Distinguishing “genetic correlation” from “causation” — across 52 diseases and complex traits

Okay, this topic is a bit intense (dense?), so stay with me on this one. The topic has to do with pleiotropy — when a gene, or a variant of one gene, causes two or more phenotypes (traits). This phenomenon is often seen in complex diseases. This phenomenon is also seen in response to an environmental toxicant (e.g. cigarette smoke) or response to a drug (think of all the side effects produced by e.g. gabapentin used to treat nerve pain). How can the effects of a single-nucleotide variant (SNV) be “divvied up” among many outcomes (traits)?

Mendelian randomization (MR) is widely used to identify potential causal relationships among heritable traits. MR allows you to test for — or, in certain cases, to estimate — a causal effect from observational data, when the study includes a number of confounding factors (as it almost always does). MR uses common genetic polymorphisms (inter-individual functionally silent differences in DNA sequence that make each human’s genome unique) with well-understood effects on exposure patterns (e.g. propensity to smoke cigarettes, or to drink alcohol) on effects that mimic those produced by modifiable exposures (e.g. filtered vs unfiltered cigarettes). Most important, the genotype (DNA sequence) must only affect the disease status indirectly via its effect on the exposure being studied.

During meiosis (i.e. formation of ova and sperm), genotypes are assigned randomly — when passed from parents to offspring. If it is assumed that choice of mate is not associated with genotype, then the population genotype distribution should be unrelated to the confounding factors that typically plague observational epidemiology studies. In this regard, MR can be thought of as a “natural” randomized controlled trial. Because “the polymorphism” is “the instrument”, MR is dependent on genome-wide association studies (GWAS) having provided good candidate genes that might explain the response to risk exposure.

From a statistical perspective, MR is an application of the technique of instrumental variables — with “genotype” acting as an instrument for the exposure being studied. MR is based on several assumptions: [a] that there is no direct relationship between the instrument and the dependent variable; and [b] that there are no direct paths between the instrument and any potential confounding factors. In addition to direct effects of “the instrument” on the trait (which can mislead the epidemiologist), confusing conclusions may also arise in the presence of: linkage disequilibrium (non-random association of alleles at different loci in a given population) with unmeasured directly-causal variants; genetic heterogeneity; pleiotropy (one gene responsible for two or more seemingly unreleated traits) often detected as a genetic correlation); or population stratification (differences in allele frequencies between a study group, and a control group, due to systematic differences in ancestry rather than association of genes with disease).

SNVs that are significantly associated with one trait (exposure; e.g. amount of cigarette-pack-years in smokers), can be used as genetic instruments to test for a causal effect on a second trait (outcome; e.g. smoking-induced lung cancer). If the exposure is causal, then SNVs affecting “the exposure” should affect “the outcome” proportionately. For example, low-density lipoprotein (LDL) and triglycerides — but not high-density lipoprotein (HDL) — causally affect coronary artery disease risk. However, pleiotropy presents a challenge for MR, especially when the exposure is highly polygenic (caused by hundreds or thousands of genes).

Authors [see attached] introduce a Latent Causal Variable (LCV) Model, under which the genetic correlation between two traits is mediated by a latent variable having a causal effect on each trait. Trait 1 is defined as a phenotype that is partially genetically causal for Trait 2, when it is strongly correlated with the causal variable (Trait 1 = high LDL levels; Trait 2 = coronary artery disease risk). Authors quantify partial causality using the genetic causality proportion (GCP) (i.e. correlation between the genetic influences on a trait and the genetic influences on a different trait, estimating the degree of pleiotropy or causal overlap). In simulations [see attached], authors show that LCV has major advantages over MR.

Authors show that LCV is able to avoid false positives (i.e. when one concludes incorrectly that a particular condition or attribute is present, but it really is not) due to genetic correlations — unlike MR. Across 52 traits (N = 331,000 = average population per trait), authors identified 30 causal relationships that had high GCP estimates. Novel findings in this study include a causal effect of LDL on bone mineral density, consistent with clinical trials of statins in which osteoporosis is a side-effect.

