BIO 106
Lecture 10
Quantitative Inheritance
A. Inheritance of Quantitative Characters
1. Multiple Genes
2. Number of Genes in polygene Systems
3. Regression to the Mean
4. Effects of Dominance and Gene Interactions
5. Effects of Genes in Multiplying Effects
B. Analysis of Quantitative Characteristics
C. Components of Phenotypic Variance
D. Heredity
1. Heritability in the Narrow Sense
2. Heritability in the Broad Sense
Genetic linkage is the tendency of DNA sequences that are close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction.
BIO 106
Lecture 10
Quantitative Inheritance
A. Inheritance of Quantitative Characters
1. Multiple Genes
2. Number of Genes in polygene Systems
3. Regression to the Mean
4. Effects of Dominance and Gene Interactions
5. Effects of Genes in Multiplying Effects
B. Analysis of Quantitative Characteristics
C. Components of Phenotypic Variance
D. Heredity
1. Heritability in the Narrow Sense
2. Heritability in the Broad Sense
Genetic linkage is the tendency of DNA sequences that are close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction.
Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
This PowerPoint presentation is from the third webinar in a five part series on Breeding Better Sheep & Goats. The presenter is Susan Schoenian, University of Maryland Extension Sheep & Goat Specialist.
Genomic evaluation of low-heritability traits: dairy cattle health as a modelJohn B. Cole, Ph.D.
Genetic selection has been very successful when applied to traits of moderate to high heritability, but progress has been slow for traits with low heritabilities. The problem is further compounded when novel traits are considered because data needed to calculate high-reliability PTA generally are not available. A combination of producer-recorded health event data and SNP genotypes may permit the routine calculation of PTA with reasonable reliabilities for health traits.
Poster presented at the 5th International Symposium on Animal Functional Genetics in Guaruja, Brazil, in 2014.
The genetic architecture of recombination rate variation in a natural populat...Susan Johnston
Genome-wide association study vs. regional heritability analysis to detect genetic variants underlying individual recombination rate variation in a wild population of Soay sheep.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Clinical trials are about comparability not generalisability V2.pptxStephenSenn3
It is a fundamental but common mistake to regard clinical trials as being a form of representative inference. The key issue is comparability. Experiments do not involve typical material. In clinical trials; it is concurrent control that is key and randomisation is a device for calculating standard errors appropriately that should reflect the design.
Generalisation beyond the clinical trial always involves theory.
Clinical trials are about comparability not generalisability V2.pptxStephenSenn2
Lecture delivered at the September 2022 EFSPI meeting in Basle in which I argued that the patients in a clinical trial should not be viewed as being a representative sample of some target population.
Chapter 14Molecular and Genetic EpidemiologyLe.docxbartholomeocoombs
Chapter 14
Molecular and Genetic
Epidemiology
Learning Objectives
• Differentiate between molecular and
genetic epidemiology
• Describe principles of inheritance and
sources of genetic variation
• Define epidemiologic approaches for
the identification of genetic
components to disease
Peeking into the “Black Box”
• Many risk factors can be quantified
through questionnaires, records, and
easily measured attributes (such as
blood pressure and anthropometrics).
• The biological mechanism(s) through
which these factors influence disease
is not always apparent (i.e., a “black
box”).
Value of Mechanistic Insight
• Biologic plausibility is a criterion for
causality.
• Linking lifestyle risk factors with
measures of biologic effect
strengthens interpretations of
causality.
• This linkage, in turn, provides
stronger support for interventions.
Why Distinguish Between
Molecular and Genetic
Epidemiology?
• The basic tenets and principles of
molecular and genetic epidemiology are
the same.
• However, there are specific features
regarding design, analysis and
interpretation inherent in the latter.
Definition of Genetic
Epidemiology
• A discipline that seeks to unravel
the role of genetic factors and their
interactions with environmental
factors in the etiology of diseases,
using family and population study
approaches.
Key Aspects of This Definition
• Inherited susceptibility does not mean
inherited disease--environment
matters!
• When families are studied, the
observations (study subjects) are no
longer independent.
• This dependence requires special
considerations for the analysis of
data.
Genetic Epidemiology is a
Method to Answer:
• Does a disease cluster in families?
• If so, is that clustering likely a result of shared
non-genetic risk factors?
• If the clustering is not accounted for by shared
lifestyle or common environment, is the pattern
of disease consistent with inherited effects?
• If so, where is the putative gene?
What Diseases or Risk Factors
Cluster in Families?
• Heart disease
• Various cancers
• Alcoholism
• Others
Epidemiologic Assessment of
Clustering
• Case-control study
• Comparison of the frequency of a
positive family history
• Expectation under genetic influence
Clustering of “Non-Genetic”
Exposures in Families
• Employment (e.g., several family
members with medical degrees)
• Radon from soil
• Religious preferences
• Lead in paint
• Others?
Major Point of This Section
• You cannot tell easily whether
clustering of a risk factor or disease
within a family is due to genetics,
culture, or shared environment
(including social or political factors).
• Clustering within a family will also
occur simply due to bad luck!
