Do height and BMI affect human capital formation? Natural experimental evidence from DNA. CHE seminar presentation by Neil Davies, University of Bristol 12 June 2020
Genotype-By-Environment Interaction (VG X E) wth ExamplesZohaib HUSSAIN
Introduction
Phenotypic variation can be caused by the combination of genotypes and environments in a population. Genotypes are all equally sensitive to their environments, meaning that a change of environment would impact the phenotype of all genotypes to the same extent. In fact, genotypes very often have different degrees of sensitivity to environmental conditions. This cause of phenotypic variance is called genotype by- environment interaction and is symbolized by VG x E. This adds another term to the expression for the independent causes of total phenotypic variation in a population
Ve = VG + VE + VG xE
I am working with collaborators in Brazil, the U.S., and Mexico to complete genetic data analyses and manuscripts from two postdoctoral research fellowships. This slideshow presents a brief overview of the two main funded research projects that I am involved in.
Camp Kinomaage is a week-long, hands-on science summer camp held at the University of Michigan Biological Station on Douglas Lake, near Pellston, Michigan for middle school students from Michigan Native American tribes.
Genetic factors in pathogen colonisation is emerging as a new field of research as " infectogenomics". The susceptible host to periodontal disease directs towards genetic factors playing a role in periodontal disease pathogenesis. Earlier identification of gene polymorphisms associated with periodontal disease preogression may help in early diagnosis, treatment of such susceptible host.
Human Genome Project is a worldwide scientific achievement. It was a thirteen-year project initiated in 1990 and completed in 2003. Human Genome Project helped a lot in the identification of diseased genes as DNA is very significant for understanding the diseased gene and their functions. It helped in the identification of disease loci for many diseases and presented their treatment through preventive measures. It identified the gene loci for many diseases like cancer, asthma, high blood pressure, diabetes type 2, obesity, Alzheimer's disease, Down's syndrome, Turner's syndrome, depression and many types of heart diseases including cardiovascular disease and coronary artery disease. This project does not directly treat the diseases but it helps in the identification of disease gene loci and then allows the treatment of disease through its preventive measures before the appearance of symptoms or at the initial stages of the disease through many techniques like gene therapy, pharmacogenomics, and targeted drug therapy. These are the helpful techniques in the diagnoses of the human disease gene locus.
Ross Shegog - The Secret of Seven Stones: A Game to Impact Youth Skills and P...SeriousGamesAssoc
Presenter: Ross Shegog, Associate Professor, University of Texas
Few game-based interventions target sexual health and even fewer target parent-youth communication. The presentation describes the development and testing of an online adventure game, ‘The Secret of Seven Stones’ (SSS), to engage parents and youth (11-14 yrs.) to go beyond ‘the sex talk’ to impact youth decisions related to friendships, dating, and sex. SSS, informed by parent-youth dyads and previous empirical data, provides behavioral skills training in 15 domains (drawn from over 1300 learning objectives) encompassing responsible decision making about friendships, dating relationships, and sex. SSS features 18 game levels that include 50 interactive skills training clusters, 54 card ‘battle’ sequences, and 7 game-mediated parent-youth ‘PEP’ talks. As youth play SSS, parents receive progress updates and cues to receive resources to guide communication with their youth. SSS offers insight into an intergenerational gaming approach for health prevention, found feasible for a RCT efficacy trial.
Genotype-By-Environment Interaction (VG X E) wth ExamplesZohaib HUSSAIN
Introduction
Phenotypic variation can be caused by the combination of genotypes and environments in a population. Genotypes are all equally sensitive to their environments, meaning that a change of environment would impact the phenotype of all genotypes to the same extent. In fact, genotypes very often have different degrees of sensitivity to environmental conditions. This cause of phenotypic variance is called genotype by- environment interaction and is symbolized by VG x E. This adds another term to the expression for the independent causes of total phenotypic variation in a population
Ve = VG + VE + VG xE
I am working with collaborators in Brazil, the U.S., and Mexico to complete genetic data analyses and manuscripts from two postdoctoral research fellowships. This slideshow presents a brief overview of the two main funded research projects that I am involved in.
Camp Kinomaage is a week-long, hands-on science summer camp held at the University of Michigan Biological Station on Douglas Lake, near Pellston, Michigan for middle school students from Michigan Native American tribes.
Genetic factors in pathogen colonisation is emerging as a new field of research as " infectogenomics". The susceptible host to periodontal disease directs towards genetic factors playing a role in periodontal disease pathogenesis. Earlier identification of gene polymorphisms associated with periodontal disease preogression may help in early diagnosis, treatment of such susceptible host.
