The document discusses the use of SNP haplotype maps in plant breeding. It begins with an introduction to genetic variations like SNPs and haplotypes. It then discusses topics like haplotype construction, inference, and factors affecting them. The document presents two case studies, one on developing a hapmap for pepper and the other on introducing novel diversity in Brassica using a concept called Heterotic Haplotype Capture. Key outputs of the studies include genome-scale data and immortal heterotic populations for genomic prediction and understanding heterosis.
A new era of genomics for plant science research has opened due the complete genome sequencing projects of Arabidopsis thaliana and rice. The sequence information available in public database has highlighted the need to develop genome scale reverse genetic strategies for functional analysis (Till et al., 2003). As most of the phenotypes are obscure, the forward genetics can hardly meet the demand of a high throughput and large-scale survey of gene functions. Targeting Induced Local Lesions in Genome TILLING is a general reverse genetic technique that combines chemical mutagenesis with PCR based screening to identity point mutations in regions of interest (McCallum et al., 2000). This strategy works with a mismatch-specific endonuclease to detect induced or natural DNA polymorphisms in genes of interest. A newly developed general reverse genetic strategy helps to locate an allelic series of induced point mutations in genes of interest. It allows the rapid and inexpensive detection of induced point mutations in populations of physically or chemically mutagenized individuals. To create an induced population with the use of physical/chemical mutagens is the first prerequisite for TILLING approach. Most of the plant species are compatible with this technique due to their self-fertilized nature and the seeds produced by these plants can be stored for long periods of time (Borevitz et al., 2003). The seeds are treated with mutagens and raised to harvest M1 plants, which are consequently, self-fertilized to raise the M2 population. DNA extracted from M2 plants is used in mutational screening (Colbert et al., 2001). To avoid mixing of the same mutation only one M2 plant from each M1 is used for DNA extraction (Till et al., 2007). The M3 seeds produce by selfing the M2 progeny can be well preserved for long term storage. Ethyl methane sulfonate (EMS) has been extensively used as a chemical mutagen in TILLING studies in plants to generate mutant populations, although other mutagens can be effective. EMS produces transitional mutations (G/C, A/T) by alkylating G residues which pairs with T instead of the conservative base pairing with C (Nagy et al., 2003). It is a constructive approach for users to attempt a range of chemical mutagens to assess the lethality and sterility on germinal tissue before creating large mutant populations.
A new era of genomics for plant science research has opened due the complete genome sequencing projects of Arabidopsis thaliana and rice. The sequence information available in public database has highlighted the need to develop genome scale reverse genetic strategies for functional analysis (Till et al., 2003). As most of the phenotypes are obscure, the forward genetics can hardly meet the demand of a high throughput and large-scale survey of gene functions. Targeting Induced Local Lesions in Genome TILLING is a general reverse genetic technique that combines chemical mutagenesis with PCR based screening to identity point mutations in regions of interest (McCallum et al., 2000). This strategy works with a mismatch-specific endonuclease to detect induced or natural DNA polymorphisms in genes of interest. A newly developed general reverse genetic strategy helps to locate an allelic series of induced point mutations in genes of interest. It allows the rapid and inexpensive detection of induced point mutations in populations of physically or chemically mutagenized individuals. To create an induced population with the use of physical/chemical mutagens is the first prerequisite for TILLING approach. Most of the plant species are compatible with this technique due to their self-fertilized nature and the seeds produced by these plants can be stored for long periods of time (Borevitz et al., 2003). The seeds are treated with mutagens and raised to harvest M1 plants, which are consequently, self-fertilized to raise the M2 population. DNA extracted from M2 plants is used in mutational screening (Colbert et al., 2001). To avoid mixing of the same mutation only one M2 plant from each M1 is used for DNA extraction (Till et al., 2007). The M3 seeds produce by selfing the M2 progeny can be well preserved for long term storage. Ethyl methane sulfonate (EMS) has been extensively used as a chemical mutagen in TILLING studies in plants to generate mutant populations, although other mutagens can be effective. EMS produces transitional mutations (G/C, A/T) by alkylating G residues which pairs with T instead of the conservative base pairing with C (Nagy et al., 2003). It is a constructive approach for users to attempt a range of chemical mutagens to assess the lethality and sterility on germinal tissue before creating large mutant populations.
