1) Trait-associated SNPs provide insights into the genetic basis of heterosis or hybrid vigor in maize. GWAS identified over 1,000 associations between SNPs and seven yield-related traits.
2) Including dominance effects in models explains more of the observed heterosis and genetic variation than additive effects alone. The ratio of SNPs exhibiting positive versus negative dominance is correlated with heterosis for a given trait.
3) Field-based phenotyping using sensors on robots and UAVs can study dynamic traits influenced by environment and GxE interactions, overcoming limitations of endpoint traits in controlled conditions. This will improve predictive models for plant breeding and variety recommendations.
Development of First Multiparent Advanced Generation Inter-cross (MAGIC) Popu...ICRISAT
Pigeonpea is the sixth most important legume crop in the world and it is a rich source of proteins. Conventional methods of breeding varieties with higher yield and inbuilt resistance are time consuming and cumbersome process. Molecular breeding with the help of genome wide sequence information will be helpful in achieving the goal in less time with high precision.
24 June 2015
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Topic- Genetic Variability and Stability Analysis in Greengram [Vigna radiata (L.) Wilczek]
PRESENTED
BY
CHIRANJEEV
Id. No. – 4213, M. Sc. (Ag.)
In the presence of External examiner and Members of Advisory Committee
Venue: Seminar class room
On date: 27/10/2020
DEPARTMENT OF GENETICS AND PLANT BREEDING
SARDAR VALLABHBHAI PATEL UNIVERSITY OF AGRICULTURE AND TECHNOLOGY MEERUT-250110 (U.P.) India
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
Comparative genomic analysis in Zingiberales: what can we learn from banana to enable Ensete and Boesenbergia to reach their potential?
Talk for Plant and Animal Genomics XXV 25 - San Diego January 2017
Trude Schwarzacher, Jennifer A. Harikrishna and Pat Heslop-Harrison, University of Leicester and University of Malaya
phh(a)molcyt.com
Within the Zingiberales there are many orphan crops that are grown in Africa and Asia where recently started genomic efforts will have an impact for the future understanding and breeding of these crops. Advanced genomics and genome knowledge of the taxonomically closely related genus Musa will help identify genes and their function. We will discuss relevant recent work with Musa and results from DNA sequencing, examinations of diversity and studies of genome structure, gene expression and epigenetic control in Boesenbergia and ensete. Ensete is an important starch staple food in Ethiopia. It is harvested just as the monocarpic plant starts to flower, a few years after planting, and the stored starch extracted from the pseudo-stem and corm. A genome sequence has been published, but there is little genomics. Characterization of the diversity in the species and understanding of the differences to Musa will enable selection and breeding for crop improvement to meet the requirements of increasing populations, climate change and environmental sustainability. Boesenbergia rotunda is widely used in traditional medicine in Asia and has been shown to produce secondary metabolites with antiviral activity. For high throughput propagation and metabolite production in vitro culture is employed; embryogenic calli of B. rotunda in vitro are able to regenerate into plants but lose this ability after prolonged periods in cell suspension media. Epigenetic factors, including histone modifications and DNA methylation are likely to play crucial roles in the regulation of genes involved in totipotency and plant regeneration. These findings are also relevant to other crops within the Zingiberales. Further details will be given at www.molcyt.com
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)
Ponent: Francesc Piferrer (ICM - CSIC)
Abstract: La proporció de sexes és un paràmetre fonamental en la demografia de les poblacions. Es presenta el coneixement que actualment es té sobre els mecanismes moleculars que la determinen i com en molts casos hi ha una participació combinada d’elements genètics i factors ambientals. La epigenètica integra la informació genòmica amb la ambiental i és la base de la plasticitat fenotípica Es repassen breument els principals mecanismes epigenètics i diferents mètodes per a avaluar canvis en la metilació del DNA. Seguidament, es presenten exemples de com la epigenètica pot contribuir en la recerca en ecologia i, de passada, en la producció animal. Per acabar, mostrarem alguns exemples de recerca en epigenòmica en poblacions naturals de les Illes Medes, de com petites variacions en les condicions ambientals al principi de la vida tenen conseqüències a llarg termini, i discutirem breument aspectes adaptatius en un context de canvi global.
Development of First Multiparent Advanced Generation Inter-cross (MAGIC) Popu...ICRISAT
Pigeonpea is the sixth most important legume crop in the world and it is a rich source of proteins. Conventional methods of breeding varieties with higher yield and inbuilt resistance are time consuming and cumbersome process. Molecular breeding with the help of genome wide sequence information will be helpful in achieving the goal in less time with high precision.
