The document summarizes Wright's F-statistics and Cockerham's θ-statistics, which are methods used to calculate genetic differentiation between populations. It then discusses methods to detect signatures of positive selection, including Extended Haplotype Homozygosity (EHH), integrated Haplotype Score (iHS), and cross population Extended Haplotype Homozygosity (xp-EHH). EHH detects when a haplotype is over-represented in a population due to recent positive selection. iHS and xp-EHH are derived from EHH to identify specific genomic regions under selection. The document uses examples and figures to illustrate key concepts.
Thesis defence of Dall'Olio Giovanni Marco. Applications of network theory to...Giovanni Marco Dall'Olio
This is the presentation of my PhD thesis defence. It describes two applications of network theory to improve the methods to understand genetic adaptation in the human genome.
Thesis defence of Dall'Olio Giovanni Marco. Applications of network theory to...Giovanni Marco Dall'Olio
This is the presentation of my PhD thesis defence. It describes two applications of network theory to improve the methods to understand genetic adaptation in the human genome.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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3. Fst Wright’s F-statistics
3 types of Heterozygosity[4]
Individual, Subpopulation, Total Population
1 HI = 1
n
n
i=1
ˆHi
2 HS = 1
n
n
i=1 2pi qi
3 HT = 2¯p¯q
( ˆHi : observed heterozygosity in ith subpopulation, 2pi qi : average
heterozygosity in ith subpopulation, 2¯p¯q: average heterozygosity of total
population)
Locus 별로 값 구한다.
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4. Fst Wright’s F-statistics
Wright’s F-statistics[4]
1 FIS = HS −HI
HS
2 FST = HT −HS
HT
3 FIT = HT −HI
HT
Example
FST = 0 → Subpopulation의 effect없다!! 차이 없다.
FST = 1 → Subpopulation별로 차이가 크다.
Simple relation
1 − FIT = (1 − FIS )(1 − FST )
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7. Fst Wright’s F-statistics
FST inference[5]
Convenient measure of genetic differentiation.
Most widely used descriptive statistics in population and
evolutionary genetics.
Natural selection in particular subpopulation.
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 7 / 65
8. Fst Wright’s F-statistics
Problem in estimation
HT = 2¯p¯q
1 Subpopulation마다 sample수가 다르면??
2 Ex: SASIA 1000명, Oceania 100명..
3 제대로 된 ¯p 추정이 아님.
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 8 / 65
9. Fst Cockerham’s θ-statistics
ANOVA approach[1, 5]
θ =
σP
σT
(σP: variance due to population, σT : total variance)
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 9 / 65
11. Fst Cockerham’s θ-statistics
θ inference
Population > 2
대세와 다른 population이 있다!!
어떤 population인지는 말 안해준다.
Pairwise FST
2 population만 가지고 계산.
상대적인 비교.
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 11 / 65
13. Fst Cockerham’s θ-statistics
Figure: FST calculated for each SNP between Tibetan and Han populations[6]
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 13 / 65
14. Fst Cockerham’s θ-statistics
Figure: Inter-population pairwise comparisons of FST statistics
http://academic.reed.edu/biology/professors/srenn/pages/
research/2011_students/sean/SM_thesis.html
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 14 / 65
15. Selection Index
Contents
1 Fst
Wright’s F-statistics
Cockerham’s θ-statistics
2 Selection Index
EHH
iHS
xp-EHH
3 Practice
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 15 / 65
16. Selection Index
특정 인구집단에 특정 haplotype이 많냐??
Example: Erik Corona’s slide - Next slide
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 16 / 65
24. Selection Index EHH
EHH: Sabeti, Reich et al. (2002)[7]
Extended Haplotype Homozygosity
Random으로 2개 haplotype 뽑았을 때 그것이 같을 확률은??
0 → haplotype이 다 다르다.
1 → haplotype이 모두 같다.
관심있는 haplotype을 Core라 한다.
EHHt =
s
i=1
eti
2
ct
2
(t: core haplotype, c: the number of samples of a particular core
haplotype, e: the number of samples of a particular extended haplotype, s:
the number of unique extended haplotype)
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 24 / 65
31. Selection Index EHH
AATTACAGATTACA AACACGC 10
AATTACAGATTACA ATGATAG 8
AATTACAGATTACA AACCCAG 7
AATTACAGATTACA CTGACAG 5
AATTACAGATTACA CAGACAG 3
AATTACAGATTACA AACACAG 6
AATTACAGATTACA CACACAG 4
AATTACAGATTACA CACCCAG 7
GATTACAGATTACA CACATAG 24
GATTACAGATTACA CACACAG 26
EHH What It Is & What It Isn’t
Detects over‐representation of a haplotype
This will raise the p(two haps are homozygous)
Does NOT detect if a haplotype spread quickly
Low recombination != spread quickly
AATTACAGATTACA AACACGC 22
AATTACAGATTACA ATGATAG 28
GATTACAGATTACA CACATAG 24
GATTACAGATTACA CACACAG 26
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53. Selection Index iHS
iHS Characteristics
As both alleles have the same AUC, iHS zero
Large negative values indicate selection of allele in the
denominator
Large positive values indicate selection of allele in the
numerator
Still heavily biased by allele frequency!