Nat Genet Dec 2o18; 50: 1728–1734

COMMENT: This last sentence “Novel findings in this study include a causal effect of LDL on bone mineral density, consistent with clinical trials of statins in which osteoporosis is a side-effect” — seems to imply that osteoporosis is a side-effect of statins use. Actually, what has been observed is that statins improve bone mineral density; therefore, if anything, statins might be useful in treating osteoporosis (more studies are needed here, because apparently any strong association with prevention of fracture risk has not yet been demonstrated). Still, osteoporosis is not a side-effect of statins use, but rather the opposite: osteogenesis (build-up of bone) appears to be a (beneficial) effect of statins.

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Whole-genome sequencing of 175 Mongolians uncovers population-specific genetic architecture and gene flow throughout North and East Asia

From time to time, these GEITP pages focus on the evolution of modern human, including the Great Diaspora (i.e. several waves of Homo sapiens originating in southeast Africa and migrating to Asia and Europe, along with some ‘back-migrations’). As humans moved to a new ecological niche, the new environment (plants, food, weather) affected their genomes; hence, gene-environment interactions. The topic of the attached article comprises the ~10 million ethnic Mongolians that currently inhabit a wide geographical range — which includes present-day Mongolia, northern China, southern Russia, and other neighboring countries. It has been suggested that the first permanent settlers of North America had migrated from Mongolia perhaps 24,000-22,000 years ago (when the warming climate, at that time, had decreased the amount of ice to allow passage).

Mongolians also played a pivotal role in shaping the culture and genetic make-up of modern Eurasia through rapid expansion of the Mongolian Empire around 1,000 years ago. Genghis Khan and his successors spread the Mongolian Empire across Asia and Eastern Europe in the 13th century (yes, there was a lot of ‘admixture’ hanky-panky going on), controlling the largest contiguous empire (16% of Earth’s total land-mass) in the history of the world (the attached article shows a nice map of this). Whereas the historical aspects of the Mongolian Empire are well documented, little is known about the genetic architecture (refers to the underlying genetic basis of a each and every trait and its variational properties due to segration of each gene’s alleles) within today’s Mongolian populations, nor is it known how those populations have influenced (or have been influenced by) the genetics of other peoples from the region and the world.

Authors [see attached article] generated a new genetic variation reference panel by whole-genome sequencing (WGS) of 175 ethnic Mongolians — representing six tribes. The catalogued variation in the panel shows strong population stratification (refers to differences in allelic frequencies between a study population, and a control population, due to systematic differences in ancestry — rather than association of genes with disease) among these tribes, which is correlated with the diverse demographic histories in the region. Incorporating these results with the 1000 Genomes Project panel identifies derived alleles that are shared between Finns and Mongolians/Siberians, suggesting that substantial gene flow between northern Eurasian populations has occurred in the past ~1200 years. Furthermore, authors point out that North, East, and Southeast Asian populations are more aligned with each other — than these groups are with South Asian and Oceanian populations.

Nat Genet Dec 2o18; 50: 1696–1704

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Mitochondrial genetic medicine. A Perspective by Doug Wallace

Doug Wallace is a pioneer of the mitochondrial genome. I first met him at Stanford in the late 1970s and tried several times to entice him to speak at the Univ of Cincinnati in the 1990s and 2000s (never happened, he was always too much in demand; Doug is an excellent speaker and a great writer, as one can see from his attached review). As we all know, almost all cells of animals contain a nucleus and cytoplasm. The nucleus contains our nuclear DNA (nDNA; genome) on chromosomes. The cytoplasm contains many different subcellular components, perhaps the most important of which are mitochondria (considered the “powerhouse of the cell” because it generates most of our energy). Often overlooked is the mitochondrial genome — with its own set of genes, which of course are also subject to single-nucleotide variants (SNVs) and deleterious mutations, leading to diseases.

The worldwide incidence of type-2 diabetes, obesity, and autism (‘complex diseases’) is rising, and age-related diseases such as Alzheimer disease, Parkinson disease, and cancer are increasing as the population ages. Large amounts of money, time and effort are being spent, in an attempt to understand and treat these diseases, but have met with limited success to date. Perhaps some of the difficulty lies in the assumptions that organ-associated symptoms are the result of organ-specific defects, and that those clinically relevant genes exist only in nDNA. It is becoming increasingly realized that organ-specific symptoms can result from systemic mitochondrial bioenergetic defects — and some of the most important mitochondrial genes are involved. Doug Wallace has been beating this drum for more than three decades.

The eukaryotic cell (i.e. containing pairs of chromosomes) arose during evolution via amalgamation of two separate life forms: an archaeon (group of microorganisms that resemble bacteria but are different from them in their genetic makeup and certain aspects of cell structure) that gave rise to the nucleus and cytosol — and an α-proteobacterium (major phylum of gram-negative bacteria — which include a wide variety of pathogens, such as Escherichia, Salmonella, Vibrio, Helicobacter, Yersinia, Legionellales and many other notable genera) that gave rise to the mitochondrion. Over 2.5 billion years of eukaryotic-cell evolution, the nucleus became specialized in encoding the anatomical elements of the cell, whereas the mitochondrion became specialized in encoding the core energy elements.

The nDNA encompasses ~20,000 anatomical genes, which include the structural proteins for the mitochondrion and enzymes for mtochondrial DNA (mtDNA) maintenance and expression. The mtDNA contains only 13 polypeptide genes, but they are present in hundreds to thousands of copies per cell (whereas the nDNA comprises one-half the genes from each parent, the mtDNA almost always comes from the female). The mtDNA polypeptides are central electron- and proton-transport proteins for the mitochondrial energy-generating process, oxidative phosphorylation (OXPHOS); mtDNA can be thought of as “the wiring diagram of the mitochondrial power plant.” OXPHOS generates ~90% of the body’s energy by oxidizing hydrogen atoms from food with the oxygen breathed in, thus generating water, via the electron-transport chain (NADH–complex I (or succinate complex II)–coenzyme Q–complex III–cytochrome c–complex IV–oxygen). The energy — released as electrons, traverses complexes I, III, and IV — and is used to pump protons out across the mitochondrial inner membrane, thereby generating an electrochemical gradient that acts as a capacitor. The potential energy of this capacitor can drive complex V (ATP synthase) to condense ADP and inorganic phosphate, thereby forming ATP used for energy.

Specifically, maternally inherited mtDNA contains 37 critical bioenergetic genes, present in hundreds of copies per cell, but the ‘mitochondrial genome’ encompasses an additional 1,000–2,000 nDNA mitochondrial genes; the interaction between these two mitochondrial genetic systems provides explanations — for phenomena such as the non-Mendelian transmission of common complex diseases, age-related disease risk and progression, variable penetrance and expressivity, and gene–environment interactions (gee, where have I heard this term before?). Therrefore, mtDNA genetics contributes to the quantitative and environmental components of human genetics that cannot be explained by Mendelian genetics. Because mtDNA is maternally inherited and cytoplasmic, it clinically has fostered the first germline gene therapy, nuclear transplantation. However, effective interventions are still lacking for existing patients with mitochondrial dysfunction. These GEITP pages believe that more attention should be made to the ‘mitochondrial genome’. 🙂


Nat Genet Dec 2o18; 50: 1642–1649

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The Perfect Dose

This GEITP emailing is a bit unusual because it is a recommendation of a book on the topic of “gene-environment interactions.” Right here in the University of Cincinnati College of Medicine, we have a physician-scientist –– who somehow has “enough time on his hands” to write a book of fiction called “The Perfect Dose,” which can be purchased on Amazon.com

It looks like great reading for a cold winter night, and I’m not receiving any “cut” in the royalties. What follows are two book reviews. 🙂


In ‘The Perfect Dose’, Jack Rubinstein takes us on a thrilling journey between a Midwest suburbia, the cardiology ward of a teaching hospital, and Mexico City. Thrown in is just about the right mixture of diverse and exciting characters starting with the protagonist cardiologist Dr. Mann, his Latina wife Pilar, and his science wingman. We are confronted with a new life-saving treatment in today’s environment of corporate greed and political anti-science sentiment that comes at the expense of scientific progress and healthcare.

