7.12: Genes and alleles - Biology

7.12: Genes and alleles - Biology

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Up to now we have been considering genes as abstract entities and mentioning, only in passing, what they actually are. While we will not consider in any significant detail, it is worth noting that genes can be complex: there can be multiple regulatory regions controlling the same coding sequence and particularly in eukaryotes a single gene can produce multiple, functionally distinct gene products through the process of RNA splicing220. Change the organism and the same, or rather, more accurately put, homologous genes (that is gene that share a common ancestor, a point we will return to) can have different roles.

Once we understand that a gene corresponds to a specific sequence of DNA, we understand that different alleles of a gene correspond to genes with different sequences. Two alleles of the same gene can differ from one another at as little as a single nucleotide position or at many positions. The most common version of an allele is often referred to as the wild type allele, but that is really just because it is the most common. There can be multiple “normal” alleles of a particular gene within any one population. Genes can overlap with one another, particularly in terms of their regulatory regions, and defining all of the regulatory regions of a gene can be difficult, particularly since different regulatory regions may be used in the different cells types present within a multicellular organism. A gene's regulatory regions can span many thousands of kilobases of DNA and be located upstream, downstream, or within the gene’s coding region. In addition, because DNA is double stranded, one gene can be located on one strand and another, completely different gene can be located on the anti-parallel strand. We will return to the basic mechanisms of gene regulation later on, but as you probably have discerned, gene regulation is complex and typically the subject of its own course.

Alleles: Different alleles of the same gene can produce quite similar gene products or their products can be different. The functional characterization of an allele is typically carried out with respect to how its presence influences a specific trait(s). Again, remember that most traits are influenced by multiple genes, and a single gene can influence multiple traits and processes. An allele can produce a gene product with completely normal function or absolutely no remaining functional activity, referred to as a null or amorphic allele. It can have less function than the "wild type" allele (hypomorphic), more function than the wild type (hypermorphic), or a new function (neomorphic). Given that many gene products function as part of multimeric complexes that are the products of multiple genes and that many organisms (like us) are diploid, there is one more possibility, the product of one allele can antagonize the activity of the other - this is known as an antimorphic allele. These different types of alleles were defined genetically by Herbert Muller, who won the Nobel prize for showing that X-rays could induce mutations, that is, new alleles.


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Allele, also called allelomorph, any one of two or more genes that may occur alternatively at a given site (locus) on a chromosome. Alleles may occur in pairs, or there may be multiple alleles affecting the expression ( phenotype) of a particular trait. The combination of alleles that an organism carries constitutes its genotype. If the paired alleles are the same, the organism’s genotype is said to be homozygous for that trait if they are different, the organism’s genotype is heterozygous. A dominant allele will override the traits of a recessive allele in a heterozygous pairing. In some traits, however, alleles may be codominant—i.e., neither acts as dominant or recessive. An example is the human ABO blood group system persons with type AB blood have one allele for A and one for B. (Persons with neither are type O.)

Most traits are determined by more than two alleles. Multiple forms of the allele may exist, though only two will attach to the designated gene site during meiosis. Also, some traits are controlled by two or more gene sites. Both possibilities multiply the number of alleles involved. All genetic traits are the result of the interactions of alleles. Mutation, crossing over, and environmental conditions selectively change the frequency of phenotypes (and thus their alleles) within a population. For example, alleles that are carried by individuals with high fitness (meaning they successfully reproduce and pass their genes to their offspring) have a higher likelihood of persisting in a population than alleles carried by less-fit individuals, which are gradually lost from the population over time.

The Editors of Encyclopaedia Britannica This article was most recently revised and updated by Kara Rogers, Senior Editor.


Genes govern the traits of an organism. They do so by acting as instructions to make proteins. Proteins are the diverse molecules that play many critical roles in our bodies, such as producing hormones and creating antibodies.

Humans have two copies (or alleles) of each gene, one inherited from each parent. Alleles play a significant role in shaping each human’s individual features. Alleles are versions of the same gene with slight variations in their sequence of DNA bases. These small differences among alleles of the same gene contribute to each person’s unique characteristics.

Results and discussion

Medaka mutant library generation and screening

The mutant medaka library was generated and screened as schematically outlined in Figure 1. Founder fish were repeatedly mutagenized with ENU, crossed with wild-type females, and the progeny were used to establish a permanent cryopreserved resource of 5,771 F1 males (Table 1). To get an indication about the induced mutation frequency, we performed a specific locus test using the albino mutant [4]. The appearance of a white-eyed embryo at a rate of 1 in 272 (Table 1) is in line with previously observed frequencies [4], suggesting that the mutagenesis was very effective.

Schematic outline of the mutant medaka library generation and screening. Male G0 fish were ENU-mutagenized and crossed with wild-type (WT) females. Male F1 progeny were used for sperm cryopreservation and parallel DNA isolation. The library was screened for induced mutations in target genes of interest by dideoxy resequencing. Interesting mutants were retrieved from the cryopreserved archive by in vitro fertilization and incrossed to homozygosity for phenotypic analysis.

