 = the volume of stock solution needed to make working solution

C2 = the desired concentration of working solution

V2 = the desired final volume of working solution

How would you make a 100 μL working stock at a concentration of 0.5 μM from a stock solution at a concentration of 0.1 mM? Always remember to put everything into the same units first!

What would be the working stock concentration (in molarity) if you diluted 250 μL of a stock concentration of 10 μM in a total volume of 1 mL?

End of Chapter Review Questions

When the product of the lacZ gene, beta-galactosidase, interacts with the substrate X-Gal on LB agar, colonies turn blue.

-genetic maps indicate the relative position of genes as they are aligned along the chromosome, but does not give information about the space between the genes
-physical maps are based on the sequence of the chromosome and give information on all the base pairs but may not give information on which base pairs are genes

Which trait would best respond to artificial selection by the farmer? A: Shell color (59/112= .53)

-Template DNA is incubated with primers, nucleotides, and a thermostable polymerase in a buffer with Mg2+Mg2+ ions (required for polymerase activity).

-PCR reaction:
-There is an initial denaturation step at 95 °C and then the following steps are cycled 25-30 times.

1) 95 °C - Denaturation Time - to generate ssDNA
2) 55 °C - Annealing Time - primers bind to complementary regions of the template DNA
3) 68 °C - Extension Time - the polymerase extends the primers to form dsDNA.

-The incubation times depend on the size of the DNA being copied.

-After 25-30 cycles, there is a final extension step at 68 °C to ensure the polymerase completes the extension of all DNA strands.

Integrated microfluidic systems for genetic analysis

13.4.4 Short tandem repeat analysis

STR assays have become an indispensable and routine technique in forensic investigations due to their ability to produce highly distinctive profiles from minute amounts of DNA ( Andersen et al., 1996 Chakraborty et al., 1999 Jobling and Gill, 2004 Butler, 2006 ). The current STR typing process includes DNA extraction from collected evidences, DNA quantitation for sample characterisation, multiplex PCR amplification using a STR typing kit, and amplicon separation and detection using a slab gel or CE. Although the automation of the STR analytical process is underway using robotics ( Butler et al., 2004 Montpetit et al., 2005 Greenspoon et al., 2006 ), a mere displacement of manual operations with automated instruments can only provide a limited degree of improvement, as this process is still performed in μL-scale volume on several bulky instruments. The limited genotyping technologies and the rising number of DNA samples submitted for DNA testing have resulted in an escalating backlog of crime scene evidence pending examination in forensic laboratories around the world. In addition to the huge demands for STR typing, forensic investigators are facing a unique challenge, in that forensic casework samples usually have lower amplification efficiency due to DNA degradation by exposure to environmental elements or natural contaminants, or low-copy-number DNA extracted from ‘touch evidence’ ( Gill et al., 2000 Whitaker et al., 2001 Wickenheiser, 2002 Butler et al., 2003 ). Mixture samples from several contributors are also often encountered in forensic investigations ( Clayton et al., 1998 Gill et al., 1998 ).

Microfabrication technology could also substantially improve STR typing in the near future. Some impressive progress has been achieved and the translation of casework sample typing to microchip-based instruments is underway. Ehrlich et al. demonstrated baseline resolved separations of single-locus STR samples in 30 s using a microfabricated CE microchip with a 2.6-cm-long separation channel ( Schmalzing et al., 1997 ). Analyses of PCR samples containing four loci (CSF1PO, TPOX, THO1, and vWA) were completed in less than two minutes. More impressive work demonstrated by Mathies et al. consisted of a 96-channel microfabricated capillary array electrophoresis (μCAE) device coupled with a four-colour confocal fluorescence scanner for high-throughput STR typing ( Yeung et al., 2006a ). A prototype of the Berkeley μCAE device was successfully installed at the Virginia Department of Forensic Science for testing of routine forensic STR analyses ( Greenspoon et al., 2008 ). The successful transfer of this technology from an academic environment to a forensic laboratory indicates that chip-based CE technology is poised for application in forensic laboratories.