The novel is a pleasure to read. It’s the ‘right dose’ of a realistic and livid account of the current state of biomedical research policy in the US and a story- telling that is funny and engaging. It’s a must-read for anyone who cares about a good story on the current affairs of biomedical research and health care in our times. —–Univ.-Prof. Dr. Björn Schumacher, Director, Institute for Genome Stability in Aging and Disease / Direktor, Institut für Genomstabilität in Alterung und Erkrankung, President of the German Society for Aging Research / Präsident der Deutschen Gesellschaft für Alternsforschung (DGfA), Medical Faculty / Medizinische Fakultät, University of Cologne / Universität zu Köln

Our dream as physician-scientist is to develop a drug that will cure or manage diseases. It is difficult enough to graduate from medical school and get trained as a resident and a fellow. It is doubly, and independently, difficult to train to be a scientist. Then the chances of sustaining such carreer are based on funding –– where the success rate is somewhere around 10%, depending on the times. In the rare occasion that a physician-scientist has intellectual property that can become biotech, or in alliance with pharmaceutical companies, the frustration is in the discovery that this experiece is another profession that is not similar to previous experience. Furthermore, it leads to success rate of less than 5%.

How can those facts be packaged in a way so as to inform medical students, doctors, scientists and intelligent lay people? Through this wonderful fiction is a suspenseful plot that deals, in addition to the above, with the politics of healthcare. I couldn’t stop reading The Perfect Dose book’ I felt like getting a second dose… —–Nir Barzilai MD, The Rennert Chair of Aging Research, Professor of Medicine and Genetics, Director of The Institute for Aging Research; Albert Einstein College of Medicine, New York.

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Genetics Humor

This cartoon by Scott Nickel in Mad Magazine is kind of cute. Comment would of course have to bring up the fact that these guys are also a few percent cellulose and other wood items, a little vegetable matter and a little wool or polyester as well. But we get it.

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Emery and Rimoin’s Principles and Practice of Medical Genetics and Genomics (ERPPMGG) Dec, 2018

For those interested in integrating genetics/genomics into medical practice –– the latest edition (“of the ultimate resource”) can now be purchased from Amazon.com –– the eTextbook is $118.75 and the hard-cover copy is amazingly only $125. This includes ELEVEN VOLUMES and comprises something like 7,500 pages(!!)  This can be pre-ordered now; release will be on December 14th.


Emery and Rimoin’s Principles and Practice of Medical Genetics and Genomics: Foundations 7th Edition

by Reed E. Pyeritz M.D. Ph.D. FACP FACMG (Editor), Bruce R. Korf MD PhD (Editor), Wayne W. Grody (Editor)

For decades, Emery and Rimoin’s Principles and Practice of Medical Genetics and Genomics has served as the ultimate resource for clinicians integrating genetics into medical practice. With detailed coverage in contributions from over 250 of the world’s most trusted authorities in medical genetics and a series of 11 volumes available for individual sale, the Seventh Edition of this classic reference includes the latest information on seminal topics such as prenatal diagnosis, genome and exome sequencing, public health genetics, genetic counseling, and management and treatment strategies to complete its coverage of this growing field for medical students, residents, physicians, and researchers involved in the care of patients with genetic conditions. This comprehensive, yet practical, resource emphasizes theory and research fundamentals related to applications of medical genetics across the full spectrum of inherited disorders and applications to medicine more broadly.

