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Full genome search for associations

depicts some closely related risk loci. Each point is a single nucleotide polymorphism , whose location in the genome is shown on the X axis, and on the Y axis, the level of shown. This example was taken from studies of genome-wide associations for microcirculation disorders in blood vessels [1]

Full genome search associations [2] ( eng. Genome-wide association studies , GWA study, GWAS ) - the direction of biological (usually biomedical ) research related to the study of associations between genomic variants and phenotypic traits. Often, a full-genome search for associations implies only a search for links between single-nucleotide polymorphisms ( single-nucleotide polymorphism, SNP ) and human diseases, but the term is also used for other organisms. The main goal of a genome-wide search for associations is to identify genetic risk factors in order to give a reasonable prediction of susceptibility to the disease, as well as to identify the biological basis of susceptibility to the disease to develop new strategies for prevention and treatment [3] .

In studies of this type, the genomes of a group of sick people with different phenotypes are usually compared with the genomes of the control group, including those similar in age, sex and other signs of healthy people. With the help of GWAS, it is possible to compare not only the genomes of patients, but also healthy people with different manifestations of the same phenotypic trait. The material for the study are the genomic DNA samples of each research participant in which SNPs are searched for using microchips . If it is possible to identify the variants of the genomes (more precisely, the totality of alleles ), which are significantly more common in people with this disease, then they say that this variant is associated (or associated) with the disease. Unlike methods that test one or more specific genome regions, the full genomic search for associations uses the complete DNA sequence. It should be noted that this approach to research does not reveal the mutations that caused the disease, but only a more or less significant correlation with the disease or another symptom [4] [5] [6] . For example, using GWAS, SNP was identified (replacing G with A ) in the 5'-untranslated region of the gene , which is associated with an increased risk of thyroid cancer [7] .

The second most important area of ​​application for genome-wide analysis of associations is pharmacogenetics , that is, the search for alleles associated with the metabolism of drugs and their side effects [3] .

History

The results of the first successful full-genome search for associations were published in 2002; Researchers searched for genomic variants associated with a predisposition to myocardial infarction [8] . In 2005, the GWAS method was applied to the group of patients with macular dystrophy . As a result of the study, two autosomal single nucleotide polymorphisms associated with macular dystrophy were found [9] . As of 2017, thousands of people have participated in GWAS research. In more than 3,000 GWAS projects, more than 1,800 diseases and phenotypic traits were studied, and more than a thousand SNPs associated with diseases were identified according to research results [10] .

Prerequisites

 
Studies of genome-wide associations, as a rule, are applied to common genetic variants with a weak effect (bottom right)

The genomes of any two people have a huge number of differences. It can be both single nucleotide polymorphisms, and larger changes: deletions , insertions and changes in the copy number of genes . Any of these differences may be responsible for individual characteristics of the individual (for example, eye color, hair color) [11] or cause disease [12] . Before the advent of methods for a full genomic search for associations, studies were based on an analysis of chained inheritance in families. This approach has been very effective in identifying genes responsible for diseases with simple Mendelian inheritance , such as cystic fibrosis . However, such genetic studies proved to be ineffective in identifying the causes of more complex diseases [13] . As an alternative to this method, a full-genome search for associations was proposed. This type of research is based on analyzing the frequency of alleles of various genes among individuals. If, when comparing, these or other alleles of genes are found in people with the studied phenotype (for example, in carriers of the disease) significantly more often than in others, then there is reason to assume that these alleles are responsible for the manifestation of this phenotype. The readings of the power of statistical tests used for the genome-wide search for associations showed that this method is better than others, such as the linkage study, suitable for detecting weak genetic effects [14] .

Some additional factors also influenced the development of research on the search for genome-wide associations. One of them was the emergence of , which are repositories of human genetic material , which facilitated the collection of biological samples for research [15] . Another such factor turned out to be the international project HapMap , which is a catalog of single nucleotide polymorphisms (SNP) [16] . Of great importance was the development of genotyping of all SNPs using [17] .

