hardy weinberg genetic equilibrium by kk sahuKAUSHAL SAHU
INTRODUCTION
HISTORY
THE HARDY-WEINBERG LAW
DERIVATION
TERMINOLOGY
PROBLEMS
ASSUMPTION OF HAEDY –WEINBERG EQUILIBRIUM
REFERANCE
The Hardy–Weinberg principle, also known as the Hardy–Weinberg equilibrium, model, theorem or law.
States that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences.
These influences include mate choice, mutation, selection, genetic drift, gene flow and meiotic drive.
Because one or more of these influences are typically present in real populations, the Hardy–Weinberg principle describes an ideal condition against which the effects of these influences can be analyzed.
hardy weinberg genetic equilibrium by kk sahuKAUSHAL SAHU
INTRODUCTION
HISTORY
THE HARDY-WEINBERG LAW
DERIVATION
TERMINOLOGY
PROBLEMS
ASSUMPTION OF HAEDY –WEINBERG EQUILIBRIUM
REFERANCE
The Hardy–Weinberg principle, also known as the Hardy–Weinberg equilibrium, model, theorem or law.
States that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences.
These influences include mate choice, mutation, selection, genetic drift, gene flow and meiotic drive.
Because one or more of these influences are typically present in real populations, the Hardy–Weinberg principle describes an ideal condition against which the effects of these influences can be analyzed.
This poster by Mary Shimoyama about RGD's PhenoMiner Phenotype Database and Data Mining tool was presented at the December 2009 Rat Genomics and Models meeting at Cold Spring Harbor Laboratory.
ASSORTIVE MATING AND GENE FREQUENCY CHANGES (POPULATION GENETICS)316116
This slide briefly the explanation of random mating as deviation from the Hardy-Weinberg equilibrium and also the changes in gene frequency as a result of violation of Hardy-Weinberg assumptions on gene frequency
Genetics 101: Genetic Differentiation in the Age of Ecological Restorationnycparksnmd
Dr Susan Mazer, University of California, Santa Barbara
Symposium:
What is Local? Genetics & Plant Selection in the Urban Context. (Tuesday, May 23, 2006, American Museum of Natural History)
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
11. Allele Frequencies Define Gene Pools As there are 1000 copies of the genes for color, the allele frequencies are (in both males and females): 320 x 2 (RR) + 160 x 1 (Rr) = 800 R; 800/1000 = 0.8 (80%) R 160 x 1 (Rr) + 20 x 2 (rr) = 200 r; 200/1000 = 0.2 (20%) r 500 flowering plants 480 red flowers 20 white flowers 320 RR 160 Rr 20 rr
12.
13.
14.
15.
16.
17.
18.
19. Hardy-Weinberg Equilibrium The gene pool of a non-evolving population remains constant over multiple generations; i.e. , the allele frequency does not change over generations of time. The Hardy-Weinberg Equation: 1.0 = p 2 + 2 pq + q 2 where p 2 = frequency of AA genotype; 2 pq = frequency of Aa plus aA genotype; q 2 = frequency of aa genotype
27. 2) Natural selection As previously stated, differential success in reproduction based on heritable traits results in selected alleles being passed to relatively more offspring (Darwinian inheritance). The only agent that results in adaptation to environment. 3) Gene flow -is genetic exchange due to the migration of fertile individuals or gametes between populations.
28.
29. 4) Mutation Mutation is a change in an organism’s DNA and is represented by changing alleles. Mutations can be transmitted in gametes to offspring, and immediately affect the composition of the gene pool. The original source of variation.
30. Genetic Variation, the Substrate for Natural Selection Genetic (heritable) variation within and between populations: exists both as what we can see ( e.g. , eye color) and what we cannot see ( e.g. , blood type). Not all variation is heritable. Environment also can alter an individual’s phenotype [ e.g. , the hydrangea we saw before, and… … Map butterflies (color changes are due to seasonal difference in hormones)].
31.
32. Variation within populations Most variations occur as quantitative characters ( e.g. , height); i.e. , variation along a continuum, usually indicating polygenic inheritance. Few variations are discrete ( e.g. , red vs . white flower color). Polymorphism is the existence of two or more forms of a character, in high frequencies, within a population. Applies only to discrete characters.
33. Variation between populations Geographic variations are differences between gene pools due to differences in environmental factors. Natural selection may contribute to geographic variation. It often occurs when populations are located in different areas, but may also occur in populations with isolated individuals.
34. Geographic variation between isolated populations of house mice. Normally house mice are 2n = 40. However, chromosomes fused in the mice in the example, so that the diploid number has gone down.
