We are searching data for your request:
Upon completion, a link will appear to access the found materials.
5 Critical biological discoveries from the last 25 years
Breakthroughs in biology have a massive impact. These are some of the most groundbreaking biological developments from the last 25 years.
Biology studies every living thing on Earth, from the simplest and smallest single-celled organisms to the complexity of the human brain. The science of biology shapes everything from agriculture to psychology.
And like most sciences, biology is rapidly advancing due to advances in technology. Breakthroughs in biology have a huge impact on our world.
In this article, we explore the five most groundbreaking biology breakthroughs from the past few decades. And to learn more about biology, be sure to check out the huge range of biology flashcards in Brainscape, created by students and educators around the globe.
Reference genomes are valuable resources for biological research ranging from specific gene function through to studying evolution. After decades of investment, the high-quality human reference genome (GRCh38) has revolutionized clinical diagnostics. However, the human genome still contains gaps and only recently has a telomere-to-telomere assembly of a single human chromosome been within reach . Nevertheless, a reference genome does not represent the vast genetic variation between any two individuals. The aggregation of genetic variation from multiple genomes is available through consortia (e.g., gnomAD), and graph genomes provide a useful way of integrating structural variation and reference genomes. The current laboratory methods used to assay genetic variation are often a combination of techniques such as bulk short- and long-read sequencing, optical mapping, and cytogenetics. A complementary tool for chromosome-scale assembly discussed here is the combination of accurate short-read sequencing applied to nuclear DNA from single gametes. This review is intended for a broad audience. Readers familiar with genetic linkage and genome assembly may wish to advance to the “Platforms for constructing iMaps—proof of principle and opportunities” section.
High-throughput DNA sequencing has made genome assembly more accessible however, fragmented DNA sequences still need to be assembled into highly contiguous chromosomal sequences. Genome assembly has historically required a dense genetic map to anchor and orient short DNA sequences onto larger chromosome-scale fragments. A genetic map is an ordering and spacing of loci (identified by markers) on a chromosome map (Fig. 1a) . Genetic maps can be built for sexually reproducing species due to chromosome reshuffling in a process called meiosis. Meiosis serves two purposes: (1) the generation of haploid gametes, sperm, and eggs and (2) the genetic diversification of gametes, by chromosome segregation and meiotic crossovers (Fig. 1b). Meiotic crossovers (COs) are large reciprocal exchanges of genetic material between homologous chromosomes, which generate unique combinations of alleles . Crossover frequencies between linked markers are used to calculate genetic distances, measured in centiMorgans, which enable marker ordering at a fine scale. Historically, genetic maps preceded physical chromosome maps, and physical sequence was anchored to its appropriate position on a genetic map. However, high-throughput sequencing revolutionized the analysis of genomes by generating orders of magnitude more data at a reasonable cost. In turn, marker density increased in line with sequencing capabilities rapidly changing how researchers assemble genomes. More recent advances in genome assembly include optical mapping, long-read technologies, strand-seq, and software capable of managing the assembly of large repetitive genomes [4,5,6,7]. A flow-on effect from the construction of marker-dense physical maps and genetic maps was an increased capacity to research the non-random distribution of crossovers throughout the human and mouse genomes [8,9,10].
