Have you ever watched the X-Men movies? If so, you might wonder why a mere 1% genetic difference between humans can result in such significant variations in physical traits and individual uniqueness? especially given that humans actually share more than 99% of their genome.
Two siblings can have different DNA sequences despite receiving chromosomes from the same parents. This phenomenon is the result of recombination during meiosis that reshuffles parental chromosomes and changes allelic variation. In this article, we are going to discuss why identical individuals can carry different DNA sequence versions even though they may be physically similar.
Mutation creates new sequence variants
In the X-Men movies, we call gifted humans as mutants, which refers to a change in their DNA sequences compared to the overall population. We call changes in the DNA sequences as mutation, which may arise through an error during DNA replication, damage to DNA, or imperfect repair. Once a mutation has occurred, later DNA replication can copy the changed sequence and influence the creation of proteins. New protein variants can influence the behaviour of cells, and ultimately the individual traits. We know it as individual powers in the X-Men movies. Mutation is therefore the source of new sequence variants that are distributed by recombination and segregation events.
From a base substitution to a population SNP
In fact, variations in genome sequences among individuals are the result of several variant classes. So far, we know that mutation is the replacement of DNA bases in a lineage, also known as base substitution. For example, if a germline cell changes from A to G at one position, that event is a substitution mutation. It can be copied into a gamete and inherited by descendants, but it begins as one event, not as a population-wide property.
A substitution mutation depends on the chemical classes of the bases involved: a transition (between two purines or two pyrimidines) or a transversion (between a purine and a pyrimidine). However, we never exactly know whether the change is common, rare, harmful, favourable, or causal for an individual trait.
Often, a substitution mutation occurs consistently at a population level. For example, a change from A in the parents to G in their offspring occurs consistently across many individuals and is passed to the next generation. We call this variation single-nucleotide variants (SNVs), and if SNVs occur in many individuals in a population, it can refer to single-nucleotide polymorphisms (SNPs). As illustrates in the figure bellow, there are four different alleles: T, C, A, and G. In some human genetics resources, an allele frequency of at least 0.5% is used as a practical SNP convention. That threshold is not a universal biological boundary and must not be transferred uncritically to a breeding population with its own sample size, ancestry, and purpose.
Figure 1: A sequence illustration compares the two chromosome copies of six individuals at one highlighted locus. The alleles are T, C, G, and A; some individuals carry two matching copies and others carry different copies.
Where the change occurs determines whether it can contribute to inherited variation. A germline variant occurs in a cell lineage that contributes to gametes and can be transmitted to offspring. A somatic variant occurs in another cell lineage. In the usual animal-breeding setting, a somatic variant can affect the descendant cells within that individual but is not transmitted through its sperm or eggs. The boundary between reproductive and somatic lineages depends on the organism and mode of reproduction, so this distinction must be interpreted carefully in plants and clonally propagated populations.
Variant size does not determine biological importance. A one-base change can disrupt a coding or regulatory sequence, while a large change may have little measured effect in a particular environment. Conversely, most observed sequence variants should not be assumed to change a trait. The effect depends on location, molecular consequence, genetic background, and the phenotype being studied.
Reference and alternative alleles are coding labels
A reference sequence provides coordinates and a comparison sequence. At a biallelic site, the allele matching that sequence is often labelled reference, and the other observed allele is labelled alternative. Suppose the reference base at a site is A and a breeding candidate carries A/G. The label tells us which base matches the chosen reference and allows the genotype to be encoded. It does not tell us that A is ancestral, common, favourable, or biologically inactive. It also does not tell us that G is derived, rare, harmful, or causal.
Those biological and population properties require separate evidence. The alternative allele can be the common allele in a particular population, and changing the reference assembly or reversing the counted allele changes the code without changing the individual’s DNA. Later pages will define allele frequency and numerical dosage. For now, the important point is that coding conventions must not be mistaken for biological conclusions.
The usual SNP dosage is especially simple because one biallelic locus has two allele states, so a diploid genotype can count 0, 1, or 2 copies of a chosen allele. A microhaplotype is different: it combines the phased states of several nearby variants on one chromosome copy. The resulting local marker can have more than two observed haplotype alleles, but it is not a third allele at any one constituent SNP. The later page on haplotypes, phasing, and microhaplotypes explains how that multiallelic representation arises and why its usefulness depends on the population, phase quality, and local genomic context.