Quantitative trait loci (QTL) analysis and its applications in plant breedingPGS
Abstract
Many agriculturally important traits such as grain yield, protein content and relative disease resistance are controlled by many genes and are known as quantitative traits (also polygenic or complex traits). A quantitative trait depends on the cumulative actions of many genes and the environment. The genomic regions that contain genes associated with a quantitative trait are known as quantitative trait loci (QTLs). Thus, a QTL could be defined as a genomic region responsible for a part of the observed phenotypic variation for a quantitative trait. A QTL can be a single gene or a cluster of linked genes that affect the trait. The effects of individual QTLs may differ from each other and change from environment to environment. The genetics of a quantitative trait can often be deduced from the statistical analysis of several segregating populations. Recently, by using molecular markers, it is feasible to analyze quantitative traits and identify individual QTLs or genes controlling the traits of interest in breeding programs.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Association mapping approaches for tagging quality traits in maizeSenthil Natesan
Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
Molecular Marker and It's ApplicationsSuresh Antre
Molecular (DNA) markers are segments of DNA that can be detected through specific laboratory techniques. With the advent of marker-assisted selection (MAS), a new breeding tool is now available to make more accurate and useful selections in breeding populations.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Quantitative trait loci (QTL) analysis and its applications in plant breedingPGS
Abstract
Many agriculturally important traits such as grain yield, protein content and relative disease resistance are controlled by many genes and are known as quantitative traits (also polygenic or complex traits). A quantitative trait depends on the cumulative actions of many genes and the environment. The genomic regions that contain genes associated with a quantitative trait are known as quantitative trait loci (QTLs). Thus, a QTL could be defined as a genomic region responsible for a part of the observed phenotypic variation for a quantitative trait. A QTL can be a single gene or a cluster of linked genes that affect the trait. The effects of individual QTLs may differ from each other and change from environment to environment. The genetics of a quantitative trait can often be deduced from the statistical analysis of several segregating populations. Recently, by using molecular markers, it is feasible to analyze quantitative traits and identify individual QTLs or genes controlling the traits of interest in breeding programs.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Association mapping approaches for tagging quality traits in maizeSenthil Natesan
Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
Molecular Marker and It's ApplicationsSuresh Antre
Molecular (DNA) markers are segments of DNA that can be detected through specific laboratory techniques. With the advent of marker-assisted selection (MAS), a new breeding tool is now available to make more accurate and useful selections in breeding populations.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Priorities of breeding approaches in bt cottons.dr. yanal alkuddsiDr. Yanal A. Alkuddsi
In few years of Bt era – over Six hundred of Bt cotton hybrids are released – Just Handful of them are popular
Ultimately it’s the genetic potentiality for productivity that determines success of a Bt genotype
Breeding efforts of improving genetic potentiality of Bt cottons assumes greater importance
Many times your dream landscape design doesn’t get completed due to unavailability or shortage of real plants. But, with the introduction of lifelike, and much more naturally appealing artificial outdoor plants by PermaLeaf®, it has made life easy for commercial space users and home-owners, to re-designs and reinstates a beautiful surrounding.
Super life on mars(amezing concept be pankaj)Punk Pankaj
if u can enjoy your life on earth then surely u will enjoy your super life on Mars. I gave the super concept and I am also going to discuss my this concept with NASA and ISRO that how we can enjoy our life on Mars, why Mars is better and all my friend just don't forget to smile hahah.
This slide deck uses case studies and scientific publications to highlight how Life Technologies platforms and products are used in plant genetic analysis applications such as plant genome sequencing, SNP genotyping, marker assisted selection, GMO detection, plant genetic engineering, plant gene expression, and plant nucleic acid isolation.