Human Genome Project is a worldwide scientific achievement. It was a thirteen-year project initiated in 1990 and completed in 2003. Human Genome Project helped a lot in the identification of diseased genes as DNA is very significant for understanding the diseased gene and their functions. It helped in the identification of disease loci for many diseases and presented their treatment through preventive measures. It identified the gene loci for many diseases like cancer, asthma, high blood pressure, diabetes type 2, obesity, Alzheimer's disease, Down's syndrome, Turner's syndrome, depression and many types of heart diseases including cardiovascular disease and coronary artery disease. This project does not directly treat the diseases but it helps in the identification of disease gene loci and then allows the treatment of disease through its preventive measures before the appearance of symptoms or at the initial stages of the disease through many techniques like gene therapy, pharmacogenomics, and targeted drug therapy. These are the helpful techniques in the diagnoses of the human disease gene locus.
Ross Shegog - The Secret of Seven Stones: A Game to Impact Youth Skills and P...SeriousGamesAssoc
Presenter: Ross Shegog, Associate Professor, University of Texas
Few game-based interventions target sexual health and even fewer target parent-youth communication. The presentation describes the development and testing of an online adventure game, ‘The Secret of Seven Stones’ (SSS), to engage parents and youth (11-14 yrs.) to go beyond ‘the sex talk’ to impact youth decisions related to friendships, dating, and sex. SSS, informed by parent-youth dyads and previous empirical data, provides behavioral skills training in 15 domains (drawn from over 1300 learning objectives) encompassing responsible decision making about friendships, dating relationships, and sex. SSS features 18 game levels that include 50 interactive skills training clusters, 54 card ‘battle’ sequences, and 7 game-mediated parent-youth ‘PEP’ talks. As youth play SSS, parents receive progress updates and cues to receive resources to guide communication with their youth. SSS offers insight into an intergenerational gaming approach for health prevention, found feasible for a RCT efficacy trial.
Running head: ILLICIT DRUGS 1
ILLICIT DRUGS 5
Illicit Drugs Abuse
Constance Lingard
Global University of Arizona
Illicit Drugs Abuse
Introduction
Abuse of harmful substances by parents has far-reaching and devastating effects on the children of those parents. These young people are exposed to various risks, including an uncertain housing situation. Substance abuse among parents is a significant threat to the health and safety of a child who does not share a home with either of their biological parents. Although studies show that these young people have a higher risk of experiencing housing insecurity, very little is known about their actual lived experiences in housing. The parent's involvement in illicit drug use affects their daily operations, including their engagement in their day-to-day jobs that would make it possible for them to meet the needs of their children (Lloyd, 2018). There is a high possibility that engagement in illicit drugs by the parents can result in them committing a crime and even failing in their duties, leading to their children becoming homeless and also seeking child protective services.
Problem Statement
The engagement of the parents in illicit drugs has a detrimental impact on their children because they are likely to be addicted in the process, making it challenging for them to address the needs of their children. The gap that the parents that engage in illicit drugs pushes the children to suffer and struggle, resulting in some failing to get their daily meals and housing challenges. Scenarios of extreme neglect of parents to their children result in some being homeless. This is where the child protective services have to take the responsibility of housing and providing food and healthcare for them (Rogers & Parkinson, 2017). Children staying with their parents who are addicts of illicit drugs put their lives in danger, especially their behavior being influenced to taking drugs at an early age or emotionally affected based on the fights and disagreements that come with drug-addicted parents. Choosing child protective services is vital in protecting the children's interests and securing them from a bad environment in them to grow.
Progression
The research proposal has highlighted the research question more detailed, which is progress that guides getting a detailed outcome. The annotated literature about the research question has been obtained that showcases the impact of illicit drugs taken by the parents. The literature highlights the challenge and how the children result in being hosted in protective services and the failure of the parents to play their roles.
Scholarly sources
According to Hardy et al. (2018), it found that women with ...
Human Clinical Relevance of Developmental and Reproductive Toxicology and Non...Joseph Holson
Presented at Forest Research Institute, May 13, 2004.
Abstract: Experimental animal models are essential to product development and toxicologic screening. The effective use of such models is dependent on the attributes of: validity, sensitivity, reproducibility, and practicability. For the two endpoints of toxicity of most societal concern, developmental effects, and cancer, experience has taught that differences between animals and humans in drug absorption, distribution, metabolism and elimination most often leads to differences in response both qualitatively, and quantitatively. In developmental toxicology, a high degree of concordance between experimental animal results and human outcomes has been demonstrated. Human reproductive outcomes are often concordant with experimental animal data, but this concordance seems to vary more among species as phenotypes diversify with approaching sexual maturity and subsequent reproductive senescence. This increase in phenotypic diversity also presents difficulties in a priori selection of animal models in non-clinical juvenile toxicity testing. Juvenile periods among species can be divided into pre-term neonatal, neonatal, infancy, childhood and adolescence, based on overall central nervous system and reproductive development. However, because physiologic time differs among species, temporality of target-organ maturation should be reconciled with the human pediatric therapeutic scenario prior to animal model selection. The heuristic impact and resultant guidance for proper selection and use of animal models for juvenile toxicity testing will be demonstrated through the use of case studies involving angiotensin-converting enzyme (ACE) inhibitors, quinilones, fluoxetine and isotretinoin.
