Pleiotropy: This certainly has implications for the endlessly repeated assertion that heritable genetic pathologies account for symptoms that include everything from “being antisocial” to “being interested in subjects that bore neurotypicals” to female ASDs “preferring to wear clothing with lots of pockets”. It is acknowledged that ASD / Asperger’s are a highly ‘heterogeneous’ bunch of individuals; no two are alike. Claims for “discovery” of scads of “autism-linked genes” are highly suspicious to begin with, and now this unsurprising report, in which “causal” links are over- and under- estimated, or MISSED COMPLETELY.
Source of Potential Bias Widespread in Large Genetic Studies
A new statistical method finds that many genetic variants used to determine trait-disease relationships may have additional effects that GWAS analyses don’t pick up.
May 15, 2018|
Genome-wide association studies, which scan thousands of genetic variants to identify links to a specific trait, have recently provided epidemiologists with a rich source of data. By applying Mendelian randomization, a technique that leverages an individual’s unique genetic variation to recreate randomized experiments, researchers have been able to infer the causal effect of specific risk factors on health outcomes, such as the link between elevated blood pressure and heart disease. (And all those supposed “links” between ASD / Autism “genes” and a bizarre selection / collection of “manifestations” in ASD / Asperger behavior, brain function and even in apparel choices)
The Mendelian randomization technique has long operated on the key assumption that horizontal pleiotropy, a phenomenon in which a single gene contributes to a disease through more than one pathway, is not happening. However, a new study published last month (April 23) in Nature Genetics finds that when it comes to potentially causal trait-disease relationships identified from genome-wide association studies (GWAS), pleiotropy is widespread—and may bias findings.
The “no pleiotropy” assumption was reasonable when scientists were examining only a few genes and much more was known about their specific biological functions, says Jack Bowden, a biostatistician at the University of Bristol’s MRC Integrative Epidemiology Unit in the U.K., who was not involved in the study. Nowadays, GWAS, which include many more genetic variants, are often conducted with little understanding about the precise mechanisms through which each gene could act on physiological traits, he adds.
Although researchers have suspected that pleiotropy exists in a large number of Mendelian randomization studies using GWAS datasets, “no one has actually tested how much of a problem this was,” says study coauthor Ron Do, a geneticist at the Icahn School of Medicine Mount Sinai.
To address this question, Do and his colleagues developed the so-called MR-PRESSO technique, an algorithm that identifies pleiotropy in Mendelian randomization analyses by searching for outliers in the relationship between the genetic variants’ effects on the trait of interest, say, blood pressure, and the same polymorphisms’ effects on the health outcome, such as heart disease. Outliers suggest that some genetic variants may not only be acting on the outcome through that particular trait—in other words, that pleiotropy exists.
The team used this method to test all possible trait-disease combinations generated from 82 publicly available GWAS datasets and found that pleiotropy was present in approximately 48 percent of the 191 statistically significant causal relationships they identified. (Yes, statistics are only as good as the quality of the “thinking” of the people manipulating the process)
When the researchers compared the Mendelian randomization results before and after correcting for pleiotropy, they discovered that pleiotropy could lead to drastic over- or underestimations of the magnitude of a trait’s influence on a disease. (And ASD / Autism is NOT A DISEASE; it’s a collection of symptoms – which have multiple sources including WESTERN socio-cultural prejudice) Approximately 10 percent of the causal associations they found were significantly distorted, and by as much as 200 percent.
For example, the team identified an outlier variant in one of the significant causal relationships they found using Mendelian randomization—a link between body mass index (BMI) and levels of C-reactive protein, a marker for inflammation and heart disease. Further examination revealed that this variant, found in a gene encoding apolipoprotein E—a protein involved in metabolism—was associated with several traits and diseases, including BMI, C-reactive protein, cholesterol levels, and Alzheimer’s disease. After removing this outlier, the effect of BMI on C-reactive protein dropped by 12 percent, still statistically significant, but obviously to a lesser degree.
“There is growing awareness that there’s widespread pleiotropy in the human genome in general, and I think these findings suggest that there needs to be rigorous analysis and careful interpretation of casual relationships when performing Mendelian randomization,” (One would have thought that this was the conservative baseline in “science-based” research) Do says. “I think what’s going to have the biggest impact is not just saying whether causal relationships exist, but actually showing that the magnitude of the causal relationship can be distorted due to pleiotropy.”
