Whole-genome sequencing (WGS) studies: Implications for autism spectrum disorder

Whole-genome sequencing (WGS) is becoming increasingly common for investigating the full spectrum of genetic variation, associated with relatively common complex diseases (e.g. obesity, cancer, autism spectrum disorder), but the challenges in interpreting data are considerable. Remember that complex diseases –– just like most instances of drug efficacy or toxicity, or genetic differences in response to an environmental toxicant –– are multifactorial traits (representing contributions from hundreds, if not thousands, of genes, plus epigenetic factors, plus environmental effects).

The primary motivation for a WGS study is to understand whether structural, rare, and de novo DNA variants (also called nucleotide changes; single-nucleotide variants, SNVs; mutations) –– in the noncoding genome –– contribute to the cause of the disease etiology –– in addition to the more well-understood contribution from mutations in the coding genome. Keep in mind that “the human (i.e. coding) exome” comprises ~180,000 exons, which represents ~30 megabases (Mb, or million bases), or about 1% of the total genome. This means the noncoding genome represents the remaining 99% of the human genome..!! Nevertheless, DNA variants in the human exome are believed to harbor ~85% of the mutations that exhibit large-effect contributions to disease.

Authors [see attached study & editorial] provide the first serious attempt to establish a framework for enrichment analyses of rare noncoding variation in WGS studies of common complex diseases. They evaluated, by way of WGS, rare and de novo noncoding SNVs, insertions/deletions (indels), and all classes of structural variations (e.g. copy-number variations, CNVs; large rearrangements; repetitive types of DNA). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions –– authors defined 51,801 annotation categories..!!

Analysis of 519 autism spectrum disorder (ASD) families (i.e. unaffected vs affected members) did NOT identify any particular association with any categories –– after authors corrected for 4,123 effective tests (this contradicts a number of previously published ASD studies) Without appropriate correction, biologically plausible associations are observed –– but in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still DO exhibit the strongest associations with ASD. Thus, in autism, the contribution of de novo variats in the noncoding region is probably modest, in comparison to that of de novo coding variants. Robust results from future WGS studies will require even larger cohorts and comprehensive analytical strategies that consider this substantial burden of multiple-testing. Prediction of risk of ASD is extremely unlikely, whereas identification of future drug targets for treating ASD remains a possibility.

Nature Genetics May 2o18; 50: 727–736 & 635–637 [News’N’Views editorial]

COMMENT:
Notice that our Ko et al. 2016 paper [Stem Cells 2o16; 34: 2826–2839; see attached] concluded, among other things, that AHR-expressing embryonic stem (ES) cells restrict cardiogenesis (i.e. can suppress transformation of some of these cells from being commited to heart cell formation) and, instead, commit to a neuroglial cell fate (i.e. transform some of these cells into a commitment to microglia cell formation). It thus appears that AHR expression needs to be repressed in order to maintain ES cell mitotic progression (repeated cell divisions) and to prevent premature loss of pluripotency (changing to tissue-specific cell-types).

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Microglial control of astrocytes in response to microbial metabolites: AHR participates in CNS inflammation (e.g. multiple sclerosis)

Authors [see attached article] identified positive and negative regulators that mediate the means by which microglia [cells in the glia of brain that function as macrophages (scavengers) in the central nervous system (CNS)] control astrocytes (star-shaped glial cells in the CNS). These exciting results define a pathway through which microbial metabolites [derived from bacteria in the gastrointestinal (GI) tract] limit pathogenic activities of microglia and astrocytes –– thereby resulting in the suppression of CNS inflammation. Authors suggest that this pathway may lead to new therapies for multiple sclerosis and other (inflammatory) neurological disorders.