Meiosis and linkage. a Meiosis involves two rounds of cell divisions following DNA replication. In the first division, meiosis I, homologous chromosomes pair for crossover formation, creating a physical link (chiasmata), to exchange some genetic material and resulting in two haploid cells that have half the number of chromosomes as the original cell. Meiosis II occurs when the sister chromatids segregate to generate four genetically unique gametes (sperm or egg). b Comparison of genetic, cytological, and physical maps, all of which characterize genetic markers. A genetic map is based on the frequency of co-segregation of linked markers. A cytological map can be constructed by labeling certain DNA markers or particular staining methods. cM, centiMorgan Mbp, megabase base pair of DNA. c An iMap with an inversion does not alter the DNA sequence but changes the linear ordering of markers. Translocation as a result of chromosome breakage and fusion affects crossover formation and changes the marker distance
The original mnd mutant was generated by X-ray mutagenesis at our institute in the 1950s . The most conspicuous characteristic of mnd plants is their shortened plastochron, that is, a faster rate of leaf initiation. Mutants have on average two times more leaves than wildtype plants as a result of a faster emergence of leaves (Figure 1). Moreover, culm internode lengths are decreased in the mutant. Despite the larger number of internode (eight to nine in the mutant versus four to five in the wildtype), plant height is reduced by about one third under field conditions, but not in the greenhouse (Figure 1d). Apart from spacing, also the shape of leaves is altered in the mutant: leaves are narrower and more erect compared to the wildtype. Additional characteristics of mnd are an increased number of tillers (vegetative shoot branches arising from lateral meristems) and shorter spikes (Figure 1b Additional file 1: Figure S1).
Phenotypic characteristics of mnd plants. (a) Mutants (right) have a significantly higher number of nodes compared to the wildtype (left) and show a semi-dwarf growth habit. (b) Ear length is reduced under field conditions (left: wildtype, right: mutant). (c) Leaf formation in early developmental stages is faster in mnd plants (right) compared to the wildtype (left). (d) Mutant plants (right) grown under greenhouse conditions have more internodes without a dwarfing phenotype. The wildtype is shown to the left.
Allele frequency mapping
We adopted a strategy similar to the ShoreMap  and MutMap  methods that inspect the genome-wide distribution of allele frequency in phenotypic bulks of an F2 population developed by outcrossing the mutant to a wildtype genotype (Additional file 2: Figure S2). Progeny of a cross between an mnd plant with a wildtype plant of cultivar (cv.) Barke was selfed to obtain an F2 population of 100 individuals. The mnd allele segregated in this population as a monogenic recessive trait (19 mutants, 81 wildtype plants, χ 2 = 1.92, P value = 0.17). DNA from 18 mutant plants and 30 randomly selected wildtype plants was combined into two pools, which were subjected to exome capture and subsequent high-throughput sequencing on the Illumina HiSeq2000, yielding 82 million and 70 million 2 × 100 bp read pairs for the mutant and wildtype pools, respectively. Reads were mapped onto the whole-genome shotgun (WGS) assembly of cv. Barke  and single nucleotide polymorphisms (SNPs) were detected. The visualization of allele frequencies at SNP positions along the physical and genetic map of barley revealed a single sharp peak on the long arm of chromosome 5H, where the frequency of the mutant allele increased to over 95% and dropped to about 30% in the wildtype pools (Figure 2a). Note that the ratio between the number of plants that are heterozygous at the mnd locus and the number of those that are homozygous for the wildtype allele is expected to be 2:1 in the wildtype bulk. Selected SNPs in the interval of 80 to 110 cM in the map of  were converted to single marker assays (Additional file 3: Table S2). Genetic mapping in the F2 population confirmed these markers to be tightly linked to the mnd phenotype (Figure 2b).
Mapping-by-sequencing. (a) The frequency of the alternate allele relative to the Barke reference in the two capture pools is visualized along the integrated physical and genetic map of barley . (b) Ten SNPs from the target intervals were converted to CAPS markers and genotyped on the entire F2 mapping population. The number of recombinants between the markers (top axis) and marker positions in genetically anchored WGS assembly  (bottom axis) are indicated. Sequence contigs carrying large (>150 bp) putative deletions are shown as gray rectangles. (c) Read depth of MND (MLOC_64838.2) in the two capture pools. The positions of the two exons of MND in WGS contig 49382 are shown as green rectangles. At the bottom, the number of sequence reads per base position is shown for the mutant pool (red) and the wildtype pool (black). Because of a single heterozygous plant that was erroneously included in the mutant bulk, MND is also present at low read coverage in the mutant pool. Note that the highest coverage peak is in the short intron (130 bp) of MND due to a higher number of redundant capture probes at the ends of the two exons.