Mandatory Reporting and Neglect: Impacts and IssuesBASPCAN
New directions in child protection and well-being: making a real difference to children's lives.
Prof Bob Loone,Queensland University of Technology Brisbane, Australia
Prof Brid Featherstone, The Open University, Milton Keynes, England.
Prof Maria Harries, University of Western Australia, Perth Australia
Prof Mel GrayUniversity of Newcastle, Newcastle, Australia
Healthy Minds: A Randomised Controlled Trial to Evaluate PHSE Curriculum Deve...cheweb1
CHE Seminar presentation 16 January 2020, Alistair McGuire, Department of Health Policy, LSE. Evaluating the Healthy Minds program: The impact on adolescent’s health related quality of life of a change in a school curriculum
Baker what to do when people disagree che york seminar jan 2019 v2cheweb1
Public values, plurality and health care resource allocation: What should we do when people disagree? (..and should economists care about reasons as well as choices?) CHE Seminar 21 January 2019
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
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1. Within-family Mendelian
Randomization
In press at NatureCommunications
York University 12th June 2020
Neil Davies, neil.davies@bristol.ac.uk
Ben Brumpton*1,2,3, Eleanor Sanderson2,4, Fernando Hartwig2,5, Sean Harrison2,4, Gunnhild Åberge Vie1,
Yoonsu Cho2,4, Laura D Howe2,4, Amanda Hughes2,4, Dorret I Boomsa6, Alexandra Havdahl2,7,8, John
Hopper9, Michael Neale10, Michel G Nivard6, Nancy L Pedersen11, ChandraRenyolds12, Elliot M Tucker-
Drob13, Andrew Grotzinger,13 Laruence Howe2,4, Tim Morris2,4, Shuai Li14,15, MR within-family Consortium,
Wei-Min Chen16, Johan Håkon Bjørngaard1,KristianHveem1, Cristen Willer17,18,19, David M Evans2,20, Jaakko
Kaprio21,22, George Davey Smith2,4,^, Bjørn Olav Åsvold1,23^, Gibran Hemani2,4,^, Neil M Davies2,4,^
1 K.G.Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU,
Norwegian University of Science and Technology, Norway.
2 Medical Research Council IntegrativeEpidemiology Unit, University of Bristol,BS8 2BN,United Kingdom.
3 Clinic of Thoracic and OccupationalMedicine, St. Olavs Hospital, Trondheim University Hospital.
https://www.biorxiv.org/content/10.1101/602516v1
2. • Questions very welcome!
• Please feel free to interrupt at any time, I’m happy to clarify, discuss,
debate, whatever.
3. Overview
1. Introduction to Mendelian randomization (genetic IVs)
2. Family based designs
3. Simulations
4. Empirical example: The effects of height and BMI on education,
blood pressure and diabetes
5. The Sibling GWAS
4. Why classical epidemiology failed
A step-by-step guide to classical epidemiology
1. Get sample (e.g. civil servants, doctors,
women)
2. Measure risk factor
3. Either estimate
• Cross sectional associations with disease
• Longitudinal associations with disease
4. Publish in NEJM
5. Spend $$$ in RCT, -> fails to replicate
6. Pick another risk factor
6. RCTs did not replicate this finding
Why? Endogeneity, confounding, and measurement error.
G. Davey Smith, M. V. Holmes, N. M. Davies,S. Ebrahim,Mendel’s laws,Mendelian randomization and causal inferencein observational data:substantiveand nomenclatural issues.EurJ Epidemiol. 35, 99–111 (2020).
7. Econometrics to the rescue?
• Classic epi – adjust measure
confounders
• Impossible to fully measure covariates
• Need to estimate causal effects even if
there are unmeasured confounders of
the exposure-outcome relationship
• Instrumental variables and natural
experiments could help
8. The instrumental variable
assumptions
The instrumentalvariableassumptions:
1. Relevance:The instrument associateswith the exposure of interest
2. Independence:There are no confoundersof the instrument-outcomeassociation
3. Exclusion restriction: The instrument only affects the outcome via the exposure
P. Wright, Letter from Philip Wrightto Sewall Wright, 4 March 1926., (availableat
https://ase.tufts.edu/economics/documents/wrightPhilipAndSewall.pdf).