Sequencing and genotyping measure variation differently
DNA sequencing determines the order of bases across the DNA regions that an assay covers. Those reads can reveal variants relative to a reference sequence, subject to coverage, alignment, and variant-calling uncertainty. Sequencing may target one region, a set of genes, or much of a genome; the word does not by itself guarantee that every base was measured accurately.
Genotyping determines which allele or genotype an individual carries at specified loci. A marker array, for example, can measure a predefined set of SNPs efficiently across many candidates, but it does not directly inspect every unassayed position between them. Genotypes can also be called from sequence data, so sequencing and genotyping are not mutually exclusive labels for instruments. The useful distinction is between reading sequence across assayed regions and assigning genotypes at defined variant sites.
These measurements require quality control before modelling. File layouts, missing-value codes, dosage matrices, and software-ready SNP inputs belong in the SNP input guide, rather than on this biological page.
Observed markers and causal truth in the project data
The recurring project dataset is simulated. Its SNP map records 500 observed markers with identifiers, chromosomes, and base-pair positions. The simulation also records which of those markers generated the genetic signal. This second piece of information is available because the data-generating process is known, not because an ordinary genotyping assay reveals causality.
The output separates 20 simulated causal SNPs from 480 other observed SNPs. The latter group should not be called biologically irrelevant: a non-causal marker may still predict a causal chromosome segment, and the usefulness of that association can depend on the population. Conversely, inclusion among the 20 causal indices is simulation truth for this teaching dataset, not a claim that real breeding data contain exactly 20 causal loci.
The map contains marker identifiers and positions, but it does not contain the nucleotide identities used as reference and alternative alleles. We can therefore discuss marker locations and the simulated causal set, but we cannot infer an A/G coding or label a favourable allele from this object. Doing so would add information that the dataset does not provide.
Exercises
A sequence comparison changes ACCT to ACGT at one position. Classify the change as a substitution and an SNV. Under what additional population-level condition could the site be called an SNP? Does any of those labels show that the changed base causes a trait difference?
NoteSolution
The comparison contains a C-to-G substitution, which is a transversion, and it is an SNV because one base differs. Calling it an SNP requires evidence that more than one allele occurs at that site in the relevant population under the stated convention. None of these labels provides evidence by itself that the site affects a trait.
At one locus, C matches the reference sequence and T is labelled the alternative allele. List three conclusions that cannot be obtained from those labels alone.
NoteSolution
The labels alone do not identify which allele is ancestral, which is more common in the target population, or which has a favourable trait effect. They also do not establish whether either allele is causal. Each conclusion needs population, evolutionary, functional, or trait evidence beyond the reference comparison.
The project map contains 500 observed SNP markers, of which 20 are marked causal by the simulation. How many observed markers are not causal in the generating model, and why might some of them still contribute to genomic prediction?
NoteSolution
There are \(500-20=480\) observed markers outside the simulated causal set. Some may carry information about nearby causal variants when their alleles are inherited together in the population. The next pages develop the population frequencies and multi-locus associations needed to explain when that marker information persists.
A DNA change is found in a skin cell but not in the individual’s gametes. Is it usually inherited by offspring? Contrast mutation with recombination as a source of genetic variation.
NoteSolution
This is a somatic variant, so in the usual animal-breeding setting it is not transmitted through sperm or eggs. Mutation changes a DNA sequence and is the source of new variants. Recombination rearranges variants already present on parental chromosomes into new combinations.
A marker array reports genotypes at predefined SNPs for many candidates. What information can it provide, and what does it not directly determine about unassayed sequence positions?
NoteSolution
The array can determine which allele or genotype each candidate carries at its specified marker loci. It does not directly read every sequence position between those markers. Sequencing reads bases across the regions that an assay covers, although both measurements still require quality control.
Identifying a variant in one candidate does not yet tell us whether it is rare, common, or fixed in the breeding population. Those descriptions require counts across individuals. Genetic variation in populations is therefore the next step from molecular differences to the population-level patterns used in genomic prediction.