Life Technologies is committed to providing instruments, reagents, and technologies for Plant Sciences and Genomic Applications that will lead the way to remarkable agricultural discoveries—everything from improved crops that feed more people to sustainable biofuels that keep things moving.
Alzheimer’s disease (AD) is a devastating neurodegenerative disease that is genetically complex. Although great progress has been made in identifying fully penetrant mutations in genes that cause early-onset AD, these still represent a very small percentage of AD cases. Large-scale, genome-wide association studies (GWAS) have identified at least 20 additional genetic risk loci for the more common form: late-onset AD. However, the identified SNPs are typically not the actual risk variants, but are in linkage disequilibrium with the presumed causative variants [1].
To help identify causative genetic variants, we have combined highly accurate, long-read sequencing with hybrid-capture technology. In this collaborative webinar*, we present this method and show how combining IDT xGen® Lockdown® Probes with PacBio SMRT® Sequencing allows targeting and sequencing of candidate genes from genomic DNA and corresponding transcripts from cDNA. Using a panel of target capture probes for 35 AD candidate genes, we demonstrate the power of this approach by looking at data for two individuals with AD. Some additional benefits of this method include the ability to leverage long reads, phase heterozygous variants, and link corresponding transcript isoforms to their respective alleles.
Reference: 1. Van Cauwenberghe C, Van Broeckhoven C, Sleegers K. (2016) The genetic landscape of Alzheimer disease: clinical implications and perspectives. Genet Med, 18(5):421–430.
* This presentation represents a collaboration between Pacific Biosciences and Integrated DNA Technologies. The individual opinions expressed may not reflect shared opinions of Pacific Biosciences and Integrated DNA Technologies.
Back to Basics: Using GWAS to Drive Discovery for Complex DiseasesGolden Helix Inc
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for nearly 10 years. Despite some assertions to the contrary, GWAS is not dead. GWAS is alive and well, and remains a viable technology for genetic discovery.
This webcast will cover:
GWAS data formats, usability, and data management techniques.
Imputation: Myths, facts, and when to use it.
Quality assurance: What questions should you be asking about your data?
Genotype association testing and statistics: Contingency tables, linear and logistic regression, Mixed Linear Models, and more.
Visualizations including Manhattan Plots, linkage disequilibrium plots, and genomic annotation sources.
Exploring public databases to investigate your results.
Tips for using exome chips and other targeted genotyping platforms.
Along the way, Dr. Christensen will highlight best practice approaches and common pitfalls to avoid. Golden Helix SNP & Variation Suite (SVS) software will be used to demonstrate many of these concepts.
Targeted Induced Local Lesions IN Genome. Mutations (Single base pair substitution) are created by traditionally used chemical mutagens. Identify SNPs and / or INDELS in a gene / genes of interest from a mutagenized population.
Microhaplotype, A Powerful New Type of Genetic MarkerMojgan Talebian
Haplotyped SNPs allow more efficient inference of family relationships on a per locus basis because they constitute multiallelic loci, analogous to the STRs. Haplotypes are optimal type of forensically useful DNA marker, especially family or lineage inference.
A SNPs is a single nucleotide base difference in the DNA sequence. 10 million common SNPs are in the human genome, many of which are already annotated in SNP database. SNPS are Di-allelic and not highly polymorphism .
SNPs are so abundant throughout the genome that it is theoretically possible to type hundreds of them.
SNP’s sample processing and data analysis may be more fully automated because size-based separation is not required.
DNA profiling process, RFLP analysis, STR analysis by PCR, basic principle of dna fingerprinting, advantages and disadvantages of RFLP and STR analysis
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
3. 3
Genetic Variations
• The genetic variations in DNA sequences (e.g.,
insertions, deletions, and mutations) have a
major impact on genotypic and phenotypic
differences.
– All humans share 99% the same DNA sequence.
– The genetic variations in the coding region may
change the codon of an amino acid and alters the
amino acid sequence.
