The document discusses several evolutionary forces that have shaped human genetic variation, including migration patterns of early humans, positive selection, background selection, and genetic drift due to population bottlenecks. It provides evidence that a significant fraction of amino acid substitutions in humans were driven by positive selection, especially for genes related to smell and response to pathogens. Background selection has also contributed substantially to the reduction of human genetic diversity due to the removal of weakly deleterious mutations over generations. The strength of background selection correlates with increased genetic differentiation between modern human populations.
If you are looking for old age homes in hyderabad then ppreddyoldagehome who take care of aged people and committed to extend much needed love and happiness to elders.
Join the MIM Team and the Science Pathfinders Program for the Health and Science Expo: Empowerment and Education on Saturday, December 13, 2014 from 8am-3pm for students from elementary school through college, parents and educators. We need your help to sponsor children to attend the conference. Thank you in advance!
If you are looking for old age homes in hyderabad then ppreddyoldagehome who take care of aged people and committed to extend much needed love and happiness to elders.
Join the MIM Team and the Science Pathfinders Program for the Health and Science Expo: Empowerment and Education on Saturday, December 13, 2014 from 8am-3pm for students from elementary school through college, parents and educators. We need your help to sponsor children to attend the conference. Thank you in advance!
Dream of Detroit Community Meeting Presentationdreamofdetroit
On Sunday, June 22, members of the metro Detroit Muslim community met at the Muslim Center Mosque and Community Center to talk about Dream of Detroit--a vision for revitalizing a westside neighborhood. This presentation was given to introduce attendees to the project.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Dream of Detroit Community Meeting Presentationdreamofdetroit
On Sunday, June 22, members of the metro Detroit Muslim community met at the Muslim Center Mosque and Community Center to talk about Dream of Detroit--a vision for revitalizing a westside neighborhood. This presentation was given to introduce attendees to the project.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Richard's aventures in two entangled wonderlandsRichard 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.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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.
7. “Selective Sweep”
• Repeated fixation of functional mutations in coding regions over
evolutionary timescales can lead to a disproportional number of
amino acid substitutions relative to observed polymorphisms.
• This can be summarized by a 2x2 table and analyzed using the
McDonald-Kreitman test:
Non-Syn Syn
Fixed F F
Polymorphic P P1000 Genomes Project Data
Adaptive
Neutral
Nearly Neutral
Mildly Deleterious
Fairly Deleterious
Strongly Deleterious
Putatively neutral diversity levels
The Effect of “Positive Selection”
8. SnIPRE: an improvement to MKT
Since few SNPs and
substitutions are usually
observed per gene, MKT
can be noisy. Pooling
observations across the
genome using a mixed
effects model vastly
increases power.
Eilertson et al, 2012
9. SnIPREASR in 1000 Genomes Project
Human-chimp divergence
Pos Sel Conserved
410 8027
• Conserved genes are either neutral or under
purifying selection.
10. SnIPREASR: an improvement to SnIPRE
• Alignments are generated using MOSAIC, a
program we developed that rigorously integrates
putative orthologs from an arbitrary number of
sources.!
!
• Using PAML, we perform AIC-based model
selection to infer the substitutions along the
human lineage since our divergence with chimp. pythonhosted.org/bio-‐MOSAIC/
Maher & Hernandez (arXiv)
Hum
an
Chim
pO
rangG
orilla
…
Cyrus Maher
11. SnIPREASR works well for positive selection
• Simulations: Human-specific substitutions; Gutenkunst et al.
demographic model.
• 𝛾 is the population scaled selection coefficient.
• SnIPREASR is best-powered to estimate values of 𝛾>0.
Hum
an
Chim
pO
rangG
orilla
…
12. ASR removes genes positively selected in chimp
Human-chimp divergence
Pos Sel Conserved
Human only
(ASR)
Pos Sel 343 0 343
Conserved 67 8027 8094
410 8027
• Conserved genes are either neutral or under
purifying selection.
• 67/410 (16%) of genes identified as positively
selected when comparing human-chimp are
conserved along the human lineage.