This volume, Foundations, summarizes basic theories, concepts, research areas, and the history of medical genetics, providing a contextual framework for integrating genetics into medical practice. In this new edition, clinically oriented information is supported by full-color images and expanded sections on the foundations of genetic analytics, next generation sequencing, and therapeutics.

With regular advances in genomic technologies propelling precision medicine into the clinic, Emery and Rimoin’s Principles and Practice of Medical Genetics and Genomics: Seventh Edition bridges the gap between high-level molecular genetics and practical application and serves as an invaluable clinical tool for the health professionals and researchers.

·         Introduces genetic researchers, students, and health professionals to basic theories, concepts, research areas, and the history of medical genetics, offering a contextual framework for integrating genetics into medical practice

·         Completely revised and up-to-date, this new edition highlights traditional approaches and new developments in the field of medical genetics — including cancer genetics, genomic technologies, genome and exome sequencing, prenatal diagnosis, public health genetics, genetic counseling, and single-cell analysis for diagnosis

·         Includes color images supporting identification, concept illustration, and method processing

·         Features contributions by leading international researchers and practitioners of medical genetics

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FLYING — Without combustion or moving parts — IONIC WIND

This topic has little, if anything, to do with gene-environment interactions. Yet — the possibilities and potential applications of this concept might be be a game-changer to our future generations. Anyone who has watched the Star Wars movie series has seen “future cars and other vehicles” floating through the atmosphere — with no signs of moving parts or combustion engine exhaust. Are these vehicles being run by nuclear fission, or nuclear fusion? To our knowledge, the originator of Star Wars George Lucas has never revealed what he was thinking, but “electroaerodynamics” (EAD) is a means of generating propulsive forces in fluids; and one can consider Earth’s atmosphere as “a fluid”.

Ions generated in an ambient fluid (when under the influence of an applied electric field) can be accelerated by the Coulomb Force (A force exerted by stationary objects (with electric charge) — on other stationary objects (with electric charge). If charges are same, then the force is repulsive; if charges are opposite, the force is attractive. The strength of the force is determined in physics by Coulomb’s Law). These ions collide with neutral molecules, and couple the momentum of accelerated ions with the momentum of the bulk fluid. The result is an IONIC WIND — that produces a thrust force in the opposite direction to ion flow. In the attached article [also attached editorials], authors describe the generation of ions, using a corona discharge (a self-sustaining electrical discharge brought on by ionization of a fluid, such as air, surrounding a conductor that is electrically charged). This corona discharge is induced by applying a constant high electric potential across two asymmetric electrodes; high electric fields (near the smaller electrode) accelerate electrons and produce a cascade of ionizations — by successive electron collisions with neutral molecules.

EAD propulsion is a method of manipulating and moving fluids without need for any moving surfaces — which makes it attractive for a number of applications (e.g. heat-transfer enhancement, ion-drag pumps). Additional advantages of being nearly silent and producing no combustion emissions make EAD particularly attractive as a propulsion system for airplanes. EAD could potentially mitigate the harmful impact of current airplane propulsion systems on the environment. In the case of designing drones, EAD could enable the design of quieter, smaller aircraft that interact more closely and innocuously (e.g. no fossil fuel emissions) with the urban environment.

The solid-state nature of EAD could also enable miniaturization to an extent not possible with conventional propulsion. Of course, the feasibility of EAD as a method of propulsion is confronted by the challenges of producing sufficient thrust, while achieving low aircraft drag and weight. Although there have been a number of design proposals for heavier-than-air EAD airplanes, no such aircraft has flown — until this experiment described herein.