Methods

The search for genome-wide associations, as a rule, is based on a comparison of the genomes of two groups of people: carriers of the studied phenotype (disease) and the control group. done for all individuals for most known single nucleotide polymorphisms (SNPs) using DNA microarrays. The number of SNPs included in the analysis depends on the method of genotyping, but, as a rule, it is not less than a million [3] . Sequencing in GWAS is not used. Further, for each SNP, it is checked how significant are the differences in the frequency distribution of alleles between the studied and control groups [18] . In these studies, the key parameter characterizing the severity of differences is the odds ratio . The odds ratio is the ratio of the likelihood that an individual with a particular allele suffers from the disease being studied, and the ratio of the likelihood of having a disease for an individual who does not have this allele. If the frequency of a certain allele is much higher in the studied sample than in the control group, the odds ratio is greater than 1 and less than 1, if in the studied sample a certain allele is rarer than in the control one. In addition, using the χ² test, a P-value is calculated, which characterizes the significance of the odds ratio. The goal of GWAS is to identify odds ratios greater than 1, since they indicate SNPs associated with the disease [18] .

An alternative to dividing into two groups in genome-wide studies is a quantitative analysis of the phenotype, for example, growth, biomarker concentration, or gene expression . In addition, data on the penetrance of the studied alleles can be used [18] . Calculations are usually performed using bioinformatics programs such as SNPTEST and PLINK, which take into account various alternative statistics [19] [20] . Initially, GWAS focused on the effects of individual SNPs. However, studies have shown that the interaction of several SNPs - epistasis can affect the development of complex diseases. In addition, researchers are currently trying to link GWAS data with other biological data, for example, with a network of protein-protein interactions in order to get the most informative results [21] [22] .

The key stage of GWAS is the genotypes on an , which contains a large number of variants of different SNPs [23] . Through this step, it is possible to increase the number of SNPs that should be tested for association with the phenotype under study, increase the scope of the study and facilitate further meta-analysis of the GWAS results in different cohorts. Genotype imputation is carried out using special statistical methods that “superimpose” GWAS data on a reference panel with control haplotypes . Imputation of alleles is greatly helped by the presence of identical sequences in haplotypes obtained from different individuals. The imputation of genotypes can be done using the IMPUTE2 [24] and MaCH [25] programs.

GWAS also takes into account variables that can affect both dependent and independent variables in the analysis. Such variables can be, for example, age and gender. Moreover, it is known that many genetic variations are associated with the geographical and historical populations in which they appeared [26] . In this regard, the analysis should take into account the ethnic and geographical affiliation of the research participant, controlling the . If you do not take into account these parameters, the analysis can give false positive results [27] .

After the odds ratios and P-values ​​have been calculated for all SNPs, a is created. In this graph, the negative logarithm of the P-value is considered as a function of the coordinates in the genome. Therefore, SNP with the strongest influence will not fall on the chart. In addition, the P-values, indicating the significance, is subjected to correction for multiple testing . The threshold value may be different in different studies [28] , but most often it is assumed to be 5 × 10 −8 for analysis, in which hundreds of thousands and millions of SNPs [3] [18] [29] were considered .

Results

Attempts are being made to create comprehensive catalogs of single nucleotide polymorphisms associated with various traits [30] . The number of loci found varies greatly depending on the disease: from a few in the case of mental illness to a hundred or more in the case of , for example, Crohn's disease or ulcerative colitis [31] .

In the first study on a genome-wide search for associations, conducted in 2005, age-related macular dystrophy was investigated. The study involved 96 patients and 50 healthy people [32] . Two single nucleotide polymorphisms were found with a significant difference in frequencies in the two groups. These polymorphisms were located in the gene of the complement system . This study spurred further research on this protein [3] [33] .