14. Amino acid
substitution
Neutral
diversity
levels …
Reflects the fraction
of amino acid
substitutions that are
adaptive
n substitutions
…
Reflects the typical
strength of selection
The footprint of adaptive amino acid substitutions
• Goal: compare the pattern around amino acid substitutions to
the pattern around synonymous substitutions.
Hernandez et al. Science (2011)
15. Observed Patterns of Diversity
Around Human Substitutions
Hernandez et al. Science (2011)
16. Genetic diversity
reduced: π=f0π0
(decrease in effective
population size [Ne])
Adaptive
Neutral
Nearly Neutral
Mildly Deleterious
Fairly Deleterious
Strongly Deleterious
Putatively neutral diversity levels
The Effect of Negative Selection
17. Genetic diversity
reduced: π=f0π0
(decrease in effective
population size [Ne])
Adaptive
Neutral
Nearly Neutral
Mildly Deleterious
Fairly Deleterious
Strongly Deleterious
Putatively neutral diversity levels
The Effect of Negative Selection
18. Putatively neutral diversity levels
The Effect of Negative Selection
Genetic diversity
reduced: π=f0π0
(decrease in effective
population size [Ne])
Adaptive
Neutral
Nearly Neutral
Mildly Deleterious
Fairly Deleterious
Strongly Deleterious
20. BGS correlates with Fst at neutral sites
4 - Population Differentiation as a Function of BGS!
The decrease in Ne locally across the genome as a result of BGS (inferred2 by the value, B, in which lower
values indicate stronger BGS) may impact the rate of genetic drift at specific loci. To investigate this
effect, we measured FST between TGP populations as a function of BGS strength. Our results suggest that
the strength of BGS is a predictor of population differentiation, with an increase in genetic drift driving
this effect.
5 - Forward Simulations of Demography and BGS!
Using a distribution of fitness effects and a demographic model inferred from previous studies3,4, we ran
forward simulations using SFS_CODE5 to estimate the effect of human demography on determining the
reduction in genetic diversity caused by BGS, observing that the effects of BGS are strongest for those
populations that have experienced sharp population bottlenecks (i.e., Europeans and Asians). However, the
expected reduction in diversity due to BGS across all human populations is still greater than for a
simulated population of constant size, illustrating the importance of population expansions for determining
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Fst (estimator method) vs. Background Selection
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Fst (estimator method) vs. Background Selection
European vs. Asian
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
B value!
FST!
BGS strength!
populationdifferentiation!
FST!
FST!
B value! B value!
African vs. Asian! African vs. European! European vs. Asian!
0.100.120.140.160.180.200.22
0.100.120.140.160.180.200.22
0.100.120.140.160.180.200.22
0-24 225-249 475-499 725-749 975-979 0-24 225-249 475-499 725-749 975-979 0-24 225-249 475-499 725-749 975-979
B value B value
4 - Population Differentiation as a Function of BGS!
The decrease in Ne locally across the genome as a result of BGS (inferred2 by the value, B, in which lower
values indicate stronger BGS) may impact the rate of genetic drift at specific loci. To investigate this
effect, we measured FST between TGP populations as a function of BGS strength. Our results suggest tha
the strength of BGS is a predictor of population differentiation, with an increase in genetic drift driving
this effect.
5 - Forward Simulations of Demography and BGS!
Using a distribution of fitness effects and a demographic model inferred from previous studies3,4, we ran
forward simulations using SFS_CODE5 to estimate the effect of human demography on determining the
reduction in genetic diversity caused by BGS, observing that the effects of BGS are strongest for those
populations that have experienced sharp population bottlenecks (i.e., Europeans and Asians). However, the
expected reduction in diversity due to BGS across all human populations is still greater than for a
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Fst (estimator method) vs. Background Selection
African vs. Asian
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
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Fst (estimator method) vs. Background Selection
African vs. European
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
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Fst (estimator method) vs. Background Selection
European vs. Asian
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
B value!
FST!
BGS strength!
populationdifferentiation!
FST!
FST!
B value! B value!
African vs. Asian! African vs. European! European vs. Asian!