Authors [see attached] were able to fly a fixed-wing airplane, having a 5-meter wingspan, ten times, showing that it achieved steady-level flight. All batteries and power systems, including a specifically developed ultralight high-voltage (40-kV) power converter, were carried on-board. This project shows that conventionally accepted limitations in thrust-to-power ratio and thrust density — which were previously thought to make EAD unfeasible as a method of aircraft propulsion — are in fact surmountable..!! Authors have provided a proof-of-concept for EAD airplane propulsion. This watershed experiment (once it can be shown to be reproduced, and expanded upon) opens up enormous possibilities for aircraft and aerodynamic devices that are quieter, mechanically simpler, and are not producing combustion emissions. J

Nature 22 Nov 2o18; 563: 532–535 [article] & pp 476–477 [News’N’Views] & p 443 [Editorial]

COMMENT: Yes, this is a fantastic article. I read this the day my issue of Nature arrived. Totally cool!

In case you’re wondering why it traveled only “~ 50 meters on each flight” (Fig. 3b), note the indoor tennis courts shown in Fig 2; the MIT scientists shut down the ionic wind device, so that it wouldn’t crash into the wall! Note also in the ‘Acknowledgments’ section:

“…we thank the MIT Department of Athletics, Physical Education, and Recreation for access to space for indoor flight testing …” Next, I want to see that device fly across the Charles River!

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Human models are needed for studying human neurodevelopmental disorders

There is a need to understand human neurodevelopmental processes and disorders — including diseases caused by, or worsened by, exposure to environmental toxicants. Animal models (especially differences between inbred mouse strains or transgenic mouse lines) can be indispensable for determining molecular mechanisms of gene regulation in the brain. However, animal models are limited in revealing some of the most fundamental aspects of development, genetics, pathology, and disease mechanisms that are unique to humans. These shortcomings are exaggerated in disorders that affect the brain, where the most significant differences between humans and animal models exist; these limitations could lead to failures in specific drug interventions in affected individuals.

Analysis of human-specific characteristics of the brain has been slowed down by the difficulty in acquiring developing brain tissue, as well as diseased human brain tissue. The various types of human pluripotent human stem cells (hPSCs) provide an important alternative for studying development and function of brain cells. To facilitate brain-disease modeling, human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) provide paradigms for defining human-specific biology and identifying disease mechanisms. By adding the “correct cocktail” to hESC cultures, one can differentiate these stem cells into (for example) myocytes (to make muscle), osteoblasts (to make cartilate or bone), keratinocytes (to make skin), endothelial cells (to make the lining of intestine), cardiomyocytes (heart muscle cells), and oligodendrocytes or astrocytes or other brain-specific cells.

Questions remain, however, about the utility and value of these cell-culture-based models and how best to move the technology forward. In the attached (excellent) review, authors summarize human-specific features of the process of neurodevelopment and neurodevelopmental diseases. Authors also present the gaps between animal models and human diseases, demonstrate how human stem cell models can bridge some of these gaps, and discuss the challenges for further improvement. This information argues for a more thoughtful approach to disease modeling through consideration of the valuable features and limitations of each model system, be they human or nonhuman animal — to mimic disease characteristics.

Am J Human Genet 6 Dec 2o18; 103, 829–857

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Individual variability in cardiovascular complex disease — affected by genetics and the gut microbiome

On these GEITP pages we have had a continuing dialogue on impact of the human genome vs impact of the gut microbiome — on human health, especially considering the latest advances in high-throughput deep-sequencing [also called next-generation sequencing (NGS); and whole-genome sequencing (WGS)] technology. Genome-wide association studies (GWAS) and metagenome-wide association studies have provided evidence that development of many complex diseases can be highly influenced by the human genome, the microbiome, and their interactions. This has now been shown for cardiovascular diseases, type-2 diabetes, inflammatory bowel disease (IBD), and several different types of cancer.

The impact of gut microbiome and genome–microbe interactions on molecular traits is largely unknown, however — which greatly limits our mechanistic understanding of microbial associations with complex diseases. As repeatedly emphasized in GEITP, this impact and interactions is also relevant to other multifactorial traits, including response to environmental toxicants and response to drugs (either efficacy or toxicity).