Another important milestone in the history of the full genome search for associations is case-control research by a consortium of the Wellcome Trust . At the time of publication (2007) it was the largest study of this type. The sample consisted of 14,000 cases of common diseases of 2,000 cases each. Diseases considered include coronary insufficiency , type 1 diabetes mellitus, type 2 diabetes mellitus , rheumatoid arthritis , Crohn's disease, bipolar disorder, and arterial hypertension . The size of the control group was 3,000 people [19] . 500 thousand genetic variations and 10 genes were identified that determine the predisposition to these diseases [19] [34] .

After the first successful studies, two directions for the further development of GWAS were outlined [35] . One of them was to increase the sample under study. By 2018, several GWAS were conducted, in which more than a million people were part of the study group. For example, 1.1 million people were involved in the search for the genetic basis of the 36] , and 1.3 million people participated in the study of insomnia [37] . An increase in the sample under study allows us to identify disease-related SNPs that have a lower odds ratio and a lower incidence of potentially dangerous alleles. The second direction is to use as narrow as possible phenotypic traits, such as blood concentration, proinsulin, and other biomarkers [38] [39] . They are called intermediate phenotypes, and their analysis is very important for functional studies of biomarkers [40] . In some embodiments, GWAS examines the immediate blood relatives of the patients. They got the name GWAX from English. genome-wide association study by proxy [41] .

The main controversial point regarding GWAS is that most SNPs identified using GWAS only increase the risk of disease very little and have little predictive power. The median odds ratio is 1.33 per SNP associated with the disease, and only for some of them is the odds ratio greater than 3 [4] [42] . Values ​​of this order are considered small because they do not explain most of the inherited variations. Inherited variations are usually studied on identical twins [43] . For example, it has been established that 80–90% of growth variations are hereditary in nature, but GWAS significantly underestimated this indicator [43] .

Medical use

One of the challenges for the future is the use of a full-genome search for associations in development and the development of diagnostics [44] . Some studies of the use of single nucleotide polymorphisms to improve the accuracy of the prognosis of diseases have been conducted, but the significance of this application remains the subject of controversy [45] [46] . In general, the problem with this approach is a weak observed effect, which practically does not contribute to an increase in the accuracy of the forecast. However, this approach has found successful application in pathophysiology [47] . One example of this is the identification of a genetic variant associated with the response to treatment for hepatitis C. Hepatitis C treatment of genotype 1 with pegylated interferon alfa-2a or combined with ribavirin has been shown to cause different responses associated with single nucleotide polymorphisms next to the human gene encoding [48] . It has also been demonstrated that the same genetic variants are responsible for the spontaneous self-healing of the genotype 1 [49] .

The introduction of GWAS into pathophysiology has fueled interest in finding links between risk-associated SNPs and the expression of neighboring genes, known as (eQTL ) [50] . The fact is that GWAS identifies SNPs that are associated with risk, not genes, but it is the affected genes that are important for drug development. Therefore, since 2011, large GWAS include eQTL analysis [51] [52] [53] . One of the most prominent eQTLs associated with the identified GWAS SNP is the locus . The study of this locus using small interfering RNA and knockout mice clarified many aspects of the metabolism of low density lipoproteins , which are important for the development of cardiovascular diseases [38] [54] [55] .

Limitations

There are some problems and limitations associated with the full-genome search for associations, and the quality control and research design methods used in this connection. The lack of clearly defined test and control samples, insufficient sample size, the need for correction for multiple testing and control of population stratification are the main difficulties [5] . In this regard, it was noted that "the approach of a full-genome search for associations can be problematic, because a huge number of statistical tests provide an unprecedented opportunity for false-positive results" [5] . However, in addition to these easily eliminated difficulties, many non-trivial problems are associated with GWAS. For example, they floated to the surface with a high-profile GWAS, aimed at finding SNP associated with longevity, on a sample of individuals with very long lifespans [56] . The publication was subjected to severe criticism because of the inconsistency of genotyