Impute.me

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Impute.me.[ dead link ] [1] was an open-source non-profit web application that allowed members of the public to use their data from direct-to-consumer (DTC) genetic tests (including tests from 23andMe and Ancestry.com) to calculate polygenic risk scores (PRS) for complex diseases and cognitive and personality traits. [2] [3] [4] [5] [6] In July 2022, Lasse Folkerson, initiator and operator of impute.me, took the website offline. [7] [8]

Contents

impute.me
Content
DescriptionA polygenic risk score calculator for human diseases and traits
Data types
captured		single-nucleotide polymorphisms, genotypes, genes, variation
Organisms Homo sapiens
Contact
Primary citation PMID   32714365
Access
Website[ dead link ] www.impute.me
Miscellaneous
License GNU Lesser General Public License v3.0

Impute.me calculates PRSs, which are used to estimate the risk of developing complex diseases from the combined effects of numerous common single nucleotide polymorphisms in the human genome. [9] [10]

It is intended for use by people who have obtained genetics data from a direct to consumer genetic testing company. [11] [12] If they upload the files, the uploaded data is expanded into ungenotyped SNPs and the overlap with public GWAS summary statistics used to estimate risk. [2] The data is then subjected to analysis scripts including PRS calculations for approximately 2,000 traits and complex diseases. [2] PRSs are calculated based on the combined effect of all SNPs reported in the summary statistics of the underlying GWAS or of the top, genome-wide significant SNPs in the underlying GWAS. [2] The scores based on all SNPs are only available for about 20 complex diseases and traits. [2] Users can then make use of the web tool GenoPred [13] to translate their PRSs onto an absolute risk scale using summary statistics from the GWAS studies. [14]

Criticisms

Numerous criticisms has been raised against consumers accessing their own genetic information, including findings that more than 30% of direct-to-consumer related contacts to clinical genetics departments involve the use of imputed risk estimates and that third party genetics analysis site generally invoke science's power without accepting its limits, while failing to make clear the limitations and potential dangers. [15] [16] In addition there are concerns that many people will react negatively to accessing their own polygenic risk scores, with findings that over 5% of users score over the threshold for potential post-traumatic stress disorder. [17] Notably, this criticism match the FDA-regulation imposed on the major direct-to-consumer genetics company 23andme. [18]

Related Research Articles

<span class="mw-page-title-main">Single-nucleotide polymorphism</span> Single nucleotide in genomic DNA at which different sequence alternatives exist

In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome that is present in a sufficiently large fraction of considered population.

The International HapMap Project was an organization that aimed to develop a haplotype map (HapMap) of the human genome, to describe the common patterns of human genetic variation. HapMap is used to find genetic variants affecting health, disease and responses to drugs and environmental factors. The information produced by the project is made freely available for research.

Imaging genetics refers to the use of anatomical or physiological imaging technologies as phenotypic assays to evaluate genetic variation. Scientists that first used the term imaging genetics were interested in how genes influence psychopathology and used functional neuroimaging to investigate genes that are expressed in the brain.

<span class="mw-page-title-main">Personalized medicine</span> Medical model that tailors medical practices to the individual patient

Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept though some authors and organisations use these expressions separately to indicate particular nuances.

<span class="mw-page-title-main">Ancestry-informative marker</span>

In population genetics, an ancestry-informative marker (AIM) is a single-nucleotide polymorphism that exhibits substantially different frequencies between different populations. A set of many AIMs can be used to estimate the proportion of ancestry of an individual derived from each population.

<span class="mw-page-title-main">Genome-wide association study</span> Study of genetic variants in different individuals

In genomics, a genome-wide association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

Population structure is the presence of a systematic difference in allele frequencies between subpopulations. In a randomly mating population, allele frequencies are expected to be roughly similar between groups. However, mating tends to be non-random to some degree, causing structure to arise. For example, a barrier like a river can separate two groups of the same species and make it difficult for potential mates to cross; if a mutation occurs, over many generations it can spread and become common in one subpopulation while being completely absent in the other.

In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.

<span class="mw-page-title-main">Behavioural genetics</span> Study of genetic-environment interactions influencing behaviour

Behavioural genetics, also referred to as behaviour genetics, is a field of scientific research that uses genetic methods to investigate the nature and origins of individual differences in behaviour. While the name "behavioural genetics" connotes a focus on genetic influences, the field broadly investigates the extent to which genetic and environmental factors influence individual differences, and the development of research designs that can remove the confounding of genes and environment. Behavioural genetics was founded as a scientific discipline by Francis Galton in the late 19th century, only to be discredited through association with eugenics movements before and during World War II. In the latter half of the 20th century, the field saw renewed prominence with research on inheritance of behaviour and mental illness in humans, as well as research on genetically informative model organisms through selective breeding and crosses. In the late 20th and early 21st centuries, technological advances in molecular genetics made it possible to measure and modify the genome directly. This led to major advances in model organism research and in human studies, leading to new scientific discoveries.

<span class="mw-page-title-main">Exome sequencing</span> Sequencing of all the exons of a genome

Exome sequencing, also known as whole exome sequencing (WES), is a genomic technique for sequencing all of the protein-coding regions of genes in a genome. It consists of two steps: the first step is to select only the subset of DNA that encodes proteins. These regions are known as exons—humans have about 180,000 exons, constituting about 1% of the human genome, or approximately 30 million base pairs. The second step is to sequence the exonic DNA using any high-throughput DNA sequencing technology.

The missing heritability problem is the fact that single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This is a problem that has significant implications for medicine, since a person's susceptibility to disease may depend more on the combined effect of all the genes in the background than on the disease genes in the foreground, or the role of genes may have been severely overestimated.

Imputation in genetics refers to the statistical inference of unobserved genotypes. It is achieved by using known haplotypes in a population, for instance from the HapMap or the 1000 Genomes Project in humans, thereby allowing to test for association between a trait of interest and experimentally untyped genetic variants, but whose genotypes have been statistically inferred ("imputed"). Genotype imputation is usually performed on SNPs, the most common kind of genetic variation.

Predictive genomics is at the intersection of multiple disciplines: predictive medicine, personal genomics and translational bioinformatics. Specifically, predictive genomics deals with the future phenotypic outcomes via prediction in areas such as complex multifactorial diseases in humans. To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in genome-wide association (GWA) studies. The identification of associated single-nucleotide polymorphisms underpin GWA studies in complex diseases that have ranged from Type 2 Diabetes (T2D), Age-related macular degeneration (AMD) and Crohn's disease.

<span class="mw-page-title-main">Polygenic score</span> Numerical score aimed at predicting a trait based on variation in multiple genetic loci

In genetics, a polygenic score (PGS), also called a polygenic index (PGI), polygenic risk score (PRS), genetic risk score, or genome-wide score, is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype, typically calculated as a weighted sum of trait-associated alleles. It reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. In other words, it gives an estimate of how likely an individual is to have a given trait only based on genetics, without taking environmental factors into account. Polygenic scores are widely used in animal breeding and plant breeding due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from genome-wide association study (GWAS) data.

In statistical genetics, linkage disequilibrium score regression is a technique that aims to quantify the separate contributions of polygenic effects and various confounding factors, such as population stratification, based on summary statistics from genome-wide association studies (GWASs). The approach involves using regression analysis to examine the relationship between linkage disequilibrium scores and the test statistics of the single-nucleotide polymorphisms (SNPs) from the GWAS. Here, the "linkage disequilibrium score" for a SNP "is the sum of LD r2 measured with all other SNPs".

Heather Clare Whalley is a Scottish scientist. She is a senior research fellow in Neuroimaging at the Centre for Clinical Brain Sciences, University of Edinburgh., and is an affiliate member of the Centre for Genomic and Experimental Medicine at the University of Edinburgh. Her main focus of research is on the mechanisms underlying the development of major psychiatric disorders using the latest genomic and neuroimaging approaches.

The Omnigenic Model, first proposed by Evan A. Boyle, Yang I. Li, and Jonathan K. Pritchard, describes a hypothesis regarding the heritability of complex traits. Expanding beyond polygenes, the authors propose that all genes expressed within a cell affect the expression of a given trait. In addition, the model states that the peripheral genes, ones that do not have a direct impact on expression, explain more heritability of traits than core genes, ones that have a direct impact on expression. The process that the authors propose that facilitates this effect is called “network pleiotropy”, in which peripheral genes can affect core genes, not by having a direct effect, but rather by virtue of being mediated within the same cell.

The GWAS catalog is a free online database that compiles data of genome-wide association studies (GWAS), summarizing unstructured data from different literature sources into accessible high quality data. It was created by the National Human Genome Research Institute (NHGRI) in 2008 and have become a collaborative project between the NHGRI and the European Bioinformatics Institute (EBI) since 2010. As of September 2018, it has included 71,673 SNP–trait associations in 3,567 publications.

Personality traits are patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances.

Transcriptome-wide association study (TWAS) is a genetic methodology that can be used to compare the genetic components of gene expression and the genetic components of a trait to determine if an association is present between the two components. TWAS are useful for the identification and prioritization of candidate causal genes in candidate gene analysis following genome-wide association studies. TWAS looks at the RNA products of a specific tissue and gives researchers the abilities to look at the genes being expressed as well as gene expression levels, which varies by tissue type. TWAS are valuable and flexible bioinformatics tools that looks at the associations between the expressions of genes and complex traits and diseases. By looking at the association between gene expression and the trait expressed, genetic regulatory mechanisms can be investigated for the role that they play in the development of specific traits and diseases.