That darned aryl hydrocarbon receptor (AHR) transcription factor just seems to keep popping up in the middle of every critical-life function. J Microglia in the CNS have been known to express AHR. To investigate the role of microglial AHR on CNS inflammation, authors generated a transgenic mouse in which a tamoxifen-inducible promoter drives expression of Cre recombinase fused to an estrogen ligand-binding domain. After treatment of these mice with tamoxifen, AHR-expressing peripheral cells are replenished from bone marrow –– while microglia remain AHR-deficient without any abnormal cell death. Microglial AHR deletion led to worsened conditions of experimental autoimmune encephalomyelitis (EAE), which led to increasing demyelination and CNS monocyte recruitment; the T-cell response remained unaffected. Collectively, these findings suggest that microglial AHR limits (i.e. is able to suppress) EAE. NF-κB (a protein complex that controls transcription of DNA, cytokine production, and cell survival) controls microglial responses during EAE, and AHR can limit NF-κB activation in a SOCS2-dependent manner (SOCS2 = suppressor of cytokine signaling-2, a key regulator of growth hormone, insulin-like growth factor, and other signaling pathways implicated in inflammation and cancer). Deletion of microglial AHR thus caused decreases in Socs2 expression and resulted in up-regulation of transcripts associated with microglial activation, inflammation, and neurodegeneration.

As previously discussed recently in these GEITP pages, AHR is known to be associated with “reception of environmental, as well as endogenous, signals” –– including the stress signal of inflammation. This resulting in a cascade of downstream events programmed to respond to those incoming signals (in ways to promote cell and organism survival) [recently reviewed in: Progr Lipid Res 2o17; 67: 38]. Below is Table 1 from that review. As can be see, as a member of the bHLH/PAS family, AHR participates in virtually all fundamental/developmental and critical-life functions in the living organism:

Table 1

Summary of organs, systems, cell functions, and developmental biology in which AHR-signaling is involved.

Location AHR-signaling pathway involvement

Central nervous system Development of brain and nervous system; Neurogenesis; Neuronal cell development; Cardiorespiratory brainstem development in ventrolateral medulla; “Brain-gut-microbiome”

Eye Ciliary body formation and function; Thyroid-associated eye disease

Gastrointestinal tract Development of GI tract; Rectal prolapse during aging; “Brain-gut-microbiome”

Heart Development of heart organ; Cardiovascular physiology; Atherogenesis; Cardiomyogenesis; Cardiorespiratory function

Hematological system Development of blood cell-forming system; Hematopoiesis; Activation or suppression of erythroid development

Immune system Immune system development; The immune response; Innate immunity; Pro-inflammatory response; Anti-inflammatory response; Immunomodulatory effects

Inner ear Development of the cochlea

Kidney Development of the kidney; Hypertension

Liver Development of liver organ; Hyperlipidemia; Glucose and lipid metabolism; Hepatic steatosis

Musculoskeletal system Transmesoderm → osteoblast transition; Bone formation; Osteoclastogenesis

Pancreas Development of pancreas; Beta-cell regulation; Pancreatic fibrosis

Endocrine system Serum lowered testosterone levels; Infertility; Mammary gland duct cell epithelial hyperplasia; Degenerative changes in testis; Gerrm-cell apoptosis; Endometriosis

Reproductive system Development of male and female sex organs; Spermatogenesis; Fertility

Respiratory tract Development of respiratory tract; Disruption of GABA-ergic transmission defects; Cardiorespiratory function

Vascular system Angiogenesis; Atherosclerotic plaque formation

Skin Barrier physiology; Atopic dermatitis

Cellular functions Cell migration; Cell adhesion; Circadian rhythmicity

DNA changes DNA synthesis; DNA repair; DNA-adduct formation; Mutagenesis

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Plain-language medical vocabulary for “precision diagnosis”

Following up on the most recent GEITP email about “precision medicine” or “personalized medicine,” this brief letter [see attached] concerns “precision diagnosis.” Often discussed in these GEITP pages have been genome-wide association studies (GWAS) in which one or more genes (genotype) is identified as “being associated with” a particular trait (phenotype). If one has a study population (cohort) of 10,000 having Trait A, compared with a control cohort of 10,000 without Trait A –– this is what is called a “genotype-phenotype association study”.

For a simple example, let’s say we have chosen to study a cohort with “blue eyes”, i.e. what gene(s) are associated with this trait? (And the control cohort is “brown eyes.”) If, within the 10,000 individuals purported to have blue eyes, there are 10 subjects misdiagnosed because they have green eyes. The results of this study would therefore be “fuzzy,” or “tainted,” by this “lack of precision diagnosis,” or “increased noise.” Many genomicists refer to this as an equivocal phenotype. Everyone would prefer to have a study cohort having only an unequivocal phenotype. The same can be said about any trait –– type-2 diabetes, schizophrenia, breast cancer, autism spectrum disorder –– if some in the study cohort are misdiagnosed, then the results of the study are plagued with decreased statistical power.

Perhaps especially for patients not yet diagnosed, and those with rare diseases, the affected individuals themselves are an especially critical source of phenotyping information. These patients live with their condition, and develop explicit and implicit knowledge about it –– whether from multiple-clinician evaluations or from other families and patients experiencing diagnosis for similar conditions. Many web sites have been set up, over the past three decades, for those with a common ailment to share their grief/concern/advice/comments/questions. From these interactions, they develop a lexicon of relevant terms; these terms are frequently in plain layman language, but can also include clinical terms.

Human genetics and precision medicine therefore aim to understand the relationship between genetic variants and diseases. Whole-exome seuencing (WES) and whole-genome sequencing (WGS) have transformed the ability comprehensively to characterize genetic variants. Although WES and WGS have led to the discovery of many novel disease-associated genes, the diagnostic yield in patients without a clear clinical diagnosis has been 11% to 25%. The Human Phenotype Ontology (HPO) was created [Am J Hum Genet 2oo8; 83: 610] to enable ‘deep phenotyping’, i.e. capture of symptoms and phenotypic findings using a logically-constructed hierarchy of phenotypic terms. The HPO has become the de facto standard for representing clinical phenotype data to inform diagnoses for rare genetic diseases by the 100,000 Genomes Project, the NIH Undiagnosed Diseases Program (UDP), and Undiagnosed Diseases Network (UDN), as well as thousands of other clinics, laboratories, tools, and databases. Further details are described in the attached letter. 🙂

DwN

Nature Genetics Apr 2o18; 50: 474–476

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Genome-wide association study in Europeans (N=176,678) reveals genetic loci for tanning response to sun exposure

Repeated exposure to the sun is well known to be associated with increased risk of all skin cancers, including cutaneous malignant melanoma (CMM), basal cell carcinoma and squamous cell carcinoma, and these types of cancer are more common in fair-skinned, rather than darker-skinned, people. The tanning response after exposure to sunlight is also well known to be mainly determined by melanin pigmentation, which aims at protecting the skin from DNA photo-damage. Genome-wide association studies (GWAS) on European populations have previously identified several DNA variants in or near seven genes –– ASIP (agout-signaling protein), EXOC2, (exocyst-complex component-2) HERC2 (ECT- and RLD-domain-containing E3 ubiquitin protein ligase-2), IRF4 (interferon regulatory factor-4), MC1R (melanocortin-1 receptor), SLC45A2 (solute-carrier family 45, member-2), and TYR (tyrosinase). These seven genes are known to be associated with both pigmentation-related traits (e.g. hair, eye or skin color) and skin cancer.

Authors [see attached] chose to investigate further the genetic basis of skin-tanning and the effect on skin cancer susceptibility (i.e. by starting with a much larger cohort –– that should ‘find’ additional significant genetic loci) –– by performing large-scale GWAS using data from the UK Biobank (N = 176,678 subjects of European ancestry). They identified significant associations with tanning ability at 20 loci –– confirming previously identified associations at six of these previous loci, and reporting 14 novel loci (ten of these loci have never before been associated with pigmentation-related phenotypes).