The mutant library was screened for genes involved in tumor biology (p53, and Blm, encoding Bloom helicase), neurodegeneration (Parkin, encoding ubiquitin ligase), aging (Sirt1, encoding deacetylase), and miRNA metabolism (Dcr-1, encoding Dicer). Although a variety of mutation discovery technologies have been established for targeted retrieval of induced mutations [11–14], we chose to use dideoxy resequencing of PCR-amplified target sequences for routine mutation discovery [15], as this technology is robust and can be automated very well at both the experimental and data interpretation levels [16]. Most importantly, it provides highly informative data about the exact location and nature of the mutation.

We screened the complete library for 10 different amplicons covering 20 exons in 5 different genes (Table 2). In total, about 22 Mbp were screened and 64 independent mutations were identified (Table 3). The average ENU-induced mutation frequency for the library was found to be 1 mutation per 345,000 bp, similar to what was found for reverse genetic screens in zebrafish [12]. We retrieved highly likely loss-of-function mutations for four out of five genes screened by the identification of four nonsense and two splice site mutations. Although a full loss-of-function has to be demonstrated for each mutant individually, we refer to these mutants as knockouts in this paper. Furthermore, 38 missense mutations were found in the different genes (Tables 2 and 3), some of which could potentially result in a partial or complete loss-of-function or gain-of-function phenotype.

All nonsense and splice site mutants were recovered from the frozen sperm archive by in vitro fertilization (Table 4). A very high fertilization rate of more than 90% was consistently obtained following standard in vitro fertilization procedures, with only 7% to 33% of the fertilized eggs failing to develop and hatch. Genotyping tail fin tissue from a portion of F2 offspring revealed that the ratio of wild-type fish to mutant heterozygotes was about one-to-one, as expected (data not shown).

P53 E241X mutant characterization

We identified seven induced mutations in the medaka p53 gene [17], including three missense mutations, one splice site, and two nonsense mutations (Figure 2). The p53 E241X allele is a G to T substitution that results in the alteration of Glu241 to a stop codon, whereas the p53 Y186X allele is a T to A substitution that alters Tyr186 to a stop codon. Both were presumed to result in a truncated protein that terminates prematurely in the midst of a DNA-binding domain. These proteins retain the amino-terminal transactivation domain but lack the nuclear localization signal and tetramerization domain required for full activity. Furthermore, no alternative splicing variants involving these mutation-containing exons are known in any species, indicating that these nonsense mutations are most likely to result in a null phenotype. All three missense mutations are at highly conserved residues within the DNA-binding region, but more detailed characterization will be needed to conclude anything about their effect on protein function.

Target-selected mutagenesis of Oryzias latipes p53 gene. Genomic organization and protein structure of the medaka p53 gene. The region analyzed by PCR and dideoxy resequencing is indicated by bidirectional arrows. The ENU mutations are shown by solid arrows. Basic, basic regulatory region DBD, DNA-binding domain NLS, nuclear localization signal Pro-rich, proline-rich domain TAD, transactivation domain TET, tetramerization domain.

Impaired target gene induction upon DNA damage is one of the phenotypes that is expected in a p53 knockout animal [18]. p53 E241X/E241X embryos were γ-irradiated and the induction of p21, Mdm2 and Bax genes was examined by RT-PCR. As expected, no increase of these target genes was observed in p53 E241X/E241X homozygous fish, while control fish clearly showed upregulation of p21 and Mdm2 transcription level in response to ionizing radiation (IR), (Figure 3a). Interestingly, the basal level of the p53 transcript was decreased in p53 E241X/E241X fish. This could be due to nonsense-mediated decay [19] of mutant RNA, a phenomenon that is frequently observed in ENU-induced nonsense mutants (E Cuppen, unpublished observations), although an autoregulatory mechanism cannot be excluded. The same results were obtained for the second nonsense allele (p53 Y186X/Y186X data not shown). Next, we investigated whether IR-induced apoptosis was affected in p53 E241X/E241X mutants. Primary cell cultures were derived from wild-type and p53 E241X/E241X fish, γ-irradiated, and observed by time-lapse video microscopy for apoptosis. While 13.2% (15 out of 142 cells counted) of p53 +/+ cells underwent apoptosis, none of the p53 E241X/E241X cells (0 out of 121 cells) showed fragmentation of the nucleus (Figure 3b). These results are consistent with a complete loss-of-function phenotype of p53 in these medaka mutants.

Radiation-induced p53 target gene induction and apoptosis. (a) Impaired IR-induced transactivation of target genes. Using semi-quantitative RT-PCR, induction of Mdm2 and p21 upon γ-irradiation can readily be observed in wild-type and heterozygous embryos, but is absent in animals homozygous for the p53 mutant allele. (b) Suppression of apoptosis in primary cultured cells. Primary cells derived from p53 E241X/E241X and p53 +/+ embryos were irradiated with 10 Gy of ionizing radiation and observed by time-lapse microscopy. The apoptotic cells from homozygous embryos with fragmented nuclei are indicated with arrows.