Read depth analysis identifies a likely candidate gene
As X-ray mutagenesis commonly induces large deletions , we queried our sequence data for exome capture targets that are covered by sequence reads in the wildtype pool, but not in the mutant pool. As gene models and exome capture targets are given as coordinates on the WGS assembly of cv. Morex, reads were mapped again onto this assembly and read coverage was calculated at each base position and averaged across contiguously covered intervals corresponding to capture targets. Marker assays revealed that we had erroneously included one heterozygous plant in the mutant bulk, which was confirmed by phenotypic analysis of the corresponding F3 family. Thus, we expected a small number of sequence reads at the mnd locus in the mutant pool originating from the single heterozygote. At genome scale, we identified 435 intervals (whole genome shotgun sequence contigs carrying the respective exome capture targets) that were at least 150 bp and fulfilled our rather relaxed criteria for potential deletions (Additional file 4: Table S3). Of these targets, 18 were mapped by POPSEQ  to the broadly defined interval (5H, 80 cM - 110 cM), 278 were mapped to other regions of the genome and 139 were unmapped. Out of all 435 intervals, 48 were located on contigs of the WGS assembly of cv. Morex  with high-confidence genes predicted on. All but two of these genes had a functional annotation. Among the contigs carrying putatively deleted capture targets and localized to our target interval, six carried high-confidence genes (Figure 2b, Table 1). One of these, contig 49382 was anchored at 96 cM in the POPSEQ map  and thus closest to the allele frequency peak (97%) in the mutant bulk at 97 cM (Additional file 5: Table S1). Moreover, contig 49382 harbored two putatively deleted regions, among them the longest detected interval. Note that a single large deletion would rather show up as several smaller deleted target intervals because exome capture targets only disjoint exons, and introns are represented neither in the mutant nor the wildtype. The deleted regions on contig 49382 overlapped with the two exons of the high-confidence gene MLOC_64838.2 annotated as ‘Cytochrome P450’ (Figure 2c). This gene was the only gene predicted on contig 49382. A BLAST search of the protein sequence against the rice and Arabidopsis genomes identified members of the CYP78A family of cytochrome P450 enzymes. One of these genes, rice CYP78A11, is known as PLASTOCHRON1 (PLA1) . As the rice pla1 phenotype (rapid leaf initiation, reduced leaf size, and plant height) closely resembles barley mnd, we considered MLOC_64838.2 as a promising candidate.
Mutant analysis confirms MLOC_64838.2 as HvMND
PCR amplification of the candidate succeeded in cultivars Morex and Barke, but failed in the mutant MHOR474. By contrast, we were able to amplify genes that were predicted to be close to MLOC_64838.2 through collinearity to the model grass Brachypodium distachyon and were anchored genetically within the mapping interval. Screening of our TILLING (Targeting Local Lesions IN Genomes) population  identified 20 EMS mutants with synonymous and 17 mutants with non-synonymous changes. One mutant carrying a SNP (G261A) that led to a premature stop codon in heterozygous state (Table 2) was selected to check the phenotypic effects. Among the offspring of this plant, 15 plants were heterozygous, two were homozygous for the wildtype allele and five were homozygous for the mutant allele. All of the homozygous mutant plants (and only these) showed a significantly increased number of internodes, characteristic of the mnd phenotype (Figure 3a,b). Furthermore, introgressions of two Bowman nearly-isogenic lines characterized as mnd (BW520 and BW522) had been mapped to chromosome arm 5HL previously . Sanger sequencing of MLOC_64838.2 in BW520 revealed one non-synonymous SNP in the coding sequence. The gene could not be amplified in BW522, whereas all syntenic genes were present (Table 3). We ordered 37 mutant accessions from the Nordic Gene Bank (NordGen) that were described as mnd. Resequencing of our candidate in these lines revealed four amino acid changes, 16 premature stop codons, one disruption of a splice site, one 107 bp deletion in the second exon, and six complete deletions (Additional file 6: Table S4). When grown in the greenhouse, all mutants showed the mnd phenotype (Figure 3c-e). We considered this large number of molecular lesions found in several independent mutant collections as conclusive evidence that loss-of-function of MLOC_64838.2 underlies the mnd phenotype and named this gene as HvMND.