Bowden notes that the presence of pleiotropy does not mean that Mendelian randomization is necessarily a flawed technique. “Many research groups around the world are currently developing novel statistical approaches that can detect and adjust for pleiotropy, enabling you to reliability test whether a [gene] has a causal effect on an outcome,” he tells The Scientist. For example, he and his colleagues at the University of Bristol recently reported another method to identify and correct for pleiotropy in large-scale Mendelian randomization analyses. (Are these “novel statistical approaches” proven to correct a problem that has much to do with the “reductive mindset” of those who place prime value on “any positive results” for their research agenda, above scientific discipline?)
“I hope that this paper will raise people’s attention to the potential problems in the assumptions behind [these studies],” says Wei Pan, a biostatistician at the University of Minnesota who was not involved in this work. “Large genetic datasets give researchers the opportunity to use a method like this to move the field forward, and as long as they use the method carefully, they can reach meaningful conclusions.” (Is this true, or social blah, blah?)
M. Verbanck et al., “Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases,” Nature Genet, doi:10.1038/s41588-018-0099-7, 2018.
The term pleiotropy is derived from the Greek words pleio, which means “many,” and tropic, which means “affecting.” Genes that affect multiple, apparently unrelated, phenotypes are thus called pleiotropic genes Pleiotropy should not be confused with polygenic traits, in which multiple genes converge to result in a single phenotype.
Examples of Pleiotropy
In some instances of pleiotropy, the influence of the single gene may be direct. For example, if a mouse is born blind due to any number of single-gene traits (Chang et al., 2002), it is not surprising that this mouse would also do poorly in visual learning tasks. In other instances, however, a single gene might be involved in multiple pathways. For instance, consider the amino acid tyrosine. This substance is needed for general protein synthesis, and it is also a precursor for several neurotransmitters (e.g., dopamine, norepinephrine), the hormone thyroxine, and the pigment melanin. Thus, mutations in any one of the genes that affect tyrosine synthesis or metabolism may affect multiple body systems. These and other instances in which a single gene affects multiple systems and therefore has widespread phenotypic effects are referred to as indirect or secondary pleiotropy (Grüneberg, 1938; Hodgkin, 1998).
Other examples of both direct and indirect pleiotropy are described in the sections that follow.
In 1936, researchers Walter Landauer and Elizabeth Upham observed that chickens that expressed the dominant frizzle gene produced feathers that curled outward rather than lying flat against their bodies (Figure 2). However, this was not the only phenotypic effect of this gene — along with producing defective feathers, the frizzle gene caused the fowl to have abnormal body temperatures, higher metabolic and blood flow rates, and greater digestive capacity. Furthermore, chickens who had this allele also laid fewer eggs than their wild-type counterparts, further highlighting the pleiotropic nature of the frizzle gene.
See article for Pigmentation and Deafness in Cats, and Antagonistic Pleiotropy and much much more on genetics…. https://www.nature.com/scitable/topicpage/pleiotropy-one-gene-can-affect-multiple-traits-569
As touched upon earlier in this article, there are many examples of pleiotropic genes in humans, some of which are associated with disease. For instance, Marfan syndrome is a disorder in humans in which one gene is responsible for a constellation of symptoms, including thinness, joint hypermobility, limb elongation, lens dislocation, and increased susceptibility to heart disease. Similarly, mutations in the gene that codes for transcription factor TBX5 cause the cardiac and limb defects of Holt-Oram syndrome, while mutation of the gene that codes for DNA damage repair protein NBS1 leads to microcephaly, immunodeficiency, and cancer predisposition in Nijmegen breakage syndrome.
One of the most widely cited examples of pleiotropy in humans is phenylketonuria (PKU). This disorder is caused by a deficiency of the enzyme phenylalanine hydroxylase, which is necessary to convert the essential amino acid phenylalanine to tyrosine. A defect in the single gene that codes for this enzyme therefore results in the multiple phenotypes associated with PKU, including mental retardation, eczema, and pigment defects that make affected individuals lighter skinned (Paul, 2000).
The phenotypic effects that single genes may impose in multiple systems often give us insight into the biological function of specific genes. Pleiotropic genes can also provide us valuable information regarding the evolution of different genes and gene families, as genes are “co-opted” for new purposes beyond what is believed to be their original function (Hodgkin, 1998). Quite simply, pleiotropy reflects the fact that most proteins have multiple roles in distinct cell types; thus, any genetic change that alters gene expression or function can potentially have wide-ranging effects in a variety of tissues.
Somewhat ironic, that large genetic studies REMOVE PLEIOTROPY, a “fact” in human genetics that may provide real progress in finding genetic links to physical conditions that are at present lumped together under a phony “autistic pathology” that is based in the “social brain” of neutrotypicals – and not in scientific reality.