In addition to identifying and replicating genes previously associated with ease of skin-tanning or pigmentation-related phenotypes (traits), authors (intriguingly to me) demonstrated a genetic correlation between ease of skin-tanning as well as risk of non-melanoma skin cancer with DNA variants at the AHR/AGR3 locus. These two genes –– AHR (aryl hydrocarbon receptor) and AGR3 (anterior-gradient-3, protein disulfide isomerase family member) reside next to one another on human chromosome 7p21.1.

The former gene, AHR (first discovered by yours truly and Alan Poland in 1974) is known to be associated with “reception of environmental, as well as endogenous, signals”, resulting in a cascade of downstream events programmed to respond to those incoming signals (in ways to promote cell and organism survival). In the case of “sunlight” as the signal, undoubtedly the “genetic response” includes cell cycle genes and DNA-repair genes [recently reviewed in: Progr Lipid Res 2o17; 67: 38].

I didn’t know if it was possible until I tried –– but I see it IS possible to download from that journal article and paste below Table 1 and Figures 8 & 9 from that elegant review. 🙂 As can be see, as a member of the bHLH/PAS family, AHR participates (lends a helping hand) in for virtually all fundamental/developmental and critical-life functions in the living organism.

Table 1

Summary of organs, systems, cell functions, and developmental biology in which AHR-signaling is involved.

Location AHR-signaling pathway involvement

Central nervous system Development of brain and nervous system; Neurogenesis; Neuronal cell development; Cardiorespiratory brainstem development in ventrolateral medulla; “Brain-gut-microbiome”

Eye Ciliary body formation and function; Thyroid-associated eye disease

Gastrointestinal tract Development of GI tract; Rectal prolapse during aging; “Brain-gut-microbiome”

Heart Development of heart organ; Cardiovascular physiology; Atherogenesis; Cardiomyogenesis; Cardiorespiratory function

Hematological system Development of blood cell-forming system; Hematopoiesis; Activation or suppression of erythroid development

Immune system Immune system development; The immune response; Innate immunity; Pro-inflammatory response; Anti-inflammatory response; Immunomodulatory effects

Inner ear Development of the cochlea

Kidney Development of the kidney; Hypertension

Liver Development of liver organ; Hyperlipidemia; Glucose and lipid metabolism; Hepatic steatosis

Musculoskeletal system Transmesoderm → osteoblast transition; Bone formation; Osteoclastogenesis

Pancreas Development of pancreas; Beta-cell regulation; Pancreatic fibrosis

Endocrine system Serum lowered testosterone levels; Infertility; Mammary gland duct cell epithelial hyperplasia; Degenerative changes in testis; Gerrm-cell apoptosis; Endometriosis

Reproductive system Development of male and female sex organs; Spermatogenesis; Fertility

Respiratory tract Development of respiratory tract; Disruption of GABA-ergic transmission defects; Cardiorespiratory function

Vascular system Angiogenesis; Atherosclerotic plaque formation

Skin Barrier physiology; Atopic dermatitis

Cellular functions Cell migration; Cell adhesion; Circadian rhythmicity

DNA changes DNA synthesis; DNA repair; DNA-adduct formation; Mutagenesis

Oxidative stress Mitochondrial ROS formation; Anti-oxidant protection against ROS formation; Mitochondrial H2O2 production; Crosstalk with hypoxia and HIFsignaling pathways; Transforming growth factor- signaling pathways; MID1-PP2A-CDC25B-CDK1 signaling pathway regulating mitosis

Tumor cells Growth suppression; Tumor initiation; Tumor promotion

ES cell basic functions Ectoderm → epithelium transition; Cell adhesion; Cell-cycle regulation; Apoptosis; Cavitation during morula →blastula formation; Activator of Rho/Rac GTPases; WNT-signaling pathways; Homeobox-signaling pathways

Other basic functions Transgenerational inheritance; Epigenetic effects; Chromatin remodeling; Histone modification; Aging-related and degenerative diseases

Nature Commun 2o18; 9: 1684

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Pharmacogenomics of GPCR Drug Targets — Data-Mining Experiment, rather than a Genome-Wide Association Study

COMMENT: Yes, Dan –– This is a very key point in precision medicine.