24 June 2015
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Topic- Genetic Variability and Stability Analysis in Greengram [Vigna radiata (L.) Wilczek]
PRESENTED
BY
CHIRANJEEV
Id. No. – 4213, M. Sc. (Ag.)
In the presence of External examiner and Members of Advisory Committee
Venue: Seminar class room
On date: 27/10/2020
DEPARTMENT OF GENETICS AND PLANT BREEDING
SARDAR VALLABHBHAI PATEL UNIVERSITY OF AGRICULTURE AND TECHNOLOGY MEERUT-250110 (U.P.) India
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
Comparative genomic analysis in Zingiberales: what can we learn from banana to enable Ensete and Boesenbergia to reach their potential?
Talk for Plant and Animal Genomics XXV 25 - San Diego January 2017
Trude Schwarzacher, Jennifer A. Harikrishna and Pat Heslop-Harrison, University of Leicester and University of Malaya
phh(a)molcyt.com
Within the Zingiberales there are many orphan crops that are grown in Africa and Asia where recently started genomic efforts will have an impact for the future understanding and breeding of these crops. Advanced genomics and genome knowledge of the taxonomically closely related genus Musa will help identify genes and their function. We will discuss relevant recent work with Musa and results from DNA sequencing, examinations of diversity and studies of genome structure, gene expression and epigenetic control in Boesenbergia and ensete. Ensete is an important starch staple food in Ethiopia. It is harvested just as the monocarpic plant starts to flower, a few years after planting, and the stored starch extracted from the pseudo-stem and corm. A genome sequence has been published, but there is little genomics. Characterization of the diversity in the species and understanding of the differences to Musa will enable selection and breeding for crop improvement to meet the requirements of increasing populations, climate change and environmental sustainability. Boesenbergia rotunda is widely used in traditional medicine in Asia and has been shown to produce secondary metabolites with antiviral activity. For high throughput propagation and metabolite production in vitro culture is employed; embryogenic calli of B. rotunda in vitro are able to regenerate into plants but lose this ability after prolonged periods in cell suspension media. Epigenetic factors, including histone modifications and DNA methylation are likely to play crucial roles in the regulation of genes involved in totipotency and plant regeneration. These findings are also relevant to other crops within the Zingiberales. Further details will be given at www.molcyt.com
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)
Ponent: Francesc Piferrer (ICM - CSIC)
Abstract: La proporció de sexes és un paràmetre fonamental en la demografia de les poblacions. Es presenta el coneixement que actualment es té sobre els mecanismes moleculars que la determinen i com en molts casos hi ha una participació combinada d’elements genètics i factors ambientals. La epigenètica integra la informació genòmica amb la ambiental i és la base de la plasticitat fenotípica Es repassen breument els principals mecanismes epigenètics i diferents mètodes per a avaluar canvis en la metilació del DNA. Seguidament, es presenten exemples de com la epigenètica pot contribuir en la recerca en ecologia i, de passada, en la producció animal. Per acabar, mostrarem alguns exemples de recerca en epigenòmica en poblacions naturals de les Illes Medes, de com petites variacions en les condicions ambientals al principi de la vida tenen conseqüències a llarg termini, i discutirem breument aspectes adaptatius en un context de canvi global.
Dr. Andres Perez - PRRS Epidemiology: Best Principles of Control at a Regiona...John Blue
PRRS Epidemiology: Best Principles of Control at a Regional Level - Dr. Andres Perez, University of Minnesota, from the 2015 North American PRRS Symposium, December 4 - 5, 2015, Chicago, IL, USA.
More presentations at http://www.swinecast.com/2015-north-american-prrs-symposium
Nature GeNetics VOLUME 46 NUMBER 10 OCTOBER 2014 1 0 8 9.docxgemaherd
Nature GeNetics VOLUME 46 | NUMBER 10 | OCTOBER 2014 1 0 8 9
A suite of forces and factors, including mutation, recombination,
selection, population history and gene duplication influence patterns
of intraspecific genetic variation. Distinguishing which factors have
shaped sequence variation across a genome requires extensive whole-
genome sequencing of multiple individuals, which has only recently
become tractable1. Most large-scale whole-genome resequencing
studies have focused on model and domesticated species1–5. However,
extensive sequencing of natural populations holds great promise for
advancing understanding of evolutionary biology, including identify-
ing functional variation and the molecular bases of adaptation. Recent
work in a number of species has identified genomic regions that show
signatures of positive selection, suggesting that such regions contain
loci that control adaptive traits4,6–8. Relatively few studies, however,
have combined genome-wide scans with phenotypic data to determine
whether computationally identified selected regions influence adap-
tive phenotypic variation5,9–13. Genome-wide studies of large natural
populations combined with phenotypic measurements are necessary
to determine which factors shape patterns of genetic variation within
species and, therefore, enhance understanding of adaptation.