To monitor for spontaneous tumorigenesis, p53 knockout (p53 E241X/E241X , n = 21), heterozygote (p53 +/E241X , n = 26), and wild-type (p53 +/+ , n = 10) littermates were raised to adulthood to monitor for spontaneous tumorigenesis. Only a single p53 +/+ fish died within 10 months after birth with no obvious signs of cancer (Figure 4). Heterozygous fish developed some tumors during the course of observation (two out of the five fish that died during the first ten months had clear tumors), but the mortality rate was relatively low. In contrast, a dramatic tumor predisposition was observed in the homozygotes, with the first incidence of tumorigenesis observed already at 2.5 months of age. The frequency of tumor formation increased after 5 months of age, resulting in a median lifespan of 228 days. All homozygous fish died within 10 months and 11 out of the 21 animals had clear tumors. The real tumor rate is most likely higher, as a significant part of the dead fish could unfortunately not be examined properly, due to rapid decomposition. It should be mentioned that at least 2 out of the 21 p53 E241X/E241X fish died without any macroscopic signs of tumors. The p53 Y186X/Y186X fish developed tumors as well but at a lower rate compared to the p53 E241X/E241X mutant. The median lifespan was also slightly increased (311 days), but was still much shorter than for wild-type fish (Figure 4). The difference in tumorigenesis between the two different nonsense alleles is not clear at this moment. We cannot exclude the possibility that co-segregating ENU mutations affect the predisposition to develop tumors in the p53 E241X background. The analysis of heteroallelic p53 E241X/Y186X fish and/or analysis of further outcrossed lines should resolve this issue.

Survival curve of p53 mutant medaka. The viability of wild-type (dotted lines), heterozygote (dashed lines), and homozygote (solid lines) littermates of the p53 E241X (black) and p53 Y186X/Y186X (grey) fish was monitored for 10 months.

Stereoscopic as well as histological characterization of tumor-bearing p53 E241X mutant fish revealed a wide variety of tumor types in kidney, eye, brain, intestine, gill, thymus and testis (Figures 5 and 6). In one case, where kidney is the primary origin, lymphoid cells spread throughout the interstitial space, destroying the normal architecture of renal tubules and glomeruli (Figure 5). This is consistent with the observation that the teleost kidney is developmentally a mesonephros, which is the site for hematopoiesis in adult fish and is thought to function analogously to the bone marrow in mammals [20]. Considering a very low natural occurrence of tumors in young medaka (<0.01%) and the propensity of medaka to liver tumors [21], the diversity in tumor types and the high incidence of tumors observed in p53-deficient fish implicate that the p53 knockout medaka are highly susceptible to spontaneous tumorigenesis compared to their p53-proficient littermates, even though the number of fish examined in this study was relatively small.

Typical kidney tumor as found in p53 E241X/E241X homozygous fish. (a) A stereoscopic view of the kidney tumor identified in a 2.5 month old homozygous p53 E241X/E241X fish. (b-d) Hematoxylin-eosin staining of normal (b) and neoplastic (c) kidney of medaka. Note that the interstitial tissue is infiltrated with numerous hematopoietic cells destroying the normal architecture of renal tubules. The higher magnification shows the mixture of small lymphocytes with little cytoplasm and the plasmacyte-like cells with large basophilic cytoplasm (d).

Various tumors that developed spontaneously in p53 medaka knockouts. (a,b) The tumor that arose in the left gill of a p53 E241X/+ fish with the lymphomatous infiltrate, consistent with the diagnosis of thymic lymphoma. (c,d) Adenocarcinoma found in the right gill of a p53 E241X/E241X homozygous fish. (e,f) Retinoblastoma in the right eye of a p53 E241X/E241X homozygous fish. Note the rosette-like structures throughout the tumor. (g,h) A germ cell tumor found in the anterior upper part of the peritoneal cavity of a p53 E241X/E241X homozygous fish. All fish presented here died or were sacrificed at around 8 months of age. Arrowheads indicate tumors. Hematoxylin-eosin staining, original magnification: (b,d) 100× (f,h) 10×.

In p53-deficient zebrafish, peripheral nerve sheath tumors were found to predominate [22]. The difference in tumor spectrum may be caused by the type of mutation introduced in the genome, namely a missense mutation at a conserved residue in zebrafish versus a nonsense mutation in medaka, or by the presence of organism-specific secondary genes that are differentially involved in tumor susceptibility. This tissue specific tumor development in different species is of great interest as this phenomenon is also found in mammals: in Li-Fraumeni syndrome patients, caused by mutations in the human p53 gene, breast cancer and sarcomas are most common, whereas p53 knockout mice develop T cell lymphomas [23, 24]. Such differences strengthen the need for parallel studies in multiple model organisms.

We identified a nonsense mutation that results in a truncated Parkin protein at Tyr314, eliminating the inbetween RING domain (IBR) and the second RING domain (RING2), which are critical for its ubiquitin ligase activity [25]. Interestingly, a similar mutation, which results in Parkin protein truncation at Glu311, has been found in a human juvenile parkinsonism patient [26]. For the Blm gene, the premature stop codon was introduced at position Glu497, which removes the entire critical helicase domain. Again, a similar 515 amino acid-long truncated protein has been reported in a human disease case that results from a 1 bp insertion prior to the helicase domain [27]. It should be noted that the complete knockout of the Blm gene results in embryonic lethality in mice [28], while Blm mutant medaka fish are viable, similar to human. We expect that the medaka mutants of the Parkinsonism and Bloom syndrome genes may serve as valuable disease models, and are currently characterizing their phenotypes in detail.