J. D. Angrist, G. W. Imbens, D. B. Rubin,Identification of causal effects usinginstrumental variables.JAm Stat Assoc. 91, 444–45
(1996).
J. Pearl,Causality: models, reasoning, and inference (Cambridge University Press,Cambridge,U.K. ; New York, 2000).
M. A. Hernán, J. Robins,Instruments for causal inference:an epidemiologist’s dream?Epidemiology. 17, 360–372 (2006).
and many, many others……
Instrument Exposure Outcome
Confounder
9. Mendelian randomization=Genetic lotteries
• DNA is randomly transmitted from
parents to offspring
• Germline DNA is not affected by the
environment
• The human genome:
• ~3 billion base pairs (A, C, G, or T)
• Over 650 million variants
• 15 million common variants (minor
allele frequency >1%)
• Sequencing vs genotyping
M. Katan,Apoupoprotein E Isoforms,Serum Cholesterol,And Cancer. The Lancet. 327, 507–508 (1986).
10. Random genetic inheritance
• Genetic variants
• Are a point in the genome that differs across the population
• A common type of variant is single nucleotide polymorphisms (SNPs)
• SNPs have one or more alleles
• A conception offspring inherit at each SNP
• One of mother’s two alleles
• One of father’s two alleles
G. Davey Smith, S. Ebrahim,“Mendelian randomization”:can genetic epidemiology contribute to understandingenvironmental
determinants of disease? Int J Epidemiol. 32, 1–22 (2003).
11. Nature’s randomized trials
G. Davey Smith, S. Ebrahim,What can mendelian randomisation tell us aboutmodifiablebehavioural and environmental exposures ?
BMJ. 330, 1076–1079 (2005).
12. Genetic variants as instrumental variables
The instrumentalvariableassumptions:
1. Relevance:SNPs associate with risk factors
2. Independence:SNPs are randomly allocatedat conception
3. Exclusion restriction: SNPs tend to be inherited independentlyof SNPs for other traits
(Mendel’s law of independentassortment)
G. Davey Smith, D. A. Lawlor,R. Harbord,N. Timpson, I. Day,S. Ebrahim,Clustered environments and randomized genes: a
fundamental distinction between conventional and genetic epidemiology.PLoS Med. 4, e352 (2007).
BMI SNPs BMI Education
Confounder
13. Mendelian randomization: a step-by-step guide
1. Define hypothesis
• E.g. does BMI affect educational attainment?
2. Select genetic variants from GWAS
• which SNPs associate with BMI? Clump + threshold at p<5×10-08
3. Estimate effect of exposure on outcome
• One sample IV estimators, 2SLS, structural mean models, weak instrument
robust methods, polygenic scores
• Two-sample instrumentalvariable estimators
• Pleiotropy robust methods - IVW, MR-Egger, weighted median
4. Sensitivity analyses N. M. Davies,M. V. Holmes, G. Davey Smith, Reading Mendelian randomisation studies:a guide,glossary,and checklist
for clinicians.BMJ, k601 (2018).
G. Hemani, J. Bowden, G. Davey Smith, Evaluatingthe potential roleof pleiotropy in Mendelian randomization studies.
Human Molecular Genetics. 27, R195–R208 (2018).
14. Genome-wide association studies (GWAS)
• Estimate association between phenotype and SNPs across the
genome
• Up to 40 million variants
• Linear or logistic regression
• Covariates
• Sex
• Age
• Principal componentsof genetic variation (PCs)
• Samples of unrelated individuals (less than 3rd degree relatives)
• Large databases of GWAS estimates available (e.g. MR-Base)
G. Hemani, J. Zheng, B. Elsworth,K. H. Wade, V. Haberland,D. Baird,C. Laurin,S. Burgess, J. Bowden, R. Langdon, V. Y. Tan, J.
Yarmolinsky,H.A. Shihab,N. J. Timpson,D. M. Evans,C. Relton, R. M. Martin,G. Davey Smith, T. R. Gaunt, P. C. Haycock, The MR-Base
platformsupports systematic causal inferenceacrossthehuman phenome. eLife. 7 (2018), doi:10.7554/eLife.34408.
17. Comparison of ROSLA and
MR estimates of effect of
educationon a range of
phenotypes in the UK
Biobank.
MR in unrelatedindividuals
suggests educationaffects
height.
N. M. Davies,M. Dickson,G. Davey Smith, F.
Windmeijer,G. J. van den Berg, The effect of
education on adultmortality,health, and income:
triangulatingacrossgenetic and policy reforms
(2018), doi:10.1101/250068.