As mentioned in the previous section on DNA sequencing, sample preparation steps, such as PCR and post-PCR clean-up, are also being integrated into CE microdevices to achieve further improvements in STR typing. Not only does such an integrated analytical process expedite the analysis and lower the cost, but also prevent contaminations and sample mix-up, an outcome extremely valuable in forensic investigations. Liu et al. constructed an integrated PCR-CE microdevice for forensic STR analysis, as well as a portable analysis instrument containing all the electronics and optics for chip operation and four-colour fluorescence detection ( Liu et al., 2007 ). Multiplex amplification of amelogenin and three Y STR loci (DYS390, DYS393, DYS439) with 35 PCR cycles and CE separation were completed in 1.5 h with a detection limit of 20 copies of genomic DNA. To further explore the concept of performing rapid STR analyses in a setting outside a forensic laboratory, they conducted real-time DNA analyses at a mock crime scene ( Liu et al., 2008 ). The crime scene was investigated following standard procedures, and three blood stain samples were extracted, amplified and correctly typed at the scene. A DNA profile search against a mock CODIS database with a ‘convicted offender’ sample was successfully conducted within six hours of arrival at the crime scene. The successful demonstration of on-site STR typing at a crime scene validates the feasibility of real-time human identification for crime scene investigation, as well as mass disaster and security checkpoint applications.

To improve the sensitivity of the conventional STR analysis, post-PCR sample purification prior to CE has been employed ( Smith and Ballantyne, 2007 ). Yeung et al. have developed an integrated STR sample clean-up and separation microdevice and method that employ a streptavidin capture gel chemistry coupled to a simple direct-injection geometry ( Yeung et al., 2009 ). Compared to microchip CE experiments performed using a cross-injector under similar conditions, the fluorescence intensity can be improved by 10–50 times for monoplex samples, and 14–19-fold for nine-plex STR products. The analyses of two artificial degraded DNA samples on the inline-injection CE microchip provided

71% more allelic markers respectively than those on the cross-injection chip. This enhanced sensitivity is highly valuable for low-copy-number and degraded DNA typing. Furthermore, the capture structure incorporated into the high-throughput μCAE has also been reported ( Liu et al., 2011b ). The near 100% amplicon product transfer between processing steps provided by the capture, concentration and inline-injection method should significantly advance the STR typing on the sensitivity, robustness and data quality for a variety of applications.

The miniaturisation of other ‘upstream’ STR steps is also under investigation towards fully integrated microfluidic systems for automated STR analysis. For example, Bienvenue et al. developed a single-channel extraction device and method for on-chip sperm cell lysis and DNA purification, which has direct implications on forensic STR analysis ( Bienvenue et al., 2006 ). The purity and concentration of the DNA samples obtained from on-chip extraction were verified using conventional STR amplification and separation. Later, they reported another glass microdevice that integrated a silica bead column and a PCR chamber together for fast DNA extraction and STR amplification from whole blood samples ( Bienvenue et al., 2010 ). Recently, several true sample-in-answer-out microsystems were finally published. As shown in Fig. 13.2 , Hopwood et al. successfully developed an integrated microfluidic system that consists of a DNA extraction and amplification cartridge coupled with a CE microchip for rapid STR analysis from reference buccal samples ( Hopwood et al., 2010 ). An aliquot of 150 μL of the sample lysate was introduced into the extraction and amplification cartridge. Using several electrochemical pumps and wax valves, the lysate was first mixed with ChargeSwitch magnetic beads for DNA extraction. Then the purified DNA was loaded into a 10-μL PCR chamber where PowerPlex ESI 16 PCR mix was pre-packaged. After amplification, amplicons along with preloaded sizing standards were injected into a separated CE chip via tubing for separation. During the overall analytical process, no manual operation was needed. Liu et al. developed a fully integrated micro-total analysis system that includes sequence-specific DNA template purification, PCR, post-PCR clean-up and capture inline injection, and CE ( Liu et al., 2011a ). While these systems demonstrated impressive performances, complicated operations need to be overcome before they can be practically applied in real forensic cases.

13.2 . Assembly of the polycarbonate sample preparation cartridge and the glass CE microchip. (a) The PC cartridge is positioned in a vertical plane, the glass CE chip in the horizontal plane. (b) Image of the adapter with wells for the electrodes. A PEEK tubing was employed to load the sample processed in the PC cartridge to the CE chip. (c) The adapter lid in position. (d) Schematic of the layout of the CE microchip.

(Source: Adapted with permission from Hopwood et al., 2010 .)


The phenotypic outcome of a mutation is determined by the genetic context in which it occurs. The causes of such variation are fascinating in themselves, but are also central to finding ways of predicting and ameliorating genetic diseases. Loss of gene function may lead to death of specific tumour cells only, making the gene a potent drug target (Behan et al, 2019 Gonçalves et al, 2020 ). Moreover, a coding mutation with no discernible impact in a parent can result in a disorder in their child (Wright et al, 2019 ). Understanding how such incomplete penetrance arises, and predicting it for a new context, would therefore deepen our understanding of cellular systems and likely impact diagnoses for developmental disorders or personalised treatments for tumours.