mnd mutants. TILLING mutants (b) with a premature stop codon within the MND genes show a significantly faster leaf initiation compared to the wildtype (a). mnd mutants in the same genetic background (cv. Kristina) with a single amino acid change (c), a complete gene deletion (d), and a premature stop codon (e). The type of mutation did not affect the severity of the mnd phenotype under greenhouse conditions. The complete growth stature (left) and a single isolated tiller (right) is shown for each plant in (c, d, and e).
MND is a member of the CYP78A subfamily of cytochrome P450 enzymes
MND is a member of the CYP78A family of cytochrome P450 enzymes. We found four CYP78A genes in the whole genome shotgun assembly of barley (Figure 4). Though the mnd phenotype mimics pla1, MND is not an ortholog of PLA1. The ortholog of MND in rice, Os09g09g3594, is located in a syntenic region on rice chromosome 9  and shows 75% identity with MND on the protein level. PLA1 does not have a clear ortholog in barley (Figure 4), but has approximately 54% amino acid sequence identity to MND and two other CYP78A genes, MLOC_68312.1 and MLOC_68718.1. As PLA1 has orthologs in maize and Arabidopsis (Figure 3), an ancient ortholog of PLA1 might have been lost in the Poaceae lineage after its split from rice and maize. In line with this hypothesis, we did not find PLA1 orthologs in barley, the wheat progenitors, T. urartu and Ae. tauschii, and B. distachyon.
Phylogenetic analysis of CYP78A genes. A phylogenetic tree of 38 protein sequences of CYP78A from different species was constructed with MEGA5. Abbreviated species names are given before gene identifiers: Aegilops tauschii (Aet), A. thaliana (Ath), B. distachyon (Bd), H. vulgare (Hv), Oryza sativa (Os), T. urartu (Tu), Zea mays (Zm). Gene names are given after identifiers if available. The CYP75B1 gene TT7 of A. thaliana was used as an outgroup. The bootstrap method was applied to test for statistical significance of branches. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches. Branches with insufficient bootstrap support (<50%) were collapsed to obtain a consensus tree.
We looked up the expression profile of HvMND and other barley genes of CYP78A family in the eight tissues examined by The International Barley Genome Sequencing Consortium . Expression of CYP78A genes was found across all tissues, with different genes of the family being most abundant in different tissues (Figure 5). Among the four CYP78A genes, HvMND was the most ubiquitous, being expressed in all samples, although only weak expression was detected in developing grains 15 days after anthesis.
Expression of MND and three other CYP78A genes of barley. Transcript abundance is given as fragments per kilobase of exon per million reads mapped (FPKM) across eight different tissues or developmental stages. A gene was considered expressed if its FPKM value was above the threshold of 0.4  (marked by gray line). All data were taken from .
A physical map of the mndlocus
There may be concerns as to the general applicability of our strategy to other map-based cloning projects. The isolation of MND was facilitated by the facts that its homolog PLA1 in the model species rice is well characterized and that the phenotype of PLA1 knockout mutants mirrors mnd. If, moreover, MND had not been represented in the exome capture target space, no obvious candidate could have been pinpointed. In this case, the distribution of allele frequency confirmed by genetic mapping of markers developed from in silico variants would have only delimited a target interval to be subjected to further scrutiny. As was proposed earlier, the genome-wide physical map of barley should principally obviate the need of constructing local physical maps by map-based cloning to delimit candidate genes . BAC survey sequence data associated with the physical map of barley  can be used to associate marker sequences or candidate genes with physical contigs, whose minimum tiling paths  can then be sequenced. Thus it was our intention to test whether the information provided by the bulked-segregant sequencing experiment was sufficient to select a physical contig of the genome-wide physical map for delimitation of the target locus region and identification of a candidate gene.