18. • Used samples of unrelated individuals
• Controlled for standard covariates (age, sex, PCs)
• Requires assumption that the height and BMI genetic variants are
randomly distributed across the population
• There are reasons to think this may not hold:
• Fine scale population structure (Haworth et al 2019, Abdellaoui et al 2019)
• Dynastic effects (Plomin and Bergeman 1991)
• Assortative mating (Wright 1921)
A. Abdellaoui,D.Hugh-Jones, L. Yengo, K. E. Kemper, M. G. Nivard,L. Veul, Y. Holtz, B. P. Zietsch,T. M. Frayling,N.R. Wray,J. Yang, K. J. H. Verweij, P. M. Visscher,Genetic correlates of social
stratification in GreatBritain. Nature Human Behaviour (2019),doi:10.1038/s41562-019-0757-5.
S. Haworth, R. Mitchell,L. Corbin,K. H. Wade, T. Dudding, A. Budu-Aggrey, D. Carslake,G.Hemani, L. Paternoster, G. D. Smith, N. Davies,D. J. Lawson, N. J. Timpson, Apparent latent structure
within the UK Biobank samplehas implicationsfor epidemiological analysis. Nature Communications. 10 (2019), doi:10.1038/s41467-018-08219-1.
R. Plomin,C. S. Bergeman, The nature of nurture: Genetic influenceon “environmental” measures.Behavioral and Brain Sciences. 14, 373–386 (1991).
S. Wright, Systems of Mating. III.AssortativeMatingBased on Somatic Resemblance. Genetics. 6, 144–161 (1921).
N. M. Davies,L. J. Howe, B. Brumpton, A. Havdahl,D. M. Evans, G. Davey Smith, Within family Mendelian randomization studies .Human Molecular Genetics. 28, R170–R179 (2019).
L.-D. Hwang, N. M. Davies,N. M. Warrington,D. M. Evans,Integrating Family-Based and Mendelian Randomization Designs. Cold Spring Harb Perspect Med, a039503 (2020).
19. 1) Fine scale population structure
• Geographic or regionaldifferences in allelefrequency that relate to a trait of interest
• For example:
• People in Scotlanddrink more Irn Bru and haveadverse health outcomes.
• Some genetic variantswill also have modestly different frequencies in Scotland
• E.g. genetic variantsassociated with lactase persistence are more common in
northern areas
• This does not imply that Iru Bru causes adverse health outcomes
G. Davey Smith, D. A. Lawlor,N. J. Timpson, J.Baban, M. Kiessling,I.N. M. Day, S. Ebrahim,Lactase
persistence-related genetic variant:population substructureand health outcomes. Eur J Hum Genet.
17, 357–367 (2009).
20. 2) Dynastic effects
• Family structure:
• dynastic effects that occur when the expression of parent’s
genotype directly affects the offspring phenotype.
• For example, if more educatedparents can afford tutoring for
their children, leadingto better educational outcomesfor their
offspring
• Parents and offspring genotypes correlate 50%
• Results in biased estimatesof the effect of exposure in the
offspring
21. 3) Assortative mating
• Assortative mating - when individualsdo not choose their partners at random but select
someone who is more similarto them on particularcharacteristicsthan would be
expected by chance.
• Assortment on education,BMI
and height
• Causes bias in MR estimates
F. P. Hartwig, N. M. Davies,G. Davey Smith, Bias in Mendelian randomization dueto assortativemating.
Genetic Epidemiology. 42, 608–620 (2018).
22. Econometric methods
• Consider the following model :
𝑥 𝑘,𝑖 = 𝛾0 + 𝛾1 𝑔 𝑘,𝑖 + 𝐶 𝑘,𝑖 + 𝑓𝑘 + 𝑣 𝑘,𝑖
𝑦 𝑘,𝑖 = 𝛽0 + 𝛽1 𝑥 𝑘,𝑖 + 𝐶 𝑘,𝑖 + 𝑓𝑘 + 𝑢 𝑘,𝑖
Where:
𝑦 𝑘,𝑖 and 𝑥 𝑘,𝑖 are the outcome and exposure for individual 𝑖from family 𝑘.
𝑔 𝑘,𝑖 is a set of genetic variantsthat are associated with the exposure.
𝐶 𝑘,𝑖 is a confounder of the associationof the exposure and the outcome.
𝑓𝑘 is a family level confounder.
𝑢 𝑘,𝑖 and 𝑣 𝑘,𝑖 are random error terms.
𝛽1 is the effect of the exposure on the outcome which we wish to estimate.
This means that Mendelianrandomizationusing data from unrelatedindividualswould
produce a biased estimate of 𝛽1 due to the correlationbetween 𝑔 𝑘,𝑖,𝑗 and 𝑓𝑘.
23. Econometric methods
• Difference-in-differencemethod with sibling data.