2/8/2017 PG seminar
4. Allelic variations within a genome of a same species-
1.Differences in the number of tandem repeats at a locus - SSRs
2.Segmental/nucleotide insertions/deletions - InDels
3.Single nucleotide polymorphisms - SNPs
Depending on detection method and throughput-
(1) Low-throughput, hybridization-based markers such as RFLPs
(2) Medium-throughput, PCR-based markers RAPD, AFLP, SSRs
(3) High-throughput (HTP) sequence-based markers: SNPs
2/8/2017 4PG seminar
5. ►A SNP is defined as a single base change in a DNA
sequence that occurs in a significant proportion (more than 1
percent) of a large population.
►SNPs are found in
coding and (mostly) noncoding regions.
►Occur with a very high frequency
about 1 in 1000 bases to 1 in 100 to 300 bases.
►The abundance of SNPs and the ease with which they can be
measured make these genetic variations significant.
►SNPs close to particular gene acts as a marker for that gene.
2/8/2017 5PG seminar
6. Single Nucleotide Polymorphism
• A Single Nucleotide Polymorphisms (SNP), pronounced “snip,”
is a genetic variation when a single nucleotide (i.e., A, T, C, or G)
is altered and kept through heredity.
– SNP: Single DNA base variation found >1%
– Mutation: Single DNA base variation found <1%
C T T A G C T T
C T T A G T T T
SNP
C T T A G C T T
C T T A G T T T
Mutation
94%
6%
99.9%
0.1%
2/8/2017 6PG seminar
7. Sequence Overlap SNP discovery
GTTTAAATAATACTGATCA
GTTTAAATAATACTGATCA
GTTTAAATAGTACTGATCA
GTTTAAATAGTACTGATCA
Genomic DNA mRNA
BAC library RRS Library
or Sampling
cDNA Library
EST OverlapShotgun OverlapBAC Overlap
SNP maps
►Sequence genomes of a
large number of individuals
►Compare the base sequences
to discover SNPs.
►Generate a single map of the
genome containing all
possible SNPs => SNP maps
2/8/2017 7PG seminar
8. What do we know?
• SNPs physically close to one another tend to be inherited
together
• Recombination breaks apart haplotypes and slowly erodes
correlation between neighboring alleles
• Since SNPs are bi-allelic, each SNP defines a partition on the
population sample.
2/8/2017 8PG seminar
9. Haplotype:
A haplotype is a group of genes in an organism that are
inherited together from a single parent.
In temrs of SNP-
A haplotype stands for a set of linked SNPs on the same
chromosome not easily separable by recombination
Within each block, recombination is rare due to tight linkage
and only very few haplotypes really occur
2/8/2017 9PG seminar
10. Haplotypes
• Haplotype: A set of closely linked genetic markers present on one
chromosome which tend to be inherited together (not easily
separable by recombination).
• A haplotype can be simply considered as a binary string since
each SNP is binary.
SNP1 SNP2 SNP3
-A C T T A G C T T-
-A A T T T G C T C-
-A C T T T G C T C-
Haplotype 2
Haplotype 3
C A T
A T C
C T CHaplotype 1
SNP1 SNP2 SNP3
2/8/2017 10PG seminar
11. PG seminar
Haplotype
• Multiple loci in the same chromosome that are
inherited together
• Usually a string of SNPs that are linked
alleles
locus
haplotypes
2/8/2017 11
13. Why Haplotypes
•Haplotypes are more powerful discriminators
between cases and controls in association studies
•Use of haplotypes in association studies reduces the
number of tests to be carried out.
•With haplotypes we can conduct evolutionary studies
•Haplotypes are necessary for linkage analysis
2/8/2017 13PG seminar
14. Genotypes
• The use of haplotype information has been limited because many
genomes are diploid.
– In large sequencing projects, genotypes instead of haplotypes are
collected due to cost consideration.
A
C
G
T
A T
SNP1 SNP2
C G
Haplotype data
SNP1 SNP2
Genotype data
A
C
G
T
SNP
1
SNP
2
A T
C
G
SNP
1
SNP
2
2/8/2017 14PG seminar
15. Problems of Genotypes
• Genotypes only tell us the alleles at each SNP
locus.