The aim of this project was to identify novel ciliary/ciliopathy genes by using a comparative genomics approach that exploits emerging sequence and sequence annotation data of related animal species. Here, we have identified an extensive list (total 93) of candidate X-box regulated genes, of which approximately one-third are known X-box-regulated/ciliary genes. Many, or even the majority, of these candidate ciliary genes when mutated may cause a dye filling defect. Since the majority (83 out of 93) of the candidate X-box-regulated genes in C. elegans have readily identifiable human orthologs (Additional data file 2), it would be productive to screen patients with known ciliopathies, such as BBS, for mutations affecting some of these genes. In addition, based on the correlation between the Dyf phenotype and ciliary gene function, the regulation of such genes by the X-box-binding DAF-19 transcription factor, and the conservation of such motifs across sister Caenorhabditis genomes, we have successfully cloned dyf-5 and identified at least one other dyf gene, namely ZK520.3 for dyf-2, which has been characterized elsewhere [24]. The cloning of these dyf genes has demonstrated the effectiveness of the combined comparative genomics and genetics analysis approach presented here. The newly cloned dyf-5 gene may be a C. elegans ortholog of a yet unidentified human BBS or other ciliopathy-associated gene since all studied C. elegans orthologs of known human BBS genes result in a Dyf phenotype when disrupted [18, 20, 40].

Because transcriptional regulatory motifs are generally short (less than 20 bp) and degenerate, many thousands of potential binding sites for any given transcription factor are expected to be found by chance [41] and this poses a great challenge in identifying bona fide binding sites, especially in large eukaryote genomes. Our approach overcomes such a challenge by using comparative genomics and the recent availability of multiple sister Caenorhabditis genomes. In the context of identifying transcription factor binding sites and target genes, such an approach is arguably advantageous compared to approaches that rely on co-expression, which can be coincidental or even secondary to a common transcriptional regulatory pathway and thus lead to a high rate of false positives. Indeed, many of the 466 daf-19 regulated genes identified in this study by microarray expression profiling do not contain the X-box motif in their promoters and are not necessarily directly regulated by DAF-19. Furthermore, comparative genomics is advantageous because it does not encounter problems of data noise and biased sampling associated with functional genomics projects. On the other hand, the comparative genomics based strategy reveals only highly conserved motifs while others are regarded as false positives and discarded accordingly. One caveat of this rather conservative filtering procedure is that species-specific binding motifs, or more divergent motifs, are mistakenly discarded, leading to a non-negligible false negative rate. Therefore, the candidate X-box regulated genes identified in this project may only represent a portion of the entire set of bona fide X-box regulated genes in C. elegans. In fact, there are still seven dyf genes (dyf-4, dyf-7, dyf-8, dyf-9, dyf-10, dyf-11 and dyf-12) in C. elegans that remain to be identified. However, we should be aware that not all of the uncloned dyf genes are DAF-19 and X-box dependent (for example, genes such as daf-6 [42] that are expressed in the sheath cell or socket cell when mutated can also lead to the Dyf phenotype). To clone these bona fide X-box-regulated dyf genes and identify additional X-box regulated genes, some of which might be uncloned osm or che genes, we will need to have a more detailed understanding of the properties of X-box motifs, including the variation, preferred position in the promoter, and interaction with other binding motifs. Some of these questions will be at least partially addressed after we have validated more of our candidate X-box-containing genes in C. elegans. This study and previous studies [6, 10, 16, 17] have found that the majority of known X-boxes are located within 250 bp upstream of the translational start site (ATG). However, many genuine X-boxes reside far outside of this optimal region, further suggesting that other factors or properties of X-boxes that are critical for their functions remain to be identified.

Additionally, improvement in gene curation and the emergence of more related sequenced genomes, including Caenorhabditis japonica and CB5161, will undoubtedly serve to reduce false negative hits and reveal more targets. Lastly, functional genomics approaches, including ChIP-Chip [43], SACO [44], or ChIP-PET [45, 46] technologies, will help to identify more novel candidate genes, in particular species-specific ones.

Examples of Allele

Flower Color in Peas

The interactions between these alleles produces important variability in the flowers. While the recessive allele can be masked by the dominant allele, it does not mean that the dominant allele is better for the plant. It could be true that white flowers attract more pollinators, and are therefore more successful at reproducing. If this were true, the allele frequency of the non-functioning allele would increase in the population, even though it is not functioning. Sometimes the most adaptable function of an enzyme is to not have the enzyme functioning at all.

Multiple Genes in Peas

One of the things that most interested Mendel was the enormous variety he could obtain by crossing two seemingly identical plants. Below is a table of the various traits Mendel observed. He observed that while each of these traits only had two forms, the different alleles could be mix-matched in an enormous variety of patterns and shapes. What Mendel was beginning to describe were the laws of segregation and independent assortment.

The laws of segregation and independent assortment deal with the way cells divide their DNA to prepare haploid cells as sperm and egg. Although both alleles for a given trait start in the same diploid cell, they will be separated into separate eggs or sperm by the end of meiosis. This, the law of segregation, means that while a recessive allele can be masked in the expression of an organism, it has the same chance of being passed on to the offspring as a dominant allele. Also important is the law of independent assortment, which says that the alleles from the same gene will be sorted independently of alleles from other genes. This is important because it gives rise to the enormous complexity of life. From the same pea plant parents, thanks to these laws, you could receive offspring with any combination of traits listed in the above table, even if the parents looked the same.


Dihybrid Cross: When two pairs of characters are studied in the cross, it is called dihybrid cross.