• For any pair of siblings within family 𝑘, indicated 𝑘, 1 and 𝑘, 2, the genotypic difference at
genetic variant 𝑗 is:
𝛿 𝑘,𝑗 = 𝑔 𝑘,1,𝑗 − 𝑔 𝑘,2,𝑗
The associationbetween the genotypic differences and phenotypicdifferences in the
exposure, 𝑥, and outcome 𝑦, for SNP 𝑗 can be estimated via:
𝑥 𝑘,1 − 𝑥 𝑘,2
2
= 𝛾𝑗 𝛿 𝑘,𝑗
2
+ 𝑢 𝑘,𝑗
𝑦 𝑘,1 − 𝑦 𝑘,2
2
= Γ𝑗 𝛿 𝑘,𝑗
2
+ 𝑣 𝑘,𝑗
The estimated associations, 𝛾𝑗 and Γ𝑗, can be used with any summary level Mendelian
randomization estimator.
The within transformation – useful for large sample sizes.
24. Econometric methods
• Family fixed effect with sibling data.
• Alternatively,we can estimate the associationsusing familyfixed effects indicatedby 𝑓𝑘
for each family:
𝑥 𝑘,𝑖 = 𝛾0
+ 𝛾1,𝑗 𝑔 𝑘,𝑖,𝑗 + 𝑓𝑘 + 𝑢 𝑘,𝑖,𝑗
𝑦 𝑘,𝑖 = 𝛽0
+ Γ1 𝑔 𝑘,𝑖,𝑗 + 𝑓𝑘 + 𝑣 𝑘,𝑖,𝑗
This estimatoraccountsfor any differences between families, which includes any effect of
assortative mating or dynastic effects common to all siblings.
The estimated associations, 𝛾𝑗 and Γ𝑗, can be used with any summary level Mendelian
randomization estimator.
25. Econometric methods
• Adjusting for parentalgenotype with mother-father-offspringtrios data.
• The estimatesof the SNP-exposure and SNP-outcome associationsfor each child can be
adjustedfor their mother’s and father’s genotypes, indicatedby 𝑔𝑖𝑚,𝑗 and 𝑔𝑖𝑓,𝑗
respectively:
𝑥𝑖 = 𝛾0
+ 𝛾1,𝑗 𝑔𝑖,𝑗 + 𝛾2,𝑗 𝑔𝑖𝑚,𝑗 + 𝛾3,𝑗 𝑔𝑖𝑓,𝑗 + 𝑢𝑖,𝑗
𝑦𝑖 = 𝛽0
+ Γ1 𝑔𝑖,𝑗 + Γ2 𝑔𝑖𝑚,𝑗 + Γ3 𝑔𝑖𝑓,𝑗 + 𝑣𝑖,𝑗
These associationscan be used to estimate the effect of the exposure on the outcome using
summary dataMendelianrandomizationmethods.
27. Results
• Simulations
• Bias occurs if there are dynastic effects.
I.e. if the parentsaffect the offspring
outcomes.
• However, estimatesfrom within-family
designs are less substantiallyless
powerful.
• The simulationsshow how family
structure can be exploitedto control for
the bias either using samples of siblings
or mother-father-offspring trios.
28. Empirical study
• Hypotheses
• What is the effect of BMI on
1. Diabetes
2. High blood pressure
3. Educational attainment
• What is the effect height on
4. Educational attainment
29. Data• HUNT
• HUNT > ~125,000 unique individuals(H1-3)> ~71,800 genotyped (H2-3) > ~24,000 unrelated (2nd degree)
Europeans.
• Genotyping - HumanCoreExome12 v1.0, HumanCoreExome12 v1.1 and UM HUNT Biobankv1.0
(n=516,608).
• Imputation– merged reference panel constructed from the HaplotypeReference Consortium (HRC) panel
(release version 1.1) and a local reference panel
• Empiricalstudy (HUNT+UKB)
• HUNT2 > 65,237 participated> 56,374 genotyped > 53,288 complete data > 19,492 unrelated| 28,823
siblings> 13,103 families
• UKBB > 503,317 participated> 370,180 met inclusion criteria > 33,642 siblings
Exposuresand outcomes
• Height, BMI > Education
• BMI > Diabetes, Blood pressure
Replication:23andMe 222,368 siblings
30. BMI and height GWAS
Clumped using r2<0.01, LD=10,000kb, to select:
• 79 SNPs associated with BMI
• 385 SNPs associated with height
35. Summary
• Meta-analysisof HUNT, UKB and 23andMe
• A 1kg/m2 increase in BMI causes:
• 0.82 (95%CI: 0.55 to 1.06) additional cases of diabetes per 100
• 1.25 (95%CI: 0.90 to 1.59) additional cases of high blood pressure per 100
• 0.00 (95%CI: -0.018 to 0.018) additional years of education (i.e. <6.6 days)
• 10cm increase in height causes
• 0.00 (95%CI: -0.015 to 0.015) additional years of education (i.e. <5.5 days)
• Very well powered estimates.