35. Cline , a type of geographic variation, is a graded variation in individuals that correspond to gradual changes in the environment. Example: Body size of North American birds tends to increase with increasing latitude. Can you think of a reason for the birds to evolve differently? Example: Height variation in yarrow along an altitudinal gradient. Can you think of a reason for the plants to evolve differently?
36.
37. Mutation and sexual recombination generate genetic variation a. New alleles originate only by mutations (heritable only in gametes; many kinds of mutations; mutations in functional gene products most important). - In stable environments, mutations often result in little or no benefit to an organism, or are often harmful. - Mutations are more beneficial (rare) in changing environments. (Example: HIV resistance to antiviral drugs.) b. Sexual recombination is the source of most genetic differences between individuals in a population. - Vast numbers of recombination possibilities result in varying genetic make-up.
38. Diploidy and balanced polymorphism preserve variation a. Diploidy often hides genetic variation from selection in the form of recessive alleles. Dominant alleles “hide” recessive alleles in heterozygotes. b. Balanced polymorphism is the ability of natural selection to maintain stable frequencies of at least two phenotypes. Heterozygote advantage is one example of a balanced polymorphism, where the heterozygote has greater survival and reproductive success than either homozygote (Example: Sickle cell anemia where heterozygotes are resistant to malaria).
39.
40. Frequency-dependent selection = survival of one phenotype declines if that form becomes too common. (Example: Parasite-Host relationship. Co-evolution occurs, so that if the host becomes resistant, the parasite changes to infect the new host. Over the time, the resistant phenotype declines and a new resistant phenotype emerges.)
41.
42.
43. Neutral variation is genetic variation that results in no competitive advantage to any individual. - Example: human fingerprints.
44. A Closer Look: Natural Selection as the Mechanism of Adaptive Evolution Evolutionary fitness - Not direct competition, but instead the difference in reproductive success that is due to many variables. Natural Selection can be defined in two ways: a. Darwinian fitness - Contribution of an individual to the gene pool, relative to the contributions of other individuals. And,
47. Diversifying selection favors extreme over intermediate phenotypes. - Occurs when environmental change favors an extreme phenotype. Stabilizing selection favors intermediate over extreme phenotypes. - Reduces variation and maintains the current average. - Example = human birth weights.
51. All asexual individuals are female (blue). With sex, offspring = half female/half male. Because males don’t reproduce, the overall output is lower for sexual reproduction.
52.
53.
54.
55. Natural selection does not produce perfect organisms a. Evolution is limited by historical constraints ( e.g. , humans have back problems because our ancestors were 4-legged). b. Adaptations are compromises. (Humans are athletic due to flexible limbs, which often dislocate or suffer torn ligaments.) c. Not all evolution is adaptive. Chance probably plays a huge role in evolution and not all changes are for the best. d. Selection edits existing variations. New alleles cannot arise as needed, but most develop from what already is present.
61. Hardy-Weinberg Equilibrium Population of cats n=100 16 white and 84 black bb = white B_ = black Can we figure out the allelic frequencies of individuals BB and Bb?
112. Selection on sickle-cell allele aa – abnormal ß hemoglobin sickle-cell anemia very low fitness intermed. fitness high fitness Selection favors heterozygotes ( Aa ). Both alleles maintained in population ( a at low level). Aa – both ß hemoglobins resistant to malaria AA – normal ß hemoglobin vulnerable to malaria
113.
114. Genetic drift 8 RR 8 rr Before: After: 2 RR 6 rr 0.50 R 0.50 r 0.25 R 0.75 r
115.
116.
117. AA x AA AA aa x aa aa genotype frequencies: AA = 0.8 x 0.8 = 0.64 Aa = 2(0.8 x0.2) = 0.32 aa = 0.2 x 0.2 = 0.04 allele frequencies: A = 0.8 A = 0.2 A A A A A A A A a a AA 0.8 x 0.8 Aa 0.8 x 0.2 aA 0.2 x 0.8 A 0.8 A 0.8 a 0.2 a 0.2 aa 0.2 x 0.2
The method of counting alleles was used in the preceding example to compute allele frequencies. This method can be used only when the actual numbers of genotypes are given. Another method of computing allele frequencies can be used when frequencies, rather than numbers, of genotypes are given. Allele frequencies can be computed from the genotypic frequencies. As long as genotypic frequencies are given, the following formulas work regardless of whether or not the population is in equilibrium:
Allele frequencies in the offspring can be computed from genotypic frequencies as: f(B) = .35 + 1/2(.50) = .6 f(b) = .15 + 1/2(.50) = .4
Note that the allele frequencies in the offspring are equal to the average of allele frequencies in the parents (averaged over sires and dams). i.e., f(B) = 1/2 (.5 + .7) = .6 f(b) = 1/2 (.5 + .3) = .4
For a trait displaying dominance/recessiveness, it is usually not possible to distinguish between homozygous dominant versus heterozygous individuals. In such situations, it is usually impossible to know the exact genotypic, and thus, allele frequencies. However, in such cases it is sometimes possible to estimate genotypic and allele frequencies by use of the Hardy-Weinberg Theorem.