We put this strategy into practice to retrieve the physical map around the MND locus (Figure 6). The major steps towards this aim were the identification of BAC contigs of the barley genome physical map harboring MND as well as its flanking markers, sequencing the minimum tiling paths (MTPs) of these contigs and perform integrative sequence analysis to predict gene models on the BAC sequence assemblies. First, we identified through BLAST searches against the sequence resources integrated to the physical map of barley  two fingerprinted contigs, contig_45097 and contig_46058, which harbored two genes whose orthologs in Brachypodium were the closest neighbors of the ortholog of MND, as well as the co-segregating and a distal flanking markers M4 and M5. Likewise, contig_1020 was found to harbor marker M3, flanking MND in proximal direction. We found no BAC sequences with high similarity to MND. This is not unexpected as only 1.1 Gb of genomic sequence information (approximately 20% of the barley genome) is directly provided by the physical map of barley (6,278 sequenced BAC clones, BAC end sequences) . However, a BAC harboring MND and assigned to fingerprinted contig_45097 was identified through BAC library screening.
A physical map of the mnd locus. (a) Fingerprinted (FP) contigs carrying flanking and co-segregating markers (triangles) as well as the MND gene (diamond). The physical map is not contiguous between contigs 1020 and 45097. A scale bar for all panels is given on top. (b) Sequenced BACs. BACs were positioned according to their FPC coordinates . (c) Gene models and orthologous Brachypodium genes. Tracks (from top to bottom) mark the positions of (1) gene models present in both de novo predictions with Augustus and the IBSC gene models (green - high-confidence (HC) IBSC genes, blue - low-confidence (LC) IBSC genes) (2) gene models only predicted by Augustus (3) gene models predicted by IBSC (green - HC genes blue - LC genes) (4) orthologous Brachypodium genes, only the last four digits of the gene identifier Bradi4g3xxxx are given. (d) SNPs discovered by exome sequencing and anchored to BAC sequences are marked by vertical lines.
Next, we assembled the MTPs of these three physical contigs (Figure 6a) by sequencing 38 BACs (Figure 6b Additional file 7: Table S5) on the Illumina HiSeq2000. Single BACs were assembled to ‘phase-1’ quality, that is, unordered contig sequences. All-against-all BLAST searches of BAC assemblies confirmed the contiguity of contigs 46058 and 45097 as well as the overlap between them. Contig_1020 did not overlap with either of them. Markers M4 and M5 were located on a contiguous sequence scaffold, which enabled to us to estimate an approximate ratio between physical and genetic distance at the MND locus of approximately 740 kb per cM.
In the following step, gene models (Figure 6c) were predicted on repeat masked BAC assemblies by using an ab initio method and through alignment of gene models defined on the Morex WGS assembly . Overall, 98 non-redundant gene models were defined on the BAC sequences. Twenty-five genes were found by both methods, 35 were only predicted ab initio and likely represent pseudogenes. Thirty-eight genes were included only in the IBSC annotation, the majority (23 genes) of them classified as low confidence transcripts, which are also putative pseudogenes or gene fragments. Gene order was largely collinear to Brachypodium with some minor rearrangements (Figure 6c). Synteny enabled us to orient contig_1020 relative to the other two contigs.