• Confirm established adverse effectsof higher BMI on health outcomes.
• There is very unlikely to be meaningful causal effect of BMI or height on
educational attainment.
36. Next steps: MR within families consortium
• a. Within siblings GWAS
• Runningwithin sib and within families (trio)analysis to investigate the difference in genetic associations in unrelated individuals
and related individuals across a range of traits and studies.
• b. Assortative mating over time and across countries
• Estimate assortativematingacross time and in different countries.Will require data on spouses and phenotype data.
• c. Non-inherited variants GWAS
• Estimatingdynasticand parent oforigin effects usingtrios or duos.This approach would allowus to investigate the
intergenerationaltransmission ofa range of traits.
• d. Assortative mating and obesity
• There’s been several interestingpapers thathavesuggested that the change in obesity,particularlythe increase in the variance of
BMI, could be explained byassortativemating.There havebeen some studies into this,but relativelyfewusingmolecular genetic
data.The studies involvedcould provide newevidence about this hypothesis.
37. Next steps: MR within families consortium
• Included studies:
• Finnish Twin Cohort
• Chinese NationalTwin Registry
• Swedish Twin Registry
• Texas Twin Project
• QIMR
• Murcia Twin Registry
• NTR
• Australian MammographicDensityTwins and Sisters Study
• Italian Twin Registry
• Minnesota Center for Twin and Family Research
• Osaka UniversityTwin Registry
• LongitudinalStudyofAging Danish Twins
• GenerationScotland
• UK Biobank
• TwinsUK
• HUNT
• Framingham Heart Study
• ALSPAC
• The HealthyTwin Study (Korea)
• TEDS
• QNTS
• Exeter Family Studyof ChildhoodHealth (EFSOCH)
• Mid-Atlantictwin reg
• MoBa
• Born in Bradford (duos)
• Long Life Family Study
• Inclusion criteria – relateds (duos,trios,siblings).
38. Within-families consortium
• Collaborative consortium effort for projects using
family data.
• Includes family studies and large population biobanks
(e.g. UK Biobank has ~20K sibling pairs).
• Main project: Sibling GWAS of 30+ complex traits.
• Fit conventional and within-family models for
comparison.
39. Sibling GWAS
• To date summary data on ~137,000 siblings, expect to reach
180,000+.
• High coverage of phenotypes although sample sizes vary.
Study Max number of siblings
UK Biobank 40,210
HUNT 38,549
Generation Scotland 19,914
Netherlands Twin Registry 4,708
FinnTwin 8,810
TEDS 4,224
China Kadoorie Biobank 13,856
Aging Danish Twins 1,172
Viking 930
Orcades 837
TwinsUK 2,806
Australian Mammographic Study 1,811
Total 137,827
40. Genetic association estimates decrease
Phenotype Number of SNPs Shrinkage estimate in comparison of
conventional and within-familymodels
(95% C.I.)
Height 385 9.0% (6.7%, 11.2%)
Educational attainment 53 38.7% (23.1%, 54.3%)
Ever smoking 92 17.5% (5.3%, 29.7%)
41. Evidence of heterogeneity across studies
Study N GWS shrinkage estimate (95% C.I.)
UK Biobank 40,068 13.1% (9.4%, 16.2%)
HUNT 37,689 0.8% (-3.3%, 4.9%)
Generation Scotland 19,904 12.4% (7.5%, 17.4%)
Meta-analysis 121,719 9.0% (6.7, 11.2%)
e.g. Height variants
42. Educational attainment more consistent
Study N GWS shrinkage estimate (95%
C.I.)
UK Biobank 39,531 48.1% (29.5%, 66.6%)
HUNT 32,120 29.2% (-0.2%, 58.6%)
Generation Scotland 19,589 56.2% (21.1%, 91.3%)
Meta-analysis 104,316 38.7% (23.1%, 54.3%)
43. MR for Health Economics
• No time, but may be of interest to health economists…
44. Conclusions
• Familialeffects can bias SNP-phenotype associations
• These effects can bias genetic approachessuch as Mendelian
randomization.
• We demonstratedhow family structure can be used to control
for these effects either using samples of siblingsor mother-
father-offspring trios.
• However, estimatesfrom within-familyMendelian
randomization areless precise than estimates using unrelated
individuals.
• In samples from HUNT, UK Biobankstudies and 23andMe, we
found that the effects of height and BMI on educational
attainmentalmost entirely attenuated afterallowingfor a
family fixed effects, whereas the effects of BMI on the risk of
diabetesand high bloodpressure were similar when allowing
for family effects.