Many of the examples that you mention are likely to be modulated by the immune system, with completely unanticipated immune reactions against the whole drug molecule. If it is a large molecular-weight drug (such as abacavir), it can bind directly to specific HLA types [Human leukocyte antigen genes (HLA) encoding the major histocompatibility complex (MHC) proteins in humans; these cell-surface proteins are responsible for regulation of the immune system) –– located on the surface of antigen-presenting cells for T-cell activation. Alternatively, if it is a small-molecular weight drug, the antibody that is produced can be directed against a hapten (a small molecule that, when combined with a larger carrier, e.g. a protein, can elicit the production of antibodies that bind specifically to it), which means the drug binds covalently with a peptide.

At the present time, there are about 40 different HLA alleles that have been associated with increased risk of ADRs caused by different drugs. To a smaller degree, I think adverse drug reactions can be determined by rare genetic variants [abstract of recent article (Hum Genomics 2o18; 12: 26) pasted below].

Integrating rare genetic variants into pharmacogenetic drug response predictions

Ingelman-Sundberg M, Mkrtchian S, Zhou Y, Lauschke VM.

BACKGROUND: Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response.

RESULTS: To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes.

CONCLUSIONS: Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment.

COMMENT This topic falls precisely under the heading of “gene-environment (G x E) interactions.” Or better yet, in the case of this paper, “drug-genome interactions.” This is among the most intriguing mysteries in all of clinical pharmacology: a drug is given to patient A and it works as expected (efficacy), but given to patient B, the drug causes an adverse drug reaction (ADR), and given to patient C, there is no beneficial or toxic effect (therapeutic failure). How does a small-molecular-weight drug — given to some patients, but not the majority of patients in any population — cause an ADR that is often indistinguishable from a complex disease?

For example, sitagliptin is approved by the FDA to treat type-2 diabetes; yet, a small subset taking the recommended prescribed dose develops acute pancreatitis. Hydroxychloroquine — given to treat malaria, lupus erythematosus, or rheumatoid arthritis — can also lead to acute pancreatitis in some patients. In a subpopulation of patients receiving many psychotropic drugs (e.g. valproic acid), undesirable weight gain can occur as a dose-independent ADR; in another small subset, hepatic steatosis (fatty liver) has been found. In a small subpopulation of patients taking bisphosphonates for osteoporosis, increased risk of esophageal and gastric cancer has been reported in a number of studies; however, a large meta-analysis of this association has found no significantly increased risk [Wright et al., BMJ Open 2015; 5: e007133].

DwN

COMMENT: Hi Dan, The most remarkable finding in this Cell paper, I think, is the shift of the mu-opioid receptor, by a rare mutation, to respond to naloxone as an agonist instead of antagonist. However, more wet-lab experiments are needed to verify some of these key findings.

PREVIOUS POST

The [attached] article is a bombshell report and, to our knowledge, represents the first study of its kind. Rather than a genome-wide association study (GWAS), authors performed an avante gard data-mining in silico approach — to search for DNA variants in or near each of the 108 G-protein-coupled receptor genes (GPCRs) known to exist in the human genome. In the field of pharmacology and drug response, these 108 genes are the known targets of 475 prescription drugs that have been approved by the U.S. Food and Drug Administration (FDA). These 475 drugs, which comprise ~34% of all prescription drugs, account for a global sales volume of >US$180 billion annually..!!

Each of the genomes of almost 68,500 individuals was separately investigated for missense variants in and near each of the GPCR genes. Then the authors searched the literature for the clinical associations with altered drug response in these individuals. To estimate the de novo missense mutation rate within these GPCR genes, authors in addition identified novel mutations from >1,700 control trios (having no reported pathological conditions) –– which were compiled from ten different studies registered in the “denovo-database,” an intriguing collection of germline de novo variants (http://denovo-db.gs.washington.edu/denovo-db/).