Other Correlates of Family
History
• Large family size
• Age of relatives (for an age-related
disease)
• Gender distribution (consider
testicular cance.
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
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.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Lecture 3 quantitative traits and heritability full
1. This session
• The theory behind quantitative traits
• Heritability – definition and estimation
• Data preparation
• Lab on data preparation
2. Learning objectives
• Primary
• Know what a quantitative vs. a categorical trait is.
• Be able to calculate heritability from twin correlations
• Explain why heritability is population specific
• Be able to transform a variable to a normal distribution
• Secondary
• Explain why we think quantitative traits are caused by
many genetic variants.
• Evaluate why heritability estimates may not be accurate
• Discuss the importance of sample and trait characteristics.
9. Quantitative traits
• Attention Deficit Hyperactivity Disorder
• Characterized by developmentally
inappropriate
– Hyperactivity / impulsivity
– Inattentiveness
• Ever said: I am sure I am ‘a bit ADHD’?
11. Qualitative vs. Quantitative traits
• Qualitative
• Categorical
• Dichotomous
• Quantitative
• Continuous
• Dimensional
Is it easy to decide whether traits are qualitative
or quantitative?
Autism
Type II Diabetes
Eating disorders
Dissociation disorders
12. “Traits are influenced by many variants of small
effect, no one variant being necessary, nor
sufficient, for the disorder”.
Implications of the dimensional
approach for genetics
Quantitative Trait Loci approach: Quantitative trait
loci (QTLs) are stretches of DNA containing or
linked to the genes that underlie a quantitative
trait
13. • Imagine 1 locus contributing to a trait
• Each locus has 2 Alleles, one of which is the
risk allele
• The presence of each copy of the risk allele
conveys an additional ‘score’ on the trait
• What happens are you add loci?
Why QTLs give rise to a normal
distribution
14. • 1 locus. Aa.
• How many genotypes?
• AA
• Aa
• aa
Why QTLs give rise to a normal
distribution
15. • Given our genotypes, each risk allele gives you
a score of +1 on the phenotype
• If A is the risk allele, what are our phenotype
scores?
• AA
• Aa
• aa
Why QTLs give rise to a normal
distribution
+2
+1
+0
20
21
19
.5
.25
.25
Genotype Effect Trait Frequency
16. Why QTLs give rise to a normal
distribution
0
0.5
1
1.5
2
2.5
19 20 21
17. • 2 locus. Aa / Bb
• 2 risk alleles (A and B)
• Aa / Bb take a value of 20
• Fill in Table 1
Why QTLs give rise to a normal
distribution
18. Why QTLs give rise to a normal
distribution
Genotypes Effect Trait Frequency
AA/BB +4 21 1/16
AA/Bb +3 20 2/16
aA/BB +3 20 2/16
AA/bb +2 19 1/16
aA/Bb +2 19 4/16
aa/BB +2 19 1/16
Aa/bb +1 18 2/16
aa/Bb +1 18 2/16
aa/bb +0 17 1/16
19. • 2 locus. Aa / Bb
• 2 risk alleles (A and B)
• Aa / Bb take a value of 20
• Fill in Table 1
• Sketch out a graph (assuming 16 individuals)
Why QTLs give rise to a normal
distribution
23. What do you notice about the trait as
the number of loci increases?
*Note: this shows additive
genetic variance. Dominant
genetic variance calculations are
in the resources section.
25. • Waiting for ‘proof’ that the phenotypes &
genes are the same
• Does not mirror how clinicians work
Controversies of the QTL
26. • Much easier to find study participants
• Can be more powerful
Advantages of the QTL approach
27. • Quantitative traits are normally distributed
traits
• Assumed that these arise from the combined
effects of multiple genetic variants
• Assumption is that finding variants associated
with traits will find genes associated with
disease:
• Attentiveness -> ADHD
• Blood sugar -> T2DM
Quantitative traits in genetic research
29. Heritability is an estimation of the proportion of
observed trait variance, attributable to genetic
influences.
What is heritability?
Trait variance (Vp)
Vp = Variance due to genetics (Vg) +
variance due to non genetics (VE)
30. • Twin studies
• Vp = A + C + E
• A = Genes
• C = Common environment
• E = Unique environment
How do we calculate heritability?
31. • Assumptions of Twin studies
• MZ twins correlate (rMZ) 100% A
• DZ twin correlate (rDZ) 50% A
• MZ and DZ correlate 100% C
• MZ and DZ correlate 0% for E
How do we calculate heritability?
32. • A = h2 = 2 (rMZ – rDZ)
• C = rMZ – A
• E = 1- rMZ
• If rMZ = .8, and rDZ = .4
• A = 2 (.8 - .4 ) = .8
• C= .8 - .8 = 0
• E = 1 = .8 = .2
How do we calculate heritability?
35. • The equal environments assumption (EEA) (including
prenatal)
• Assortative mating
• Generalizability.
Assumptions of the twin method
36. • Gene environment correlation (rGE)
• Passive rG
• Increase C
• Active rG
• Increases or decreases heritability
• (why does this increase with age?)