MR
Davey Smith et al. 2003
45. Conclusions
• While allowing for family fixed effects or using difference-in-difference estimatorswill account
for dynastic effects or assortative mating, these methods will not address bias due to
violationsof the second Mendelianrandomizationassumption.
• Use these estimatorswith the summary data methods (MR-Egger, weighted median and
mode).
• Any one study is likely to be underpowered to use both within family methods and pleiotropy
robust methods.
• Therefore, a consortium of family based studies was required, this gives sufficient power to
use both within family and pleiotropyrobust methods.
• Currently running sibling GWAS in just under 200,000 siblings…. watch this space!
• https://www.biorxiv.org/content/10.1101/602516v1
46. Acknowledgements – co-authors
Bristol/MRC IEU
• Laurence Howe
• George DaveySmith
• Gib Hemani
• Tim Morris
• Amanda Hughes
• EleanorSanderson
• Sean Harrison
• Yoonsu Cho
• Laura Howe
University of Queensland
• David Evans
University of Pelotas
• Fernando Hartwig
23andMe Research Team
• Karl Heilbron
• AdamAuton
NTNU
• Ben Brumpton
• GunnhildÅberge Vie
• Johan Håkon Bjørngaard
• Bjørn Olav Åsvold
• Cristen Willer
• Kristian Hveem
NIPH
• Alexandra Havdahl
Vrije Universiteit Amsterdam
• Dorret I Boomsma
• Michel G Nivard
Oxford University
• FrankWindmeijer
The University of Melbourne
• John Hopper
• Shuai Li
Virginia Commonwealth University
• Michael Neale
Karolinska Institutet
• Nancy L Pedersen
University of California Riverside
• Chandra A Reynolds
University of Texas at Austin
• Elliot M Tucker-Drob
• AndrewGrotzinger
University of Virginia
• Wei-Min Chen
University of Helsinki
• Jaakko Kaprio
47. Acknowledgements – funding
The Medical Research Council (MRC) and the UniversityofBristol support the MRC Integrative EpidemiologyUnit [MC_UU_12013/1,
MC_UU_12013/9, MC_UU_00011/1]. NMD is supported byan Economics and Social Research Council (ESRC) Future Research
Leaders grant [ES/N000757/1] and a Norwegian Research Council Grant number 295989. JHB was funded bythe Norwegian Research
Council with grant number 295989. DME is funded by a National Health and Medical Research Council Senior Research Fellowship
(1137714). EMTD was supported byNIH grants R01AG054628 and R01HD083613, and by the Jacobs Foundation.LDH is supported by
a Career Development Award from the UK Medical Research Council (MR/M020894/1). This work is part of a project entitled ‘social
and economicconsequences of health:causal inference methods and longitudinal,intergenerationaldata’,which is part of theHealth
Foundation’s Social and EconomicValue of Health Research Programme (Award 807293). The Health Foundationis an independent
charitycommitted to bringingabout better health and healthcare for people in the UK. GAV is supported bya Norwegian Research
Council grant code 250335. CAR receives support from the NationalInstitutes ofHealth (NIH) includingR01AG060470, R01AG059329,
R01AG058068, R01AG018386, and R01AG046938. NLP receives fundingfrom the National Institutes ofHealth Grants No.
R01AG060470, R01AG059329. The Nord-TrøndelagHealth Study(The HUNT Study) is a collaborationbetween HUNT Research Center
(Faculty of Medicine and Health Sciences, NTNU,Norwegian UniversityofScience and Technology),Nord-TrøndelagCountyCouncil,
Central NorwayRegional Health Authority,and the Norwegian Institute ofPublic Health.The K.G. Jebsen Center for Genetic
Epidemiologyis funded byStiftelsen Kristian Gerhard Jebsen;Facultyof Medicine and Health Sciences, NTNU; The Liaison Committee
for education,research and innovation in CentralNorway;and the Joint Research Committee between St. Olavs Hospital and the
Faculty of Medicine and Health Sciences, NTNU.The genotypingin HUNT was financed by the National Institute ofHealth (NIH);
UniversityofMichigan; The Research Council of Norway;The Liaison Committee for education,research and innovation in Central
Norway; and the Joint Research Committee between St. Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. JK
has been supported bythe Academyof Finland (grants 308248, 312073). RMF and RNB are supported bySir Henry Dale Fellowship
(Wellcome Trust and Royal Societygrant:WT104150). GH is supported bytheWellcome Trust and Royal Society[208806/Z/17/Z]. AH
was funded by the South-EasternNorwayRegional Health Authority,grants 2018059 and 2020022.