Circulating plasma proteins are often used as risk factors, or biomarkers, for various diseases. Authors [see attached article] present a “systems genome and metagenome association analysis” on 92 circulating plasma proteins in two cohorts (one representing 1,178 persons, the other numbering 86 persons). Authors examined genotype (DNA sequence), metagenome (genetic material recovered directly from environmental samples; this field has also been called ‘environmental genomics’, ‘ecogenomics’ or ‘community genomics’), transcriptome [the messenger-RNA (mRNA) transcribed from active genes in the DNA template], and detailed phenotype (trait) data. The 92 proteins were selected a priori, based on their direct or indirect role in development of cardiovascular diseases (CVDs). However, most of these proteins have a broader impact on host health and have also been shown to be relevant to many other diseases.

Authors identified genetic components for 73 proteins and microbial associations for 41 proteins — of which 31 were associated with both. The recognized genetic and microbial factors mostly exerted additive effects and, collectively, were able to explain as much as 76% of inter-individual variation (17.5% on average). Genetic effects contributed mostly to concentrations of immune-related proteins, whereas the gut microbiome contributed mostly to proteins involved in metabolism and intestinal health. Authors found several host–microbe interactions that had an impact on proteins involved in epithelial function, lipid metabolism, and central nervous system (CNS) function. This study therefore provides important evidence for a combined genetic and microbial effect on cardiovascular disease. Authors propose that this approach should provide directions for future applications in personalized medicine.

Nature Genet Nov 2o18; 50: 1524–1532

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Statistical pitfalls of “personalized medicine”

“Personalized medicine” has become a buzzword for many clinicians and geneticists during the past decade. Personalized medicine aims to match each individual with the (most appropriate) therapy that is “best suited to him/her for their condition” (this goal of course, is “FAR easier said, than done”). The author of the attached brief review was formerly at the Luxembourg Institute of Health. Before joining LIH in 2011, he had been Professor in Statistics at the University of Glasgow and at University College London. In addition to working as an academic, the author has also worked for the pharmaceutical industry in Switzerland, and the National Health Service in England. He is author of three books and claims to have expertise in statistical methods for drug development and statistical inference. He consults extensively for the pharmaceutical industry. The author [see attached] presents his view of personalized medicine.

Some of us prefer a broader, more lucid view of “personalized medicine”: Each person’s drug response should be considered holistic (i.e. characterized by treating the whole patient, taking into account mental and social factors, rather than just the physical symptoms of a disease). Every drug response is a phenotype (a trait) that reflects five contributing factors: genotype (differences in DNA sequence), epigenetic effects (independent of DNA sequence, which include DNA-methylation, RNA-interference, histone modifications, and chromatin remodeling), endogenous influences (e.g. age, disease, status of heart and kidney, hormones, exercise, stress), environmental factors (e.g. cigarette smoking, diet, consumption of alcohol and over-the-counter drugs, occupational chemicals), and microbiome differences (metabolites from gut bacteria are increasingly being realized to affect the immune system, brain, intestine, and just about all aspects of the patient).

In a recent review [Zhang G & Nebert DW, Pharmacol Ther Jul 2017; 175: 75–90 & Glossary, Mar 2018; 183: 205-206], genetic variations in inter-individual drug response were categorized in three classes, albeit as a gradient with overlapping categories: [a] monogenic traits (Mendelian; typically influenced by one or a few rare coding variants); [b] predominantly oligogenic traits (usually representing variability elicited largely by a small number of major pharmacogenes); and [c] complex pharmacogenomic traits (contributed mostly by hundreds or thousands of small-effect variants). This last category is by far the most common; each of these variants usually contributes such a small-effect to the trait, however, that they have no clinically useful predictive value, even when combined. The more that is learned about how complex the human genome is, and how unique each individual is, prediction of most drug responses — in each individual patient — seems unlikely in the foreseeable future.

Nature 29 Nov 2o18; 563: 619–621

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