To demonstrate proof-of-principle, authors then experimentally showed that certain variants of the mu-opioid and cholecystokinin receptors resulted in altered drug responses and/or idiosyncratic dose-independent adverse drug reactions. These amazing results — on just two of the 108 GPCR genes — underscore the need to characterize DNA variants among all 108 of the GPCR genes. Authors suggest that “the ultimate results of this novel type of in silico study might enhance prescription precision, improve patients’ quality-of-life, and remove some of the economic and societal burden caused by variability in drug response.”

We anticipate that such “dry-lab” data-mining studies, i.e. just sitting in front of a computer and searching databases online — such as this landmark publication [attached] — are likely to become a major new way to approach pharmacogenomics research in the near future..!! J

DwN

Cell Jan 2o18; 172: 41–54

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Pharmacogenomics of GPCR Drug Targets — Data-Mining Experiment, rather than a Genome-Wide Association Study

The [attached] article is a bombshell report and, to our knowledge, represents the first study of its kind. Rather than a genome-wide association study (GWAS), authors performed an avante gard data-mining in silico approach — to search for DNA variants in or near each of the 108 G-protein-coupled receptor genes (GPCRs) known to exist in the human genome. In the field of pharmacology and drug response, these 108 genes are the known targets of 475 prescription drugs that have been approved by the U.S. Food and Drug Administration (FDA). These 475 drugs, which comprise ~34% of all prescription drugs, account for a global sales volume of >US$180 billion annually..!!

Each of the genomes of almost 68,500 individuals was separately investigated for missense variants in and near each of the GPCR genes. Then the authors searched the literature for the clinical associations with altered drug response in these individuals. To estimate the de novo missense mutation rate within these GPCR genes, authors in addition identified novel mutations from >1,700 control trios (having no reported pathological conditions) –– which were compiled from ten different studies registered in the “denovo-database,” an intriguing collection of germline de novo variants (http://denovo-db.gs.washington.edu/denovo-db/).

To demonstrate proof-of-principle, authors then experimentally showed that certain variants of the mu-opioid and cholecystokinin receptors resulted in altered drug responses and/or idiosyncratic dose-independent adverse drug reactions. These amazing results — on just two of the 108 GPCR genes — underscore the need to characterize DNA variants among all 108 of the GPCR genes. Authors suggest that “the ultimate results of this novel type of in silico study might enhance prescription precision, improve patients’ quality-of-life, and remove some of the economic and societal burden caused by variability in drug response.”

We anticipate that such “dry-lab” data-mining studies, i.e. just sitting in front of a computer and searching databases online — such as this landmark publication [attached] — are likely to become a major new way to approach pharmacogenomics research in the near future..!! J

DwN

Cell Jan 2o18; 172: 41–54

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Einstein’s quotes — which ones are true and which are not?

This (somewhat tongue-in-cheek-humorous) one-page book report –– about “which quotes REALLY DID originate from Einstein, and which ones are attributed falsely to him?” [attached] is worth sharing with all GEITP’ers. Beyond his towering contributions to Physics, Albert Einstein was an avid commentator on Education, Marriage, Money, the Nature of Genius, Music-Making, Politics, and more. His insights were legendary, as we are reminded by the recent publication of Volume 15 in The Collected Papers of Albert Einstein. Even the website of the U.S. Internal Revenue Service enshrines his words (as quoted by his accountant): “The hardest thing in the world to understand … is the income tax.”

“There appears to be a bottomless pit of quotable gems to be mined from Einstein’s enormous archives,” notes Alice Calaprice, editor of The Ultimate Quotable Einstein (2011), but there might be a hint of despair in her comment. Indeed –– Einstein could be the “most quoted scientist in history”. The website Wikiquote has many more entries attributed to Einstein –– than for Aristotle, Galileo Galilei, Isaac Newton, Charles Darwin, or Stephen Hawking, and even more than Ein­stein’s opinionated contemporaries Winston Churchill and George Bernard Shaw. However, how much of this super-abundance actually emanated with certainty from Einstein? See the attached article to find out. 🙂