Finally, we attempted to estimate the size of the gap that was remaining between fingerprinted contigs 1020 and 45097 and to find additional BACs that may bridge it. As 10 Brachypodium genes between Bradi4g 35770 and Bradi4g35860 are missing, the gap between contigs 1020 and 45097 may size up to several hundred kilobases, or the gap is small and may represent a region with lack of collinearity between barley and Brachypodium. We linked WGS contigs carrying the barley orthologs of the ‘missing’ Brachypodium genes to end sequences of BACs that were part of two short physical contigs (45219 and 45903) of sizes 227 and 236 kb (Table 4). These contigs carry the orthologs of Bradi4g35840 and Bradi4g35800, further supporting overall collinearity with Brachypodium in this genomic region. Moreover, one BAC end sequence (HF198106) pertaining to contig_45219 matched with high identity (99.9% identity over 755 bp) to two BAC sequences of contigs_45097, indicating that these two FP contigs may overlap.
In summary, at the genetic resolution provided by 100 F2 plants, we were not able to obtain in one step a single physical sequence scaffold of overlapping BAC clones from the MND locus between the two closest flanking markers. However, the remaining gap may be closed by sequencing the MTP of the two additional FP contigs identified based on conserved synteny information to Brachypodium. Furthermore, increasing the genetic resolution significantly to several thousand meioses, as often required in barley, may allow to resolve recombinations between marker M4 and the MND gene, which would result in landing with flanking markers on a single BAC contig scaffold provided by the physical map of barley. Thus, in spite of the advanced genomic resources that are now available for barley, an iterative process involving more than one round of MTP sequencing and overlap analysis may still be required to obtain a contiguous physical map of a candidate locus.
The earliest fate maps were based on direct observation of the embryos of ascidians or other marine invertebrates.  Modern fate mapping began in 1929 when Walter Vogt marked the groups of cells using a dyed agar chip and tracked them through gastrulation.  In 1978, horseradish peroxidase (HRP) was introduced as a marker. HRP was more effective than previous markers, but required embryos to be fixed before viewing.  Genetic fate mapping is a technique developed in 1981 which uses a site-specific recombinase to track cell lineage genetically. Today, fate mapping is an important tool in many fields of biology research, such as developmental biology,  stem cell research, and kidney research. 
Fate mapping and cell lineage are similar but distinct topics, although there is often overlap. For example, the development of the complete cell lineage of C. elegans can be described as the fate maps of each cell division stacked hierarchically.  The distinction between the topics is in the type of information included. Fate mapping shows which tissues come from which part of the embryo at a certain stage in development, whereas cell lineage shows the relationships between cells at each division.  A cell lineage can be used to generate a fate map, and in cases like C. elegans, successive fate mapping is used to develop a cell lineage. 
Fate mapping is accomplished by inserting a heritable genetic mark into a cell. Typically, this is a fluorescent protein. Therefore, any progeny of the cell will have this genetic mark. It can also be done through the use of molecular barcodes, which are introduced to the cell by retroviruses. 
17.2 Mapping Genomes
By the end of this section, you will be able to do the following:
- Define genomics
- Describe genetic and physical maps
- Describe genomic mapping methods
Genomics is the study of entire genomes, including the complete set of genes, their nucleotide sequence and organization, and their interactions within a species and with other species. Genome mapping is the process of finding the locations of genes on each chromosome. The maps that genome mapping create are comparable to the maps that we use to navigate streets. A genetic map is an illustration that lists genes and their location on a chromosome. Genetic maps provide the big picture (similar to an interstate highway map) and use genetic markers (similar to landmarks). A genetic marker is a gene or sequence on a chromosome that co-segregates (shows genetic linkage) with a specific trait. Early geneticists called this linkage analysis. Physical maps present the intimate details of smaller chromosome regions (similar to a detailed road map). A physical map is a representation of the physical distance, in nucleotides, between genes or genetic markers. Both genetic linkage maps and physical maps are required to build a genome’s complete picture. Having a complete genome map of the genome makes it easier for researchers to study individual genes. Human genome maps help researchers in their efforts to identify human disease-causing genes related to illnesses like cancer, heart disease, and cystic fibrosis. We can use genome mapping in a variety of other applications, such as using live microbes to clean up pollutants or even prevent pollution. Research involving plant genome mapping may lead to producing higher crop yields or developing plants that better adapt to climate change.