References

  1. ↑ "Impute.me". impute.me. Retrieved 2021-09-01.
  2. 1 2 3 4 5 Folkersen, Lasse; Pain, Oliver; Ingason, Andrés; Werge, Thomas; Lewis, Cathryn M.; Austin, Jehannine (2020). "Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores". Frontiers in Genetics. 11: 578. doi: 10.3389/fgene.2020.00578 . ISSN   1664-8021. PMC   7340159 . PMID   32714365.
  3. ↑ Brockman DG, Petronio L, Dron JS, Kwon BC, Vosburg T, Nip L, Tang A, O'Reilly M, Lennon N, Wong B, Ng K, Huang KH, Fahed AC, Khera AV (October 2021). "Design and user experience testing of a polygenic score report: a qualitative study of prospective users". BMC Med Genomics. 14 (1): 238. doi: 10.1186/s12920-021-01056-0 . PMC   8485114 . PMID   34598685.
  4. ↑ Ronald A (2020). "Editorial: Polygenic scores in child and adolescent psychiatry - strengths, weaknesses, opportunities and threats". J Child Psychol Psychiatry. 61 (5): 519–521. doi: 10.1111/jcpp.13246 . PMID   32304105. S2CID   215810502.
  5. ↑ Corpas M, Beck S, Glusman G, Shabani M (2021). "Editorial: Personal Genomes: Accessing, Sharing, and Interpretation". Front Genet. 12: 687584. doi: 10.3389/fgene.2021.687584 . PMC   8213362 . PMID   34149819.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  6. ↑ Zanardi R, Prestifilippo D, Fabbri C, Colombo C, Maron E, Serretti A (2021). "Precision psychiatry in clinical practice". Int J Psychiatry Clin Pract. 25 (1): 19–27. doi:10.1080/13651501.2020.1809680. PMID   32852246. S2CID   221347950.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ↑ @ImputeMe (July 14, 2022). "We are undergoing an evolution to the next level of easy-to-access, high-quality genetic analysis" (Tweet) – via Twitter.
  8. ↑ @lassefolkersen (July 14, 2022). "Start spreading the news, I'm leaving today" (Tweet) – via Twitter.
  9. ↑ Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK; et al. (2021). "From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer". JAMA Psychiatry. 78 (1): 101–109. doi:10.1001/jamapsychiatry.2020.3049. ISSN   2168-622X. PMID   32997097. S2CID   222169651.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  10. ↑ Murray GK, Lin T, Austin J, McGrath JJ, Hickie IB, Wray NR (2021). "Could Polygenic Risk Scores Be Useful in Psychiatry?: A Review". JAMA Psychiatry. 78 (2): 210–219. doi:10.1001/jamapsychiatry.2020.3042. PMID   33052393. S2CID   222351995.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. ↑ France2 (2018). "ADN pour tous". YouTube .
  12. ↑ Vanzo RJ, Prasad A, Staunch L, Hensel CH, Serrano MA, Wassman ER; et al. (2020). "The Temple Grandin Genome: Comprehensive Analysis in a Scientist with High-Functioning Autism". J Pers Med. 11 (1): 21. doi: 10.3390/jpm11010021 . PMC   7824360 . PMID   33383702.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  13. ↑ "Converting Polygenic Score to Absolute Scale". opain.github.io. Retrieved 2021-09-02.
  14. ↑ Pain, Oliver; Gillett, Alexandra C.; Austin, Jehannine C.; Folkersen, Lasse; Lewis, Cathryn M. (2021-04-20). "A Tool for Translating Polygenic Scores onto the Absolute Scale Using Summary Statistics". medRxiv   10.1101/2021.04.16.21255481v1 .
  15. ↑ Millward M, Tiller J, Bogwitz M, Kincaid H, Taylor S, Trainer AH, Lacaze P (September 2020). "Impact of direct-to-consumer genetic testing on Australian clinical genetics services". Eur J Med Genet. 63 (9): 103968. doi:10.1016/j.ejmg.2020.103968. PMID   32502649.
  16. ↑ Charbonneau J, Nicol D, Chalmers D, Kato K, Yamamoto N, Walshe J, Critchley C (March 2020). "Public reactions to direct-to-consumer genetic health tests: A comparison across the US, UK, Japan and Australia". Eur J Hum Genet. 28 (3): 339–348. doi:10.1038/s41431-019-0529-8. PMC   7029038 . PMID   31645768.
  17. ↑ Peck L, Borle K, Folkersen L, Austin J (2021). "Why do people seek out polygenic risk scores for complex disorders, and how do they understand and react to results?". Eur J Hum Genet. 30 (1): 81–87. doi:10.1038/s41431-021-00929-3. PMC   8738734 . PMID   34276054.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  18. ↑ Yim SH, Chung YJ (2014). "Reflections on the US FDA's Warning on Direct-to-Consumer Genetic Testing". Genomics Inform. 12 (4): 151–5. doi:10.5808/GI.2014.12.4.151. PMC   4330248 . PMID   25705152.
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