Mendel selected yellow colour (YY) and green colour (yy) as seed colour. He further selected round seeds (RR) and wrinkled seeds (rr) for seed texture. In this case, yellow colour is dominant over green colour, while round texture is dominant over wrinkled texture.

F1 Generation: When gametes RY and ry were crossed, all plants in F1 generation produced yellow and wrinkled seeds (RrYy). The genotype was heterozygous in these plants. Yellow colour and round texture showed dominance.

When plants of F1 generation were allowed to self pollinate, the result could be shown by following Punette Square.


The plants of F2 generation produced 3 types of seeds, i.e. round yellow, wrinkled yellow, round green and wrinkled green in ratio 9:3:3:1. Based on this observation, Mendel proposed the Law of Independent Assortment.

Law of Independent Assortment: When two pairs of traits are combined in a hybrid, segregation of one pair of characters is independent of the other pair of characters.

Chromosomal Theory of Inheritance: Chromosomes as well as genes occur in pairs. The two alleles of a gene pair are located on homologous sites on homologous chromosomes. Sutton and Boveri argued that the pairing and separation of a pair of chromosomes would lead to the segregation of a pair of factors they carried. Sutton united the knowledge of chromosomal segregation and Mendelian principles and termed it the Chromosomal Theory of Inheritance.

Linkage: The physical association of genes on a chromosome is called linkage.

Recombination: Combination of non-parental genes is called recombination.

Morgan carried out several dihybrid crosses in Drosophila to study genes that were sex-linked. Morgan hybridized yellow-bodied, white-eyed females to brown-bodied, red-eyed males. He intercrossed the F1 progeny. He observed that the two genes did not segregate independently of each other, and the F2 ratio deviated very significantly from the 9:3:3:1 ratio. Morgan was aware that the genes were located on the X chromosome. He could see that when the two genes in a dihybrid cross were situated on the same chromosome, the proportion of parental gene combinations were much higher than the non-parental gene combinations. This was attributed to the physical association or linkage of the two genes. Morgan also found that even when genes were grouped on the same chromosome, some genes were tightly linked, while others were loosely linked. The tightly linked genes showed very low recombination, while the loosely linked genes showed higher recombination. For example genes for white and yellow colours were tightly linked and showed only 1.3% recombination. On the other hand, genes for white and miniature wing showed 37.2% recombination because they were loosely linked.

What is an allele?

When genes mutate, they can take on multiple forms, with each form differing slightly in the sequence of their base DNA. These gene variants still code for the same trait (i.e. hair color), but they differ in how the trait is expressed (i.e. brown vs blonde hair). Different versions of the same gene are called alleles.

Genes can have two or more possible alleles. Individual humans have two alleles, or versions, of every gene. Because humans have two gene variants for each gene, we are known as diploid organisms.

The greater the number of potential alleles, the more diversity in a given heritable trait. An incredible number of genes and gene forms underly human genetic diversity, and they are the reason why no two people are exactly alike.

As an example, let’s look at eye color. In a simplified model, we will assume that there is only one gene that encodes for eye color (although there are multiple genes involved in most physical traits). Blue, green, brown, and hazel eyes are each encoded by unique alleles of said gene. The pair of alleles present on an individual’s chromosomes dictates what eye color will be expressed.


Single-nucleotide polymorphisms may fall within coding sequences of genes, non-coding regions of genes, or in the intergenic regions (regions between genes). SNPs within a coding sequence do not necessarily change the amino acid sequence of the protein that is produced, due to degeneracy of the genetic code.

SNPs in the coding region are of two types: synonymous and nonsynonymous SNPs. Synonymous SNPs do not affect the protein sequence, while nonsynonymous SNPs change the amino acid sequence of protein.

  • SNPs in non-coding regions can manifest in a higher risk of cancer, [11] and may affect mRNA structure and disease susceptibility. [12] Non-coding SNPs can also alter the level of expression of a gene, as an eQTL (expression quantitative trait locus).
  • SNPs in coding regions:
      by definition do not result in a change of amino acid in the protein, but still can affect its function in other ways. An example would be a seemingly silent mutation in the multidrug resistance gene 1 (MDR1), which codes for a cellular membrane pump that expels drugs from the cell, can slow down translation and allow the peptide chain to fold into an unusual conformation, causing the mutant pump to be less functional (in MDR1 protein e.g. C1236T polymorphism changes a GGC codon to GGT at amino acid position 412 of the polypeptide (both encode glycine) and the C3435T polymorphism changes ATC to ATT at position 1145 (both encode isoleucine)). [13] :
        – single change in the base results in change in amino acid of protein and its malfunction which leads to disease (e.g. c.1580G>T SNP in LMNA gene – position 1580 (nt) in the DNA sequence (CGT codon) causing the guanine to be replaced with the thymine, yielding CTT codon in the DNA sequence, results at the protein level in the replacement of the arginine by the leucine in the position 527, [14] at the phenotype level this manifests in overlapping mandibuloacral dysplasia and progeria syndrome) – point mutation in a sequence of DNA that results in a premature stop codon, or a nonsense codon in the transcribedmRNA, and in a truncated, incomplete, and usually nonfunctional protein product (e.g. Cystic fibrosis caused by the G542X mutation in the cystic fibrosis transmembrane conductance regulator gene). [15]
  • SNPs that are not in protein-coding regions may still affect gene splicing, transcription factor binding, messenger RNA degradation, or the sequence of noncoding RNA. Gene expression affected by this type of SNP is referred to as an eSNP (expression SNP) and may be upstream or downstream from the gene.