The study of genetic maps begins with linkage analysis , a procedure that analyzes the recombination frequency between genes to determine if they are linked or show independent assortment. Scientists used the term linkage before the discovery of DNA. Early geneticists relied on observing phenotypic changes to understand an organism’s genotype. Shortly after Gregor Mendel (the father of modern genetics) proposed that traits were determined by what we now call genes, other researchers observed that different traits were often inherited together, and thereby deduced that the genes were physically linked by their location on the same chromosome. Gene mapping relative to each other based on linkage analysis led to developing the first genetic maps.
Observations that certain traits were always linked and certain others were not linked came from studying the offspring of crosses between parents with different traits. For example, in garden pea experiments, researchers discovered, that the flower’s color and plant pollen’s shape were linked traits, and therefore the genes encoding these traits were in close proximity on the same chromosome. We call exchanging DNA between homologous chromosome pairs genetic recombination , which occurs by crossing over DNA between homologous DNA strands, such as nonsister chromatids. Linkage analysis involves studying the recombination frequency between any two genes. The greater the distance between two genes, the higher the chance that a recombination event will occur between them, and the higher the recombination frequency between them. Figure 17.11 shows two possibilities for recombination between two nonsister chromatids during meiosis. If the recombination frequency between two genes is less than 50 percent, they are linked.
The generation of genetic maps requires markers, just as a road map requires landmarks (such as rivers and mountains). Scientists based early genetic maps on using known genes as markers. Scientists now use more sophisticated markers, including those based on non-coding DNA, to compare individuals’ genomes in a population. Although individuals of a given species are genetically similar, they are not identical. Every individual has a unique set of traits. These minor differences in the genome between individuals in a population are useful for genetic mapping purposes. In general, a good genetic marker is a region on the chromosome that shows variability or polymorphism (multiple forms) in the population.
Some genetic markers that scientists use in generating genetic maps are restriction fragment length polymorphisms (RFLP), variable number of tandem repeats (VNTRs), microsatellite polymorphisms , and the single nucleotide polymorphisms (SNPs). We can detect RFLPs (sometimes pronounced “rif-lips”) when the DNA of an individual is cut with a restriction endonuclease that recognizes specific sequences in the DNA to generate a series of DNA fragments, which we can then analyze using gel electrophoresis. Every individual’s DNA will give rise to a unique pattern of bands when cut with a particular set of restriction endonucleases. Scientists sometimes refer to this as an individual’s DNA “fingerprint.” Certain chromosome regions that are subject to polymorphism will lead to generating the unique banding pattern. VNTRs are repeated sets of nucleotides present in DNA’s non-coding regions. Non-coding, or “junk,” DNA has no known biological function however, research shows that much of this DNA is actually transcribed. While its function is uncertain, it is certainly active, and it may be involved in regulating coding genes. The number of repeats may vary in a population’s individual organisms. Microsatellite polymorphisms are similar to VNTRs, but the repeat unit is very small. SNPs are variations in a single nucleotide.
Because genetic maps rely completely on the natural process of recombination, natural increases or decreases in the recombination level given genome area affects mapping. Some parts of the genome are recombination hotspots whereas, others do not show a propensity for recombination. For this reason, it is important to look at mapping information developed by multiple methods.