Nature 3 May 2o18 557: 30

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Evolutionary origin of mitochondria predates the origin of Alphaproteobacteria

Mitochondria are known as the “energy factories” of most (animal) cells. Earliest bacteria that had originated on the planet do not have mitochondria, and have only one chromosome (haploid, seen in all prokaryotes) rather than chromosome-pairs (diploid, seen in all eukaryotes). Most eukaryotic cells have a nucleus, and then the cytoplasm outside the nucleus –– which contains many types of organelles –– including hundreds of mitochondria. These mitochondria are involved in various processes, of which ATP generation by means of oxidative phosphorylation (used by the cell to generate energy for itself and other cells) is a hallmark feature. Structures in plants are similar to mitochondria and called chloroplasts.

“The Endosymbiotic Theory” describes how the fusion of a large host eukaryotic cell, with one or more ingested bacteria, could easily have become dependent on one another for survival (hence, this is a topic of gene-environment interactions), resulting in a permanent relationship. After more than 2 billion years of evolution, mitochondria and chloroplasts have become more specialized, and today they cannot live outside the cell. In humans, while there are >20,000 genes in the nuclear genome, there are at least 37 important genes in the mitochondrial genome. During fertilization, a sperm (derived usually from the man) combines with an egg (derived usually from the woman), and only the egg has mitochondria in its cytoplasm. When there is a serious defect in a gene of the woman’s mitochondrial genome, today there is clinically successful in vitro fertilization scheme –– in which the male’s sperm, and nucleus of the female’s egg, is combined with mitochondria from a healthy woman –– following which a healthy baby is formed, derived from three persons.

To trace the evolutionary history of mitochondria and their role in the genesis of eukaryotes, detailed knowledge about the identity and nature of the mitochondrial ancestor is important. Alphaproteobacteria is a distinct Class of bacteria (which evolved later than early bacteria) in the Proteobacteria phylum; its members are highly diverse, some Taxa contain mitochondria, and certain Alphaproteobacteria can cause specific human (and agricultural) diseases, but nevertheless they share a common evolutionary ancestor. Despite the fact that the origin of mitochondria in Alphaproteobacteria is generally undisputed, efforts to resolve the phylogenetic position of mitochondria in the Alphaproteobacterial species tree have failed to reach an agreement.

Whereas most studies support the idea that mitochondria evolved from an ancestor related to Rickettsiales (an Order within Alphaproteobacteria that includes several host-associated pathogenic and endosymbiotic lineages), other studies suggest that mitochondria evolved from a free-living group. Authors [see attached publication] re-evaluated the phylogenetic placement of mitochondria. They used genome-resolved binning of oceanic meta-genome datasets and increased the genomic sampling of Alphaproteobacteria with twelve divergent clades, plus one clade representing a sister group to all Alphaproteobacteria. Subsequent phylogenomic analyses –– that specifically address long-branch attraction and compositional bias artifacts –– suggest that mitochondria did not evolve from Rickettsiales or any other currently recognized Alphaproteobacterial lineage. Rather, the analyses of these authors indicate that mitochondria evolved from a proteobacterial lineage that branched off before the divergence of all sampled Alphaproteobacteria. In light of this new finding, previous hypotheses about the nature of the mitochondrial ancestor will have to be re-evaluated. 🙂

Nature 3 May 2o18 557: 101–105

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The Post-GWAS Era: From Association to Function

After the discovery of the structure of DNA and the genetic code in the early 1950s, the field of human genetics was largely focused on understanding the structure and function of protein-coding genes and how rare mutations in these genes might be associated with causing disease or increasing risk of disease. Furthermore, the central dogma of molecular biology had decided that “genes are first transcribed into messenger RNA (mRNA), after which the mRNA is translated into protein.” Because of the straightforward nature of the genetic code –– it SEEMED easy to predict how alterations of the underlying DNA sequence would change the gene product (amino-acid sequence of the resulting protein). In addition, it was clear from Mendelian genetics that diseases “that run in families in predictable patterns” are caused by mutations in a single gene. Beginning with the mapping of the genetic cause (e.g. of sickle-cell anemia and the neurodegenerative disorder Huntington Disease), the causative mutations underlying many Mendelian diseases were elucidated by positional cloning, and an important hurdle had been accomplished in our understanding of the genetic bases of human disease.