With large geographic ranges spanning wide environmental gradi-
ents and a long history of research showing local adaptation14, forest
trees are ideal for examining the processes shaping genetic variation
in natural populations. Forest trees cover approximately 30% of ter-
restrial land area15, provide direct feedback to global climate15 and
are often foundation species that organize entire biotic communities
and biogeochemical systems16,17. Clearly, biotic and abiotic interac-
tions have influenced population sizes and distributions of forest
trees, leaving diagnostic signatures in the genomes of present-day
populations14,18,19. A deeper understanding of the evolutionary and
ecological forces that shaped these patterns will offer insights and
options for ecosystem management, applied tree improvement and
accelerated domestication efforts20.
Black cottonwood, Populus trichocarpa Torr. & Gray, is a dominant
riparian tree that has become a model for the advancement of genome-
level insights in forest trees21. The sequencing of 16 P. trichocarpa
genomes revealed widespread patterns of linkage disequilibrium (LD)
and population structure22 and extensive genecological studies have
revealed a high degree of adaptive phenotypic variation in growth,
vegetative phenology and physiological traits such as water-use effi-
ciency and photosynthesis23–25, suggesting that local adaptation is
prevalent. To date, candidate gene–association analyses have revealed
loci with significant effects on phenotypic traits26,27. However, thus
far there have been no publications describing whole-genome asso-
.
Nature GeNetics VOLUME 46 NUMBER 10 OCTOBER 2014 1 0 8 9.docxvannagoforth
Nature GeNetics VOLUME 46 | NUMBER 10 | OCTOBER 2014 1 0 8 9
A suite of forces and factors, including mutation, recombination,
selection, population history and gene duplication influence patterns
of intraspecific genetic variation. Distinguishing which factors have
shaped sequence variation across a genome requires extensive whole-
genome sequencing of multiple individuals, which has only recently
become tractable1. Most large-scale whole-genome resequencing
studies have focused on model and domesticated species1–5. However,
extensive sequencing of natural populations holds great promise for
advancing understanding of evolutionary biology, including identify-
ing functional variation and the molecular bases of adaptation. Recent
work in a number of species has identified genomic regions that show
signatures of positive selection, suggesting that such regions contain
loci that control adaptive traits4,6–8. Relatively few studies, however,
have combined genome-wide scans with phenotypic data to determine
whether computationally identified selected regions influence adap-
tive phenotypic variation5,9–13. Genome-wide studies of large natural
populations combined with phenotypic measurements are necessary
to determine which factors shape patterns of genetic variation within
species and, therefore, enhance understanding of adaptation.
With large geographic ranges spanning wide environmental gradi-
ents and a long history of research showing local adaptation14, forest
trees are ideal for examining the processes shaping genetic variation
in natural populations. Forest trees cover approximately 30% of ter-
restrial land area15, provide direct feedback to global climate15 and
are often foundation species that organize entire biotic communities
and biogeochemical systems16,17. Clearly, biotic and abiotic interac-
tions have influenced population sizes and distributions of forest
trees, leaving diagnostic signatures in the genomes of present-day
populations14,18,19. A deeper understanding of the evolutionary and
ecological forces that shaped these patterns will offer insights and
options for ecosystem management, applied tree improvement and
accelerated domestication efforts20.
Black cottonwood, Populus trichocarpa Torr. & Gray, is a dominant
riparian tree that has become a model for the advancement of genome-
level insights in forest trees21. The sequencing of 16 P. trichocarpa
genomes revealed widespread patterns of linkage disequilibrium (LD)
and population structure22 and extensive genecological studies have
revealed a high degree of adaptive phenotypic variation in growth,
vegetative phenology and physiological traits such as water-use effi-
ciency and photosynthesis23–25, suggesting that local adaptation is
prevalent. To date, candidate gene–association analyses have revealed
loci with significant effects on phenotypic traits26,27. However, thus
far there have been no publications describing whole-genome asso-
...