A physical map provides detail of the actual physical distance between genetic markers, as well as the number of nucleotides. There are three methods scientists use to create a physical map: cytogenetic mapping, radiation hybrid mapping, and sequence mapping. Cytogenetic mapping uses information from microscopic analysis of stained chromosome sections (Figure 17.12). It is possible to determine the approximate distance between genetic markers using cytogenetic mapping, but not the exact distance (number of base pairs). Radiation hybrid mapping uses radiation, such as x-rays, to break the DNA into fragments. We can adjust the radiation amount to create smaller or larger fragments. This technique overcomes the limitation of genetic mapping, and we can adjust the radiation so that increased or decreased recombination frequency does not affect it. Sequence mapping resulted from DNA sequencing technology that allowed for creating detailed physical maps with distances measured in terms of the number of base pairs. Creating genomic libraries and complementary DNA (cDNA) libraries (collections of cloned sequences or all DNA from a genome) has sped the physical mapping process. A genetic site that scientists use to generate a physical map with sequencing technology (a sequence-tagged site, or STS) is a unique sequence in the genome with a known exact chromosomal location. An expressed sequence tag (EST) and a single sequence length polymorphism (SSLP) are common STSs. An EST is a short STS that we can identify with cDNA libraries, while we obtain SSLPs from known genetic markers, which provide a link between genetic and physical maps.
Genetic and Physical Maps Integration
Genetic maps provide the outline and physical maps provide the details. It is easy to understand why both genome mapping technique types are important to show the big picture. Scientists use information from each technique in combination to study the genome. Scientists are using genomic mapping with different model organisms for research. Genome mapping is still an ongoing process, and as researchers develop more advanced techniques, they expect more breakthroughs. Genome mapping is similar to completing a complicated puzzle using every piece of available data. Mapping information generated in laboratories all over the world goes into central databases, such as GenBank at the National Center for Biotechnology Information (NCBI). Researchers are making efforts for the information to be more easily accessible to other researchers and the general public. Just as we use global positioning systems instead of paper maps to navigate through roadways, NCBI has created a genome viewer tool to simplify the data-mining process.
Scientific Method Connection
How to Use a Genome Map Viewer
Problem statement: Do the human, macaque, and mouse genomes contain common DNA sequences?
Develop a hypothesis.
Go to this website to test the hypothesis.
The web page displays the comparison of the gene sequences of many organisms to the Human Insulin Receptor gene. Explore the type of information provided, select the groups of organisms needed for testing of the hypothesis from the top portion of the displayed data. Focus the attention to the bottom part, the Selected Orthologues. Explore which columns are relevant to the needed information.
On the same page, there are other options to explore, not all are necessary for the task, however it might give more insight to the value of genome/gene comparisons.
Link to Learning
Online Mendelian Inheritance in Man (OMIM) is a searchable online catalog of human genes and genetic disorders. This website shows genome mapping information, and also details the history and research of each trait and disorder. Click this link to search for traits (such as handedness) and genetic disorders (such as diabetes).
Summary – Genetic Map vs Physical Map
Genome studies use genetic markers located in the chromosomes. To study these markers, they have to be mapped using different techniques. Mendelian genetics is the basis of genetic maps. During the genetic mapping, different traits are studied for many generations and the genes are analyzed using gene linkage and gene association studies. In contrast, Physical gene maps involve the isolation and characterization of genetic markers physically by extracting it. This is the main difference between genetic map and physical map.
1.O’Rourke, Jamie A. “Genetic and Physical Map Correlation.” Encyclopedia of Life Sciences, Nov. 2014. Available here
2.“Genetic Mapping Fact Sheet.” National Human Genome Research Institute (NHGRI). Available here
1.’File:NHGRI Fact Sheet- Genetic Mapping (27058469495)’By National Human Genome Research Institute (NHGRI) from Bethesda, MD, USA – NHGRI Fact Sheet: Genetic Mapping, (CC BY 2.0) via Commons Wikimedia
2.’Human chromosome Y – 400 550 850 bphs’By National Center for Biotechnology Information, U.S. National Library of Medicine (Public Domain) via Commons Wikimedia
About the Author: Samanthi
Dr.Samanthi Udayangani holds a B.Sc. Degree in Plant Science, M.Sc. in Molecular and Applied Microbiology, and PhD in Applied Microbiology. Her research interests include Bio-fertilizers, Plant-Microbe Interactions, Molecular Microbiology, Soil Fungi, and Fungal Ecology.
So can vaccines be made with only genome mapping or do you need genome sequencing to create a vaccine?