    More than 335 million SNPs have been found across humans from multiple populations. A typical genome differs from the reference human genome at 4 to 5 million sites, most of which (more than 99.9%) consist of SNPs and short indels. [16]

    Within a genome Edit

    The genomic distribution of SNPs is not homogenous SNPs occur in non-coding regions more frequently than in coding regions or, in general, where natural selection is acting and "fixing" the allele (eliminating other variants) of the SNP that constitutes the most favorable genetic adaptation. [17] Other factors, like genetic recombination and mutation rate, can also determine SNP density. [18]

    SNP density can be predicted by the presence of microsatellites: AT microsatellites in particular are potent predictors of SNP density, with long (AT)(n) repeat tracts tending to be found in regions of significantly reduced SNP density and low GC content. [19]

    Within a population Edit

    There are variations between human populations, so a SNP allele that is common in one geographical or ethnic group may be much rarer in another. However, this pattern of variation is relatively rare in a global sample of 67.3 million SNPs, the Human Genome Diversity Project

    found no such private variants that are fixed in a given continent or major region. The highest frequencies are reached by a few tens of variants present at >70% (and a few thousands at >50%) in Africa, the Americas, and Oceania. By contrast, the highest frequency variants private to Europe, East Asia, the Middle East, or Central and South Asia reach just 10 to 30%. [20]

    Within a population, SNPs can be assigned a minor allele frequency—the lowest allele frequency at a locus that is observed in a particular population. [21] This is simply the lesser of the two allele frequencies for single-nucleotide polymorphisms.

    With this knowledge scientists have developed new methods in analyzing population structures in less studied species. [22] [23] [24] By using pooling techniques the cost of the analysis is significantly lowered. [ citation needed ] These techniques are based on sequencing a population in a pooled sample instead of sequencing every individual within the population by itself. With new bioinformatics tools there is a possibility of investigating population structure, gene flow and gene migration by observing the allele frequencies within the entire population. With these protocols there is a possibility in combining the advantages of SNPs with micro satellite markers. [25] [26] However, there are information lost in the process such as linkage disequilibrium and zygosity information.

      can determine whether a genetic variant is associated with a disease or trait. [27]
    • A tag SNP is a representative single-nucleotide polymorphism in a region of the genome with high linkage disequilibrium (the non-random association of alleles at two or more loci). Tag SNPs are useful in whole-genome SNP association studies, in which hundreds of thousands of SNPs across the entire genome are genotyped. mapping: sets of alleles or DNA sequences can be clustered so that a single SNP can identify many linked SNPs. (LD), a term used in population genetics, indicates non-random association of alleles at two or more loci, not necessarily on the same chromosome. It refers to the phenomenon that SNP allele or DNA sequence that are close together in the genome tend to be inherited together. LD can be affected by two parameters (among other factors, such as population stratification): 1) The distance between the SNPs [the larger the distance, the lower the LD]. 2) Recombination rate [the lower the recombination rate, the higher the LD]. [28]

    Importance Edit

    Variations in the DNA sequences of humans can affect how humans develop diseases and respond to pathogens, chemicals, drugs, vaccines, and other agents. SNPs are also critical for personalized medicine. [29] Examples include biomedical research, forensics, pharmacogenetics, and disease causation, as outlined below.

    Clinical research Edit

    SNPs' greatest importance in clinical research is for comparing regions of the genome between cohorts (such as with matched cohorts with and without a disease) in genome-wide association studies. SNPs have been used in genome-wide association studies as high-resolution markers in gene mapping related to diseases or normal traits. [30] SNPs without an observable impact on the phenotype (so called silent mutations) are still useful as genetic markers in genome-wide association studies, because of their quantity and the stable inheritance over generations. [31]

    Forensics Edit

    SNPs have historically been used to match a forensic DNA sample to a suspect but has been made obsolete due to advancing STR-based DNA fingerprinting techniques. However, the development of next-generation-sequencing (NGS) technology may allow for more opportunities for the use of SNPs in phenotypic clues such as ethnicity, hair color, and eye color with a good probability of a match. This can additionally be applied to increase the accuracy of facial reconstructions by providing information that may otherwise be unknown, and this information can be used to help identify suspects even without a STR DNA profile match.

    Some cons to using SNPs versus STRs is that SNPs yield less information than STRs, and therefore more SNPs are needed for analysis before a profile of a suspect is able to be created. Additionally, SNPs heavily rely on the presence of a database for comparative analysis of samples. However, in instances with degraded or small volume samples, SNP techniques are an excellent alternative to STR methods. SNPs (as opposed to STRs) have an abundance of potential markers, can be fully automated, and a possible reduction of required fragment length to less than 100bp.[26]

    Pharmacogenetics Edit

    Some SNPs are associated with the metabolism of different drugs. [32] [33] SNP's can be mutations, such as deletions, which can inhibit or promote enzymatic activity such change in enzymatic activity can lead to decreased rates of drug metabolism [34] The association of a wide range of human diseases like cancer, infectious diseases (AIDS, leprosy, hepatitis, etc.) autoimmune, neuropsychiatric and many other diseases with different SNPs can be made as relevant pharmacogenomic targets for drug therapy. [35]