However, many of the most common and (financially and emotionally) burdensome diseases –– such as cardiovascular disease, cancer, Alzheimer disease, Parkinsons disease, and type-2 diabetes –– are typically not (or never) caused by single mutations. Such ‘‘multifactorial traits’’ are instead influenced by a combination of multiple genetic, epigenetic, and environmental risk factors, and thus do not follow “simple” Mendelian inheritance patterns. The departure from a ‘‘one-gene, one-mutation, one-outcome’’ model posed a formidable challenge to elucidating the biology of these diseases. Multifactorial traits, by definition, are influenced by many genes (polygenic). Human height, for example, appears to be affected by genetic variation at hundreds if not thousands of loci across the genome. These genetic loci may interact in additive, or in non-additive (i.e., epistatic; gene-gene interactions), ways.

Yet, while it may not always be necessary to understand the cause of a disease in order to successfully treat it, such a mechanistic understanding certainly increases the likelihood that a successful therapeutic intervention will be achieved. The attached review summarizes what has happened since the first genome-wide association studies (GWAS) during the 2oo2-2oo6 era –– linking genetic variation to identify loci that harbor genetic variants [typically single-nucleotide variants (SNVs) or polymorphisms (SNPs)] that are associated with risk for complex diseases and quantitative traits. The earliest two GWAS that I can find include: the lymphotoxin-a gene (LTA) linked to myocardial infarction (2oo2) and the complement factor H gene (CFH) linked to age-related macular degeneration (2oo5). Today, the GWAS era has been successful in the sense that thousands of loci have been statistically significantly associated with risk for diseases and traits, and a notable number of these loci are well-replicated –– suggesting that they are true associations.

Several factors have made it difficult, however, to bridge the gap between the statistical associations linking locus-and-trait and a functional understanding of the biology underlying disease risk. First, the association of a DNA locus with disease does not specify which variant (or variants) at that locus is actually causing the association (the ‘‘causal variant’’) –– nor which gene (or genes) is affected by the causal variant (the ‘‘target gene’’). The former problem is due to the fact that there are often many co-inherited variants in strong linkage disequilibrium (LD; the non-random association of alleles at different loci along the same strand of DNA, same chromosome, in a given population) with the most significant (or ‘‘sentinel’’) disease-associated variant, comprising a haplotype. Within the haplotype, genetic variants in strong LD often have statistically indistinguishable associations with disease risk; as a consequence, empirical validation might be needed to determine which of the linked variants are functional. Second, more than 90% of disease-associated SNVs are located in non-protein-coding regions of the genome, and many of them are far away from the nearest known gene.

Am J Hum Genet 3 May 2o18; 102: 717–730

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Valid Statistical Rationales for Sample Sizes

Valid Statistical Rationales for Sample Sizes

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This webinar provides guidance on how to justify such sample sizes, and thereby indirectly provides guidance on how to choose sample sizes.

Date: Wednesday, June 6, 2018
Time: 10:00 AM PDT | 01:00 PM EDT
Duration: 90 Minutes
Instructor: John N. Zorich

Overview:

This webinar explains the logic behind sample-size choice for several statistical methods that are commonly used in verification or validation efforts, and how to express a valid statistical justification for a chosen sample size. Read more…

Who Will Benefit:

QA/QC Supervisor
Process Engineer
Manufacturing Engineer
QC/QC Technician
Manufacturing Technician
R&D Engineer

About Speaker:
John N. Zorich
Statistical Consultant & Trainer, Ohlone College & SV Polytechnic
John N. Zorich, has spent 35 years in the medical device manufacturing industry; the first 20 years were as a “regular” employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the last 15 years were as consultant in the areas of QA/QC and Statistics… Read More

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