– But we don’t know the connection of alleles at
different SNP loci.
– There could be several possible haplotypes for the
same genotype.
A
C
G
T
SNP1 SNP2
Genotype data
or
A T
C G
SNP1 SNP2
A G
C T
SNP1 SNP2
A
C
G
T
SNP1 SNP2
We don’t know which
haplotype pair is real.2/8/2017 15PG seminar
17. PG seminar
Haplotype blocks
• Low recombination rate in the region
• Strong Linkage Disequillibrium
• Small number of SNPs in the block are enough to identify
common haplotypes; tag SNPs
2/8/2017 17
18. Block detection methods
• Four gamete test, Hudson and Kaplan,Genetics, 1985,
A segment of SNPs is a block if between every pair (aA and bB) of SNPs
at most 3 gametes (ab, aB, Ab, AB) are observed.
• P-Value test
– A segment of SNPs is a block if for 95% of the pairs of SNPs
we can reject the hypothesis (with P-value 0.05 or 0.001)
that they are in linkage equilibrium.
• LD-based, Gabriel et al. Science,2002,296:2225-9
2/8/2017 18PG seminar
19. Research Directions of SNPs and
Haplotypes in Recent Years
Haplotype
Inference
Tag SNP
Selection
Maximum
Parsimony
Perfect
Phylogeny
Statistical
Methods
Haplotype
block
LD bin
Prediction
Accuracy
SNP
Database
2/8/2017 19PG seminar
20. Haplotype Blocks and Tag SNPs
• Recent studies have shown that the chromosome can be
partitioned into haplotype blocks interspersed by recombination
hotspots (Daly et al, Patil et al., 2011).
– Within a haplotype block, there is little or no recombination.
– The SNPs within a haplotype block tend to be inherited
together.
• Within a haplotype block, a small subset of SNPs (called tag SNPs)
is sufficient to distinguish each pair of haplotype patterns in the
block.
– We only need to genotype tag SNPs instead of all SNPs within
a haplotype block.
2/8/2017 20PG seminar
21. Recombination Hotspots and Haplotype
Blocks
Recombination
hotspots
Chromosome
Haplotype
blocks
P1 P2 P3 P4
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
SNP
loci
Haplotype patterns
: Major allele
: Minor allele
2/8/2017 21PG seminar
22. Three Problems
1. Estimation of frequency of all possible
haplotypes
2. Reconstruction of haplotype for individuals
3. Detection of all possible haplotypes in a
population
2/8/2017 22PG seminar
23. PG seminar
...Haplotype construction
• Family-based haplotype construction
– Linkage analysis softwares: Simwalk, Merlin,
Genehunter, Allegro...
• Population-based haplotype construction
– Not as reliable as family-based
2/8/2017 23
24. Haplotype reconstruction for individuals
C
A
T G A
A
T
C A
T
haplotype h(h1, h2)
possible associations of alleles to
chromosome
C T A
T G ACp
Cm
This is a mixture modeling problem!
ATGC
sequencing
Heterozygous
diploid individual
TC TG AA
Genotype
pairs of alleles with association of
alleles to chromosomes unknown
G
T
2/8/2017 24PG seminar
25. Haplotype Inference
• The problem of inferring the haplotypes from a
set of genotypes is called haplotype inference.
• Most combinatorial methods consider the
maximum parsimony model to solve this
problem.
– This model assumes that the real haplotypes in
natural population is rare.
– The solution of this problem is a minimum set of
haplotypes that can explain the given genotypes.
2/8/2017 25PG seminar
26. Maximum Parsimony
A Gh3
C Th4
A Th1
C Gh2
A Th1
A Th1
orG1
A
C
SNP1 SNP2
G
T
G2
A
A
SNP1 SNP2
T
T
A G
C T
A T
A T
C G
• Find a minimum set of
haplotypes to explain the
given genotypes.
2/8/2017 26PG seminar
27. Haplotype analysis algorithms
• Given a random sample of multilocus genotypes at a set of SNPs
the following actions can be taken:
– Estimate the frequencies of all possible haplotypes.