    Disease Edit

    A single SNP may cause a Mendelian disease, though for complex diseases, SNPs do not usually function individually, rather, they work in coordination with other SNPs to manifest a disease such as in Osteoporosis.[33] One of the earliest successes in this field was finding a single base mutation in the non-coding region of the APOC3 (apolipoprotein C3 gene) that associated with higher risks of hypertriglyceridemia and atherosclerosis.[34]. Some diseases caused by SNPs include rheumatoid arthritis, crohn’s disease, breast cancer, alzheimer's, and some autoimmune disorders. Large scale association studies have been performed to attempt to discover additional disease causing SNPs within a population , but a large number of them are still unknown.

      and rs6313 are SNPs in the Serotonin 5-HT2A receptor gene on human chromosome 13. [36]
    • A SNP in the F5 gene causes Factor V Leiden thrombophilia.[37] is an example of a triallelic SNP in the CRP gene on human chromosome 1. [38] codes for PTC tasting ability, and contains 6 annotated SNPs. [39]
    • rs148649884 and rs138055828 in the FCN1 gene encoding M-ficolin crippled the ligand-binding capability of the recombinant M-ficolin. [40]
    • An intronic SNP in DNA mismatch repair gene PMS2 (rs1059060, Ser775Asn) is associated with increased spermDNA damage and risk of male infertility. [41]

    As there are for genes, bioinformatics databases exist for SNPs.

    • dbSNP is a SNP database from the National Center for Biotechnology Information (NCBI). As of June 8, 2015 [update] , dbSNP listed 149,735,377 SNPs in humans. [42][43]
    • Kaviar[44] is a compendium of SNPs from multiple data sources including dbSNP.
    • SNPedia is a wiki-style database supporting personal genome annotation, interpretation and analysis.
    • The OMIM database describes the association between polymorphisms and diseases (e.g., gives diseases in text form)
    • dbSAP – single amino-acid polymorphism database for protein variation detection [45]
    • The Human Gene Mutation Database provides gene mutations causing or associated with human inherited diseases and functional SNPs
    • The International HapMap Project, where researchers are identifying Tag SNPs to be able to determine the collection of haplotypes present in each subject. allows users to visually interrogate the actual summary-level association data in one or more genome-wide association studies.

    The International SNP Map working group mapped the sequence flanking each SNP by alignment to the genomic sequence of large-insert clones in Genebank. These alignments were converted to chromosomal coordinates that is shown in Table 1. [46] This list has greatly increased since, with, for instance, the Kaviar database now listing 162 million single nucleotide variants (SNVs).

    Chromosome Length(bp) All SNPs TSC SNPs
    Total SNPs kb per SNP Total SNPs kb per SNP
    1 214,066,000 129,931 1.65 75,166 2.85
    2 222,889,000 103,664 2.15 76,985 2.90
    3 186,938,000 93,140 2.01 63,669 2.94
    4 169,035,000 84,426 2.00 65,719 2.57
    5 170,954,000 117,882 1.45 63,545 2.69
    6 165,022,000 96,317 1.71 53,797 3.07
    7 149,414,000 71,752 2.08 42,327 3.53
    8 125,148,000 57,834 2.16 42,653 2.93
    9 107,440,000 62,013 1.73 43,020 2.50
    10 127,894,000 61,298 2.09 42,466 3.01
    11 129,193,000 84,663 1.53 47,621 2.71
    12 125,198,000 59,245 2.11 38,136 3.28
    13 93,711,000 53,093 1.77 35,745 2.62
    14 89,344,000 44,112 2.03 29,746 3.00
    15 73,467,000 37,814 1.94 26,524 2.77
    16 74,037,000 38,735 1.91 23,328 3.17
    17 73,367,000 34,621 2.12 19,396 3.78
    18 73,078,000 45,135 1.62 27,028 2.70
    19 56,044,000 25,676 2.18 11,185 5.01
    20 63,317,000 29,478 2.15 17,051 3.71
    21 33,824,000 20,916 1.62 9,103 3.72
    22 33,786,000 28,410 1.19 11,056 3.06
    X 131,245,000 34,842 3.77 20,400 6.43
    Y 21,753,000 4,193 5.19 1,784 12.19
    RefSeq 15,696,674 14,534 1.08
    Totals 2,710,164,000 1,419,190 1.91 887,450 3.05

    The nomenclature for SNPs include several variations for an individual SNP, while lacking a common consensus.

    The rs### standard is that which has been adopted by dbSNP and uses the prefix "rs", for "reference SNP", followed by a unique and arbitrary number. [47] SNPs are frequently referred to by their dbSNP rs number, as in the examples above.

    The Human Genome Variation Society (HGVS) uses a standard which conveys more information about the SNP. Examples are:

    • c.76A>T: "c." for coding region, followed by a number for the position of the nucleotide, followed by a one-letter abbreviation for the nucleotide (A, C, G, T or U), followed by a greater than sign (">") to indicate substitution, followed by the abbreviation of the nucleotide which replaces the former [48][49][50]
    • p.Ser123Arg: "p." for protein, followed by a three-letter abbreviation for the amino acid, followed by a number for the position of the amino acid, followed by the abbreviation of the amino acid which replaces the former. [51]

    SNPs can be easily assayed due to only containing two possible alleles and three possible genotypes involving the two alleles: homozygous A, homozygous B and heterozygous AB, leading to many possible techniques for analysis. Some include: DNA sequencing capillary electrophoresis mass spectrometry single-strand conformation polymorphism (SSCP) single base extension electrochemical analysis denaturating HPLC and gel electrophoresis restriction fragment length polymorphism and hybridization analysis.