ASHG 2015 - Redundant Annotations in Tertiary AnalysisJames Warren
After obtaining genetic variants from next generation sequencing data, a precursory step in tertiary analysis is to annotate each variant with available relevant information. There is no standardized compendium for this purpose; researchers instead are required to compile data from a motley of annotation tools and public datasets. These sources for annotation are independently maintained, and accordingly there is limited concordance between their reported contents. The choice of annotation datasets thus has a direct and significant impact on the results of the analysis.
In this presentation, we will delve into the principles of QTL mapping and explore various strategies for mapping QTLs in plants. We will also discuss the advantages and limitations, and provide insights into how QTL mapping is advancing our understanding of genetics.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
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.
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.
2015. Patrik Schnable. Trait associated SNPs provide insights into heterosis in maize
1. Trait-associated SNPs provide insights
into heterosis in maize
Patrick S. Schnable
Iowa State University
China Agriculture University
Data2Bio, LLC
ICRISAT
19 February 2015
2. How to Translate Genomic Data into
Biological Understanding and Crop
Improvement?
2
B73 Reference Genome NGS data in NCBI SRA (Feb. 2014)
zmHapMap1
zmHapMap2
CAU resequencing
ISU Zeanome (RNA-seq)
Ames Diversity Panel
IBM RILs RNA-seq
CAAS resequencing
And many others
Schnable, Ware et al., Science, 2009
Tera(1012)Bases
$32M
3. Associate Genes (or genetic markers)
with Traits
• Which of the ~50,000 maize genes control
important traits?
• GWAS (Genome-wide association studies)
– Typically conducted on diversity panels
– By exploiting historical recombination events they
yield higher resolution associations than QTL
studies
– Identifies associations between genetic markers
(e.g., SNPs) and traits
• Forward and reverse genetics
4. Y: phenotypic trait;
Pi: Fix effect of cross type population (N=4);
Sl: Fix effect of sub-population (N=25).
Approaches for GWAS
4
Y = u+ biPi
i=1
4
å + alSl
l=1
25
å + dSNP +e
• Single-marker GWAS approach
– SNP effects tested one at a time
– Using PLINK command line tool
• Stepwise regression approach
– SNPs fitted in a step-wise manner
– Using GenSel4 Stepwise (alpha=0.05,MaxMarkers=300)
• Bayesian-based approach
– SNPs fitted simultaneously into a model
– Using GenSel4 BayesC (chainLength=41,000, burnin=1,000)
5. GWAS for Yield-Related Traits
Kernel Count
Total Kernel Weight
Avg. Kernel Weight
Cob
Length
Cob Diameter
Cob Weight
Kernel Row Number
Jinliang Yang
(杨金良)
Jeff Ross-Ibarra Lab,
UC Davis
6. Yu, J. et al. Genetics 2008;178:539-551
Nested Association Mapping (NAM) Population
6
Four related populations (N=7,000
lines):
• NAM RILs (N=5,000 lines) + IBM
RILs (N=300 lines)
• Subset of MxRILs (N=300 lines;
IBM + NAM)
• Subset of BxRILs (N=800 lines;
IBM + NAM)
• NAM Partial Diallel (N=250 lines)
7. High Density Genotypic Data
• SNPs from three sources:
– Maize HapMap1* (1.6M)
– Maize HapMap2* (18.4M)
– Our RNA-seq SNPs from
5 tissues (4.9M)
7
# Concordance among overlapping variant sites
HapMap1
0.7 M
HapMap2
16.6 M
0.4 M
98.7%
1.2 M
96.6%
0.3 M
96.9%
0.2 M
RNA-seq
3.2 M
##
#
Imputation or
Projection
NAM RILs
BxNAM RILs
MxNAM RILs
NAM Diallels
*Gore, M.A., et. al.,
Science, 2009;
Chia, H-M, et. al., Nature
Genetics, 2012.
Merging and Filtering
Minor Allele Freq. (MAF) >= 0.1
SNP Missing Rate < 0.6
Merged SNP set
13.0M
8. Phenotypic distributions
8
• CD=Cob Diameter, AKW=Avg. Kernel Weight, CL=Cob Length, CW=Cob
Weight, KC=Kernel Count, TKW=Total Kernel Weight
Based on ~100k observations/trait from 9 locations; ~20% our data and 80%
from: Brown, P. J., et. al., PLoS Genetics, 2011
9. Different GWAS Approaches are
Complementary
9
40/77 (52%) KAVs, representing 39 chromosomal bins
(bin size =100kb), have been cross-validated.