B value
4 - Population Differentiation as a Function of BGS!
The decrease in Ne locally across the genome as a result of BGS (inferred2 by the value, B, in which lower
values indicate stronger BGS) may impact the rate of genetic drift at specific loci. To investigate this
effect, we measured FST between TGP populations as a function of BGS strength. Our results suggest tha
the strength of BGS is a predictor of population differentiation, with an increase in genetic drift driving
this effect.
5 - Forward Simulations of Demography and BGS!
Using a distribution of fitness effects and a demographic model inferred from previous studies3,4, we ran
forward simulations using SFS_CODE5 to estimate the effect of human demography on determining the
reduction in genetic diversity caused by BGS, observing that the effects of BGS are strongest for those
populations that have experienced sharp population bottlenecks (i.e., Europeans and Asians). However, the
expected reduction in diversity due to BGS across all human populations is still greater than for a
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Fst (estimator method) vs. Background Selection
African vs. Asian
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
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Fst (estimator method) vs. Background Selection
African vs. European
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
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Fst (estimator method) vs. Background Selection
European vs. Asian
0.100.120.140.160.180.200.22
0−24 225−249 475−499 725−749 975−1000
B value!
FST!
BGS strength!
populationdifferentiation!
FST!
FST!
B value! B value!
African vs. Asian! African vs. European! European vs. Asian!
strong weak
• Neutral sites defined as PhyloP ⊂ (-1.2, 1.2)
21. BGS in the human genome
Low Coverage
WGS
High Coverage
exome
of BGS!
, in which lower
investigate this
ults suggest that
etic drift driving
● ● ●
●
●
●
● ● ● ●
●
●
●
● ●
● ●
●
●
●
vs. Background Selection
n vs. Asian
499 725−749 975−1000
alue!
vs. Asian!
ES
LW
YR
MS
GW
IB
CE
TS
GB
FI
CH
JP
CH
KH
CD
BE
PJ
IT
ST
GI
ES
LW
Y
MS
GW
IB
CE
T
GB
FI
CH
JP
CH
KH
CD
BE
PJ
IT
ST
GI
AFR!
!
EUR!
!
EASN!
!
SASN!
!
AFR!
!
EUR!
!
EASN!
!
SASN!
!
4 - BGS Skews the SFS Towards Rare Variants!
Purifying selection on linked sites can cause distortions in gene geneologies, leading to potential skews in
the site-frequency spectrum. To investigate these effects, we measured the SFS as a function of B
separately across the high-coverage and low-coverage regions of phase 3 TGP populations. We observed a
marked increase in the number of of rare variants, especially singletons, in both datasets as a function of
BGS strength. This pattern is amplified in non-African vs. African populations.
Derived Allele Count (log-scale)!
frequency!frequency!
0.00.10.20.30.40.5
YRI
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
0.00.10.20.30.4
CHS
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
CHS!
0.00.10.20.30.40.5
TSI
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
Derived Allele Count (log-scale)!
0.00.10.20.30.40.5
CHS
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
Derived Allele Count (log-scale)!
0.00.10.20.30.40.5
ITU
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
Derived Allele Count (log-scale)!
0.00.10.20.30.4
ITU
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
0.00.10.20.30.4 YRI
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
YRI!
0.00.10.20.30.4
TSI
1 2 3 5 10 25 50 150
B: 0−50
B: 476−525
B: 951−1000
TSI!
Low-!
Coverage!
High-!
Coverage!
ratiovec[1]
1.351.45
●
Low−Coverage
High−Coverage
Ratio of Singleton Frequency in Strong BGS Bin vs. Weak BGS Bin!
ITU!
ratio!
• Neutral sites defined as PhyloP ⊂ (-1.2, 1.2)
23. Complex signatures of selection
• Soft selective sweeps result in multiple
haplotypes increasing in frequency.
Soft Sweep
Zach Szpiech
24. Extended Multiple Haplotype Homozygosity
-- haplotype sample size!
-- set of distinct haplotypes from the locus to marker x!
-- ith most frequent haplotype!