H-W formulas will not be accurate if population does not at least approximated equilibrium.
For a trait displaying dominance/recessiveness, it is usually not possible to distinguish between homozygous dominant versus heterozygous individuals. In such situations, it is usually impossible to know the exact genotypic, and thus, allele frequencies. However, in such cases it is sometimes possible to estimate genotypic and allele frequencies by use of the Hardy-Weinberg Theorem.
For a trait displaying dominance/recessiveness, it is usually not possible to distinguish between homozygous dominant versus heterozygous individuals. In such situations, it is usually impossible to know the exact genotypic, and thus, allele frequencies. However, in such cases it is sometimes possible to estimate genotypic and allele frequencies by use of the Hardy-Weinberg Theorem.
For a trait displaying dominance/recessiveness, it is usually not possible to distinguish between homozygous dominant versus heterozygous individuals. In such situations, it is usually impossible to know the exact genotypic, and thus, allele frequencies. However, in such cases it is sometimes possible to estimate genotypic and allele frequencies by use of the Hardy-Weinberg Theorem.
We can assume that the population is sufficiently near equilibrium, because: it is a large population there has been no selection for this particular trait closed population implies no migration mutation can generally be assumed to be negligible Therefore, we can use the Hardy-Weinberg formulas.
Start with the known: f(black) = f(bb) = .09 =q 2 Then, calculate the allele frequencies, starting with the recessive allele. Then, p = 1 – q = 1 - .3 = .7 = f(B) because P + q = 1 We already know that f(bb) = .09. The other genotypic frequencies are then calculated as: f(BB) = p 2 = .49 f(Bb) = 2pq = .42
Again, f(bb) = .04 is known. The other genotypic frequencies and the allele frequencies are estimates. Question: Does equilibrium ever exist? Many loci may at least approximate equil.
The effects of mutation alone are usually very small in one generation. The effects can be much greater if the mutation has a selective advantage (i.e., combined effects of mutation and selection). For example, the polled allele in cattle is thought to originally have arisen as the result of mutation, and its frequency in some breeds was increased by selection. Also, in very small populations, mutation effects can sometimes be important because of random drift.
Migration can be thought of on several levels: herd, breed, commercial population, etc. Examples: 1. Bringing in a new herd sire. 2. The herd books of a previously “pure” breed are opened to allow the registration of animals that are not strict purebreds. 3. "Under-the-counter" additions of a different breed to a supposedly pure breed (against breed regulations). 4. Importation of continental European “exotic” cattle into the U.S. 5. A wildlife agency transports animals from an “outside” population into a small, isolated population in order to widen the genetic base (i.e., reduce inbreeding depression).
Δ p mig = m(P m -P o ) New allele frequency: P 1 = P o + ∆p For migration to have a significant effect: 1. m must be quite large 2. difference between P m and P o must be large
Note: Δ p = - Δ q so that Δ p + Δ q = 0
Random (chance) deviations due to the Law of Random Segregation. That is, for any gene pair of a parent, random chance determines which member of the gene pair gets passed on to a gamete and thus to an offspring. Changes in allele frequencies due to genetic drift should be random and cancel one another out in most situations. However, in very small, isolated populations, it is possible for random drift to have rather significant effects on allele frequencies simply due to chance occurrences. Not usually very important in most livestock populations. Can be important in small populations such as endangered breeds or endangered species.
Occurs whenever some individuals (genotypes) leave more offspring than others. i.e. differential reproductive rate. Some individuals contribute more genetic material to future generations than other individuals. No new genes are created. However, gene frequencies are changed. New combinations of genes (i.e. genotypes) may be created. Primary tool that we use as breeders to make genetic change in livestock herds. * Primary effect of selection - to change gene freq. 2 General Types of Selection 1. Natural Selection - those most fit (most desired or adapted) leave most offspring (Darwin Th. Survival of Fittest). - Still occurs in most domestic species - Important from evolutionary standpoint 2. Artificial - imposed by humans, who determine which individuals contribute the most genetic material to the next generation.
Initial frequencies prior to selection.
q = .111 - .2 = -.089 (change in freq. of undesired allele)
q = .25 -.70 = -.45 (change in freq. of horned gene) Note: more change can be made when the initial freq. of the gene that you're selecting against is high, or when initial frequency of the gene that you're selecting for is low.
Easiest to select against recessive allele when it’s at higher frequency (fewer are hidden in heterozygotes. When q is low, most of the recessive genes are hidden in the heterozygote & sel. progress is slow.