Genotyped TAS
Cross-validated TAS
Single-variant GWAS (-log10(P-Value))
Bayesian-basedGWAS(ModelFreq)
Bayesian-based
and single-variant
N=16/21(76%)
Bayesian-based
N=9/26(35%)
Single-variant
N=10/15(67%)
Stepwise
regression
N=6/14(43%)
13. Missing heritability
Inclusion of dominant gene action
improves predictions
13
Percentage of HPH
heritability
Four GWAS populations Only Diallel population
Additive Dominance General
heritability
Percentage of HPH
Missing heritability
14. Classical Models for Heterosis
Over-dominance
x
AA bb aa BB Aa Bb
Complementation
Zamir
Additive or dominant gene action Over-dominant gene action
15. Degree of Dominance for TASs
15
Degree of dominance (h), where d denotes dominant
effect and a denotes additive effect.
h =
d
a
A A B BBA
a
d
positive
dominance
h > 0.5
negative
dominance
h < -0.5
additive
-0.5 <= h <= 0.5
16. Trait Associated SNP Effects
16
*Dominance includes true dominance, over-dominance and pseudo-overdominance
17. Phenotype (P) = Genotype
(G) + Environment (E) +
GxE
• Genotype: NGS revolution and GBS
• Environment: weather, soil type, water,
nutrients, disease pressure, agronomic
practices etc.
• GxE interactions complicate phenotypic
predictions, but offer fascinating avenues
of investigation
17
L
SL
L L
SL
S
S
L
S
SL S
SSL
L
L
SL
S
SL
SS
L
S
SS
SL SL
S
The Drought Monitor focuses on broad-
scale conditions. Local conditions may
vary. See accompanying text summary for
forecast statements.S
L
U.S. Drought Monitor October 1, 2013
Valid 7 a.m. EDT
(Released Thursday, Oct. 3, 2013)
Intensity:
D0 Abnormally Dry
D1 Moderate Drought
D2 Severe Drought
D3 Extreme Drought
D4 Exceptional Drought
Author:
David Miskus
Drought Impact Types:
S = Short-Term, typically less than
6 months (e.g. agriculture, grasslands)
L = Long-Term, typically greater than
6 months (e.g. hydrology, ecology)
Delineates dominant impacts
NOAA/NWS/NCEP/CPC
18. E and GxE complicate
phenotypic predictions
• Strategies for dealing with “E” and “GxE”
– Study traits that are stable across E
– Conduct studies in controlled environments,
taking E and GxE out of the equation
– Control for and study the effects of E and GxE
statistically…embrace the opportunity to gain
a deeper understanding of the underlying
biology
18
28. Phenotype (P) = Genotype (G) + Environment (E) + GxE
Predictive Models Will:
• Improve the accuracy of selection in plant breeding
programs, thereby increasing the rate of genetic
gain per year
• Enhance our ability to efficiently breed crops to
withstand the increased weather variability
associated with global climate change
• Improved ability to provide farmers with evidence-
based recommendations for the appropriate
varieties to plant in a given field, under a particular
management practice in a given year, leading to
greater farmer profits and enhanced yield stability
28
29. Summary
• DNA sequence variation (SNP) can explain 40-70% of
genetic variation (considering only additive gene action)
or 80-90% (including dominant gene action)
• Dominant effects explain much of the missing heritability
• Ratio of loci exhibiting positive dominant gene action to
those exhibit negative dominant gene action is correlated
with the degree of heterosis for that trait
• Determining which loci confer positive and negative
heterosis for specific traits may increase our ability to
predict hybrid performance
• Phenomics is a bottleneck in GWAS, GS and breeding
• Field-based sensors will allow us to study the genetics of
dynamic traits rather than being limited to end-point traits
30. PSS has IP and equity interests
in Data2Bio LLC
31. Data2Bio, LLC
31
•Founded in 2010, Data2Bio designs,
executes, analyzes and interprets
research projects involving next
generation sequencing
•Core strengths are experimental
design, genomics, bioinformatics, and
breeding support
•Academic and private-sector
customers on all continents except
Antarctica
•Proprietary genomic technologies
associated with DNA barcoding and
genotyping-by-sequencing (tGBS™),
as well as proprietary bioinformatic
pipelines