-- number of haplotypes
EHH
SelScan: Szpiech & Hernandez (arXiv)
Sorry, redacted for now… More
coming soon!!
25. Power
0 0.01 0.02 0.05 0.10
160%
120%
80%
40%
0%
Constant Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
%increaseinpoweroveriHS
Sampling
Frequency
0 0.01 0.02 0.05 0.10
140%
120%
60%
100%
80%
40%
20%
0%
African Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
%increaseinpoweroveriHS
Sampling
Frequency
0 0.01 0.02 0.05 0.10
60%
100%
80%
40%
20%
0%
European Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
%increaseinpoweroveriHS
Sampling
Frequency
0 0.01 0.02 0.05 0.10
100%
60%
80%
40%
20%
0%
Constant Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
Power
Sampling
Frequency
0 0.01 0.02 0.05 0.10
100%
60%
80%
40%
20%
0%
African Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
Power
Sampling
Frequency
0 0.01 0.02 0.05 0.10
100%
60%
80%
40%
20%
0%
European Demography (s = 0.01)
0.70
0.80
0.90
Frequency at which selection begins
Power
Sampling
Frequency
26. A genomic approach to
detecting selection
• Most SNPs are non-coding.
• Most regulatory elements do not act on the
nearest gene.
• We can use genome-wide signatures of selection
to infer selection on genes using eQTL
information.
ARTICLE
Sherlock: Detecting Gene-Disease Associations
by Matching Patterns of Expression QTL and GWAS
Xin He,1,2 Chris K. Fuller,1 Yi Song,1 Qingying Meng,3 Bin Zhang,4 Xia Yang,3 and Hao Li1,*
Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong
body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene
expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we pre-
sent a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a
disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression
quantitative trait loci (eQTL) of the gene, which constitute a unique ‘‘genetic signature,’’ should overlap significantly with the set of loci
associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associa-
tions from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes pre-
dicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly,
predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association
signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and
is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites,
noncoding RNAs, and epigenetic modifications.
Introduction
Recent application of genome-wide association studies
(GWASs) to complex human diseases led to the discovery
both cis- and trans-expression QTL in the context of associ-
ation studies. So far, information from trans variations has
largely been ignored because only cis variants can be as-
signed to their target genes based on proximity by using
the GWAS data alone. The growing collection of eQTLHe et al. AJHG (2013)
27. Detecting selection on
regulatory networks
Figure 1. The Sherlock Algorithm: Matching Genetic Signatures of Gene Expression Traits to that of the Disease to Identify Gene-
Disease Associations He et al. AJHG (2013)
29. Selection on standing variation
driven by response to pathogens
Description P-value FDR q-value
cytokine-mediated signaling
pathway
5.92E-06 6.26E-02
immune effector process 7.47E-06 3.95E-02
regulation of immune system
process
7.47E-06 2.64E-02
regulation of defense response
to virus
8.53E-06 2.26E-02
lymphocyte costimulation 9.36E-06 1.98E-02
T cell costimulation 9.36E-06 1.65E-02
GOrilla
30. Haplotype-based selection signals
recapitulate geography
−5 0 5
−50510
Top 1% of windows
PC1 (14.4%)
PC2(12.6%)
ACB
ASW
CDX
CEU
CHBCHS
CLM
FIN GBRGIH IBS
JPT
KHV
LWK
MKK
MXL
PEL PUR
TSI
YRI• TGP samples with
phased OMNI
genotype data
• Used iHS
• 100kb windows for
each population are
coded 1 if selection
score is in top 1%
(0 otherwise)
31. Conclusions
• Many complex signatures of selection in the human
genome.
• Mixtures of positive and negative selection
• Complicated modes of selection (including soft sweeps)
• Predominant signature of ancient human-lineage
selection seems to be from olfactory processes
• Recent selection on standing variation associated with
complex traits, including pathogen response.
32. Thanks!
1000 Genomes Project Consortium
Funding: NHGRI; QB3; CHARM; CTSI
ryan.hernandez@ucsf.edu
Nicolas
Strauli
Cyrus
Maher
Raul
Torres
Lawrence
Uricchio
Zach
Szpiech