    7.12: Genes and alleles - Biology

    Mendel implied that only two alleles, one dominant and one recessive, could exist for a given gene. We now know that this is an oversimplification. Although individual humans (and all diploid organisms) can only have two alleles for a given gene, multiple alleles may exist at the population level such that many combinations of two alleles are observed. Note that when many alleles exist for the same gene, the convention is to denote the most common phenotype or genotype among wild animals as the wild type (often abbreviated “+”) this is considered the standard or norm. All other phenotypes or genotypes are considered variants of this standard, meaning that they deviate from the wild type. The variant may be recessive or dominant to the wild-type allele.

    An example of multiple alleles is coat color in rabbits (Figure 1). Here, four alleles exist for the c gene. The wild-type version, C + C + , is expressed as brown fur. The chinchilla phenotype, c ch c ch, is expressed as black-tipped white fur. The Himalayan phenotype, c h c h, has black fur on the extremities and white fur elsewhere. Finally, the albino, or “colorless” phenotype, cc, is expressed as white fur. In cases of multiple alleles, dominance hierarchies can exist. In this case, the wild-type allele is dominant over all the others, chinchilla is incompletely dominant over Himalayan and albino, and Himalayan is dominant over albino. This hierarchy, or allelic series, was revealed by observing the phenotypes of each possible heterozygote offspring.

    Figure 1. Four different alleles exist for the rabbit coat color (C) gene.

    Figure 2. As seen in comparing the wild-type Drosophila (left) and the Antennapedia mutant (right), the Antennapedia mutant has legs on its head in place of antennae.

    The complete dominance of a wild-type phenotype over all other mutants often occurs as an effect of “dosage” of a specific gene product, such that the wild-type allele supplies the correct amount of gene product whereas the mutant alleles cannot. For the allelic series in rabbits, the wild-type allele may supply a given dosage of fur pigment, whereas the mutants supply a lesser dosage or none at all. Interestingly, the Himalayan phenotype is the result of an allele that produces a temperature-sensitive gene product that only produces pigment in the cooler extremities of the rabbit’s body.

    Alternatively, one mutant allele can be dominant over all other phenotypes, including the wild type. This may occur when the mutant allele somehow interferes with the genetic message so that even a heterozygote with one wild-type allele copy expresses the mutant phenotype. One way in which the mutant allele can interfere is by enhancing the function of the wild-type gene product or changing its distribution in the body.

    One example of this is the Antennapedia mutation in Drosophila (Figure 2). In this case, the mutant allele expands the distribution of the gene product, and as a result, the Antennapedia heterozygote develops legs on its head where its antennae should be.

    Multiple Alleles Confer Drug Resistance in the Malaria Parasite

    Malaria is a parasitic disease in humans that is transmitted by infected female mosquitoes, including Anopheles gambiae (Figure 3a), and is characterized by cyclic high fevers, chills, flu-like symptoms, and severe anemia. Plasmodium falciparum and P. vivax are the most common causative agents of malaria, and P. falciparum is the most deadly (Figure 3b). When promptly and correctly treated, P. falciparummalaria has a mortality rate of 0.1 percent. However, in some parts of the world, the parasite has evolved resistance to commonly used malaria treatments, so the most effective malarial treatments can vary by geographic region.

    Figure 3. The (a) Anopheles gambiae, or African malaria mosquito, acts as a vector in the transmission to humans of the malaria-causing parasite (b) Plasmodium falciparum, here visualized using false-color transmission electron microscopy. (credit a: James D. Gathany credit b: Ute Frevert false color by Margaret Shear scale-bar data from Matt Russell)

    In Southeast Asia, Africa, and South America, P. falciparum has developed resistance to the anti-malarial drugs chloroquine, mefloquine, and sulfadoxine-pyrimethamine. P. falciparum, which is haploid during the life stage in which it is infectious to humans, has evolved multiple drug-resistant mutant alleles of the dhps gene. Varying degrees of sulfadoxine resistance are associated with each of these alleles. Being haploid, P. falciparum needs only one drug-resistant allele to express this trait.

    In Southeast Asia, different sulfadoxine-resistant alleles of the dhps gene are localized to different geographic regions. This is a common evolutionary phenomenon that occurs because drug-resistant mutants arise in a population and interbreed with other P. falciparum isolates in close proximity. Sulfadoxine-resistant parasites cause considerable human hardship in regions where this drug is widely used as an over-the-counter malaria remedy. As is common with pathogens that multiply to large numbers within an infection cycle, P. falciparum evolves relatively rapidly (over a decade or so) in response to the selective pressure of commonly used anti-malarial drugs. For this reason, scientists must constantly work to develop new drugs or drug combinations to combat the worldwide malaria burden. [1]

    Watch the video: Chromosomes, genes, and alleles IB Biology (June 2022).


  1. Tokasa

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  2. Jular

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  3. Fallon

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  4. Labid

    And what is the result?

  5. Toktilar

    She should tell it - the falsehood.

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