• Evocative rG
• Increases or decreases heritability
• Gene environment interaction (G*E)
– Increases E
Limitations of the twin method
37. Our concept of heritability is tied up with
variation, and with our population.
What is heritability?
38. A thought experiment:
• Where do our hearts come from? If you have a heart,
is this from your genetics? Or from the environment?
• What is the heritability of having a heart?
Heritability & Variation
39. • Tonsillectomies (NG martin, 1991)
• Thought experiment: Reading ability
Population specific heritability
40. 1. How much genetics contributes to some trait
that an individual shows.
Heritability?
41. 2. Proportion of trait variation between
individuals in a given population due to genetic
variation.
Heritability?
42. 2 definitions:
1. How much genetics contributes to some trait
that an individual shows.
2. Proportion of trait variation between
individuals in a given population due to
genetic variation.
What is heritability?
What is the difference?
43. Question:
Does a high heritability for a disease mean that
we should target our treatments at genetics?
What is heritability?
46. • If offspring do not resemble parents then best fit line has a slope of approximately zero.
• Slope of zero indicates most variation in individuals due to variation in environment.
• If offspring strongly resemble parents then best fit line will be close to 1.
Heritability estimates in non twins
47. • Most traits in most populations fall somewhere in the middle
with offspring showing moderate resemblance to parents.
Heritability estimates in non twins
48. • Heritability can be ascertained from twin correlations, and parent-
offspring data
• The point heritability is estimate is not exact (not like a mean)
• Furthermore, it applies only to your population, at your time.
Heritability summary
50. The world of quantitative genetics
• Genetics… without genotype data.
Phenotype data
1. Sample
characterization
2. Quantitative trait
distribution
3. Heritability
Genotype data
– Variant description
– Missing data
– HWE
– LD and haplotypes
51. • Make a table of sample characteristics
• Prepare a quantitative trait for genetic analysis
Goals of this lab
52. Summarize the sample characteristics (covariates) for
our population, often broken down by gender or
ethnicity.
Summarize trait distribution
1. Summarize data characteristics
Why?
53. – Define the population parameters for comparison
of results with those from other samples (i.e.
gender, age, health)
– Help to identify biases in the data
Why summarize sample
characteristics?
LOOK AT THE
TABLE CLOSELY!!!
54. – Population definition
– Generalizability
Why summarize trait distribution?
LOOK AT THE
TABLE CLOSELY!!!
57. 1. Is the distribution normal?
2. Are there outliers?
2. Prepare a quantitative trait for
genetic analysis
58. 1. What is a normal distribution?
For continuous data we don’t have equally spaced
discrete values so instead we use a curve or function
that describes the probability density over the range of
the distribution.
Continuous data
59. Normal distribution describes a special class of
continous distributions that are symmetric and can be
described by two parameters
(i) μ = The mean of the distribution
(ii) σ = The standard deviation of the distribution
Changing the values of μ and σ alter the positions and
shapes of the distributions.
The normal distribution
62. Deviations from normal - Kurtosis
‘Tails’ are misshapen
The normal distribution will have a kurtosis of 0
63. Why we care about the normal
distribution
• Assumption of most (including
genetic association) tests.
64. How do we test for a
normal distribution?
• The Chi-square and KS GOF test
(low power).
• Eyeball methods: look at
histogram & look for a skew and
kurtosis -1 - +1
• Shapiro and Wilk formal test
What is the Ho for Shapiro Wilk?
65. What do we do about
non normal distributions
• Run a monotonic transformation
• You can try
• Log
• Square root
• Cube root
• Reciprocal
• STATA: lnskew0 command which
does it for you!
66. Example of a log transformation
Pre transformation Log transformed
68. Screening outliers
Screen ‘odd’ or extreme values
Subjective definition: sometimes values 3 or 4 +/- the
mean
Contentious. Positives and negatives.
My personal recommendation ‘sensitivity analysis’
69. Summary
Normal distribution is a symmetrical distribution
Skew and kurtosis represent a deviation from
normality
Most genetic tests require a normal distribution
Therefore we try to transform our distributions
70. Lab Goals
Going to prepare three variables for analysis: fasting
VLDL, LDL and HDL particle size (cardiovascular
disease risk factors)
1. Prepare a summary table, split by gender, for the
trait and relevant covariate characteristics of the
sample
2. Decide if the variables need to be transformed,
and transform if so.
3. Prepare a variable with no outliers (using 2
definitions)
71. Learning objectives
• Primary
• Given an example of a qualitative trait
• Give an example of a quantitative trait
• With rMz = 6 and rDz = 6, what is the heritability?
• Why is heritability is population specific?
• How would you recognize a non-normal variable and transform it
to a normal distribution
• Secondary
• Explain why we think quantitative traits are caused by many genetic
variants.
• Given one reason why heritability estimates may not be accurate
• Why do we need to include covariate characteristics?
73. Lab Goals
Going to prepare three variables for analysis:
fasting VLDL, LDL and HDL particle size.
Prepare a summary table, split by gender of the
trait distributions and relevant