Z‐score normalization
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 53 / 65
54. Selection Index iHS
Unstandardized iHS ‐ E(iHS | Allele Frequency)
SD(iHS | Allele Frequency)
E(iHS | Allele Freq.): Estimated from empirical distribution
SD(iHS | Allele Freq.): Estimated from empirical distribution
Integrated Haplotype Score (iHS)
= iHS
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55. Selection Index iHS
iHS Overview
iHS and REHH are EHH based methods to detect
positive selection
iHS outperforms REHH in specific allele frequencies
They don’t completely outperform each other
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 55 / 65
57. Selection Index xp-EHH
xp-EHH: sabeti(2007)[8]
Population 별, 같은 allele별 integreted EHH를 비교!!
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58. Selection Index xp-EHH
Cross Population EHH (XP‐EHH)
AATTACAGATTACA AACACGC 10
AATTACAGATTACA ATGATAG 8
AATTACAGATTACA AACCCAG 7
AATTACAGATTACA CTGACAG 5
AATTACAGATTACA CAGACAG 3
AATTACAGATTACA AACACAG 6
AATTACAGATTACA CACACAG 4
AATTACAGATTACA CACCCAG 7
Same allele but diff population
AATTACAGATTACA CACATAG 20
AATTACAGATTACA CACACAG 30
0.5
XP‐EHH = ln(3.3/0.5) = 1.89 Z‐score Norn
Integrate EHH over distance from allele
Calculated for fwd/rev sides independently
Integrate until EHH = 0.04 in e.a. population
3.3
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59. Selection Index xp-EHH
REHH and iHS are more or less complementary
e.a. is better at detecting pos. sel. at diff freqs.
XP‐EHH
Can detect pos. sel. in high freq. alleles
Susceptible to population variation in
recombination rate
Overview
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 59 / 65
61. Selection Index xp-EHH
Rsb[9]
Population끼리 비교하는 또다른 지표.
Population별로만 비교.
Locus별로 두 allele의 integrated EHH의 average: iES
Locus의 대략적인 selection정도를 population끼리 비교.
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 61 / 65
63. Practice
FST
hierfstat[3]
PER3 gene in HGDP(Human Genome Diversity Panel): 289 SNPs &
7 population
EHH, iHS
rehh[2]
패키지 자체 제공 예제
김진섭 (GSPH, SNU) FST & Some Selection Index October 29, 2014 63 / 65
64. Practice
Reference I
[1] Cockerham, C. C. (1969). Variance of gene frequencies. Evolution, pages 72–84.
[2] Gautier, M. and Vitalis, R. (2012). rehh: an r package to detect footprints of selection in genome-wide snp data from
haplotype structure. Bioinformatics, 28(8):1176–1177.
[3] Goudet, J. (2005). Hierfstat, a package for r to compute and test hierarchical f-statistics. Molecular Ecology Notes,
5(1):184–186.
[4] Hamilton, M. (2011). Population genetics. John Wiley & Sons.
[5] Holsinger, K. E. and Weir, B. S. (2009). Genetics in geographically structured populations: defining, estimating and
interpreting fst. Nature Reviews Genetics, 10(9):639–650.
[6] Huerta-S´anchez, E., Jin, X., Bianba, Z., Peter, B. M., Vinckenbosch, N., Liang, Y., Yi, X., He, M., Somel, M., Ni, P., et al.
(2014). Altitude adaptation in tibetans caused by introgression of denisovan-like dna. Nature, 512(7513):194–197.
[7] Sabeti, P. C., Reich, D. E., Higgins, J. M., Levine, H. Z., Richter, D. J., Schaffner, S. F., Gabriel, S. B., Platko, J. V.,
Patterson, N. J., McDonald, G. J., et al. (2002). Detecting recent positive selection in the human genome from haplotype
structure. Nature, 419(6909):832–837.
[8] Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E. H., McCarroll, S. A.,
Gaudet, R., et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature,
449(7164):913–918.
[9] Tang, K., Thornton, K. R., and Stoneking, M. (2007). A new approach for using genome scans to detect recent positive
selection in the human genome. PLoS biology, 5(7):e171.
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