Viability is perhaps the simplest mutation phenotype to analyse. In the course of establishing the yeast gene knockout collection, it became clear that about 1,100 of the

6,000 yeast genes are indispensable under standard, nutrient-rich growth conditions (Giaever et al, 2002 ). However, repeating this resource construction in another genetic background offered a tantalising glimpse into the complexity of mutant phenotypes, as over 5% of the essential genes were variable between two closely related strains (Dowell et al, 2010 ). New strain panels (Galardini et al, 2019 Sanchez et al, 2019 ) both established estimates of

10% of all genes demonstrating variable knockout phenotypes between strains and species.

Systematic identification of spontaneous mutations that can suppress fitness defects of “query” mutant alleles in a reference yeast strain has illuminated mechanisms of suppression (van Leeuwen et al, 2016 , 2017 , 2020 ). These studies have shown that although deletion mutants are mainly suppressed by genes with a role in the same functional module, partial loss-of-function alleles are frequently suppressed by more general mechanisms affecting query protein expression or stability. However, surveys in model organisms have been largely limited to detecting single gene suppression in a laboratory setting, whereas more complex networks of modifiers may affect the penetrance of any given allele in natural populations. Linkage-based analyses of large panels of individuals have indeed identified second and higher-order modifier effects (Chandler et al, 2014 Taylor & Ehrenreich, 2015 Mullis et al, 2018 Hou et al, 2019 Sanchez et al, 2019 ), but few modifiers are usually characterised in depth beyond mapping the loci in such designs. The relevance of established broad suppression mechanisms for natural populations thus remains unclear (Matsui et al, 2017 ).

Here, we measure phenotypes elicited by crossing about 1,100 temperature-sensitive mutant alleles of essential genes to ten genetically diverse yeast strains. We use powerful genetic mapping approaches to identify modifier loci of a subset and validate causal genes for 19 of them. A single strong suppressor allele could independently overcome the query mutation phenotype in nearly all mapped cases. The suppressing variants tend to operate within the same biological module as the query gene, with mutations in protein interaction partners or protein complexes often suppressing specific genes, mutations in pathways suppressing other pathway members and general modifiers altering the effect of many mutations. Together, these results demonstrate the natural genetic flexibility of cells to fulfil crucial tasks and suggest that loss of human gene function could often be specifically rescued as well.

What happens at each stage of PCR?

Denaturing stage

  • During this stage the cocktail containing the template DNA and all the other core ingredients is heated to 94-95⁰C.
  • The high temperature causes the hydrogen bonds between the bases in two strands of template DNA to break and the two strands to separate.
  • This results in two single strands of DNA, which will act as templates for the production of the new strands of DNA.
  • It is important that the temperature is maintained at this stage for long enough to ensure that the DNA strands have separated completely.
  • This usually takes between 15-30 seconds.

Annealing stage

  • During this stage the reaction is cooled to 50-65⁰C. This enables the primers to attach to a specific location on the single-stranded template DNA by way of hydrogen bonding (the exact temperature depends on the melting temperature of the primers you are using).
  • Primers are single strands of DNA or RNA sequence that are around 20 to 30 bases in length.
  • The primers are designed to be complementary in sequence to short sections of DNA on each end of the sequence to be copied.
  • Primers serve as the starting point for DNA synthesis. The polymerase enzyme can only add DNA bases to a double strand of DNA. Only once the primer has bound can the polymerase enzyme attach and start making the new complementary strand of DNA from the loose DNA bases.
  • The two separated strands of DNA are complementary and run in opposite directions (from one end - the 5’ end – to the other - the 3’ end) as a result, there are two primers – a forward primer and a reverse primer.
  • This step usually takes about 10-30 seconds.

Extending stage

  • During this final step, the heat is increased to 72⁰C to enable the new DNA to be made by a special Taq DNA polymerase enzyme which adds DNA bases.
  • Taq DNA polymerase is an enzyme taken from the heat-loving bacteriaThermus aquaticus.
    • This bacteria normally lives in hot springs so can tolerate temperatures above 80⁰C.
    • The bacteria's DNA polymerase is very stable at high temperatures, which means it can withstand the temperatures needed to break the strands of DNA apart in the denaturing stage of PCR.
    • DNA polymerase from most other organisms would not be able to withstand these high temperatures, for example, human polymerase works ideally at 37˚C (body temperature).
    • These three processes of thermal cycling are repeated 20-40 times to produce lots of copies of the DNA sequence of interest.
    • The new fragments of DNA that are made during PCR also serve as templates to which the DNA polymerase enzyme can attach and start making DNA.
    • The result is a huge number of copies of the specific DNA segment produced in a relatively short period of time.

    Illustration showing how the polymerase chain reaction (PCR) produces lots of copies of DNA. Image credit: Genome Research Limited