– Infer the haplotypes of all individuals.
• Haplotyping Algorithms:
– Clark algorithm
– EM algorithm
• Haplotyping programs:
– HAPINFEREX ( Clark Parsimony algorthm)
– EM-Decoder ( EM algorithm)
– PHASE ( Gibbs Sampler)
– HAPLOTYPER
2/8/2017 27PG seminar
28. Comparison between algorithms
• Clark
– Intuitive
– Fast
• EM
– Complete solution
– Slightly more
accurate than Clark
– Robust to
ambiguity
• PHASE
– Complete solution
– Slightly more accurate
than EM
– Slow version
• Haplotyper (Ligation)
– Fast
– Better than Clark
– Less accurate than EM
or PHASE
2/8/2017 28PG seminar
29. Factors affecting
• SNP allele frequency distribution
• Haplotype allele numbers
• Linkage disequilibrium (LD)
• Difference in power
• Overlap in results of marker types
2/8/2017 29PG seminar
30. Benefits of haplotypes instead of
individual SNPs
• Information content is higher
• Gene function may depend on more than one SNP
• Smaller number of required markers
– The amount of wrong positive association is reduced
• Replacing of missing genotypes by computational methods
• Elimination of genotyping errors
• Challenges:
– Haplotypes are difficult to define directly in the lab; computational
methods
– Defining of block boarders is ambiguous; several different
algorithms
2/8/2017 30PG seminar
31. Haplotype v/s SNP
1. When large number
of SNPs in the genome
(Hamblin and Jannink, 2011)
2. When less number
of SNPs in the genome
2/8/2017 31PG seminar
32. HAPLOTYPE CORRELATION WITH PHENOTYPE
Association of haplotype frequencies with the presence of
desired phenotypic frequencies in the population will help in
utilizing the maximum potential of SNP as a marker.
The “Haplotype centric” approach combines the information
of adjacent SNPs into composite multilocus haplotypes.
Haplotypes are not only more informative but also capture
the regional LD information, which is assumed to be robust and
powerful
2/8/2017 32PG seminar
34. Case study: 1
Aim :
1. To resequence the pepper gnome and to systematically
assess the diversity with capsicum sp.
2. Develop a complete HapMap using SNP
3. Annotating the identified SNPs to the genes
2/8/2017 34PG seminar
35. lines with different chile and bell pepper phenotypes
DNA extraction
Sequencing using illumina HiSeq2000
SNP calling using infinium array technique
Development of PepperSNP16K array
Genotyping with the array
Cluster map developed
2/8/2017 35PG seminar
38. Utility of the study:
Conclusion for the case study:
2/8/2017 38PG seminar
39. Case study: 2
Aim:
• To capture untapped novel diversity in the Brassica sp.
•To introduce the new concept Heterotic Haplotype
Capture (HHC)
2/8/2017 39PG seminar
41. Mixing up the gene pool by de novo allopolyploidisation
• Generated synthetic B. napus derived from de novo interspecific
hybridisation
• de novo synthesis of synthetic B. napus increases recombination
Intergenomic chromosomal rearrangements as a driver for heterosis
• identified large numbers of homoeologous chromosome exchanges by
using SNP haplotypes
• large- scale deletions, duplications, and copy-number variation
• Structural chromosome variants can also have a significant influence on
heterotic potential within and between heterotic pools
2/8/2017 41PG seminar
43. Output of study:
1. Genome-scale data available for the NAM and HHC
populations enable the identification (in any given NAM line)
of haplotype blocks that are predicted to be heterozygous in
combination with a genotyped maternal tester.
2. HHC-like approaches benefit genomic prediction based plant
breeding
3. Availability of immortal heterotic populations, provides a
powerful resource for genome-scale investigations into the
genetic basis of heterosis for yield and other important
agronomic traits.
2/8/2017 43PG seminar
44. Vinay Kumar et al. Plant Biotechnology Journal (2016), pp. 1–9
Other few examples :
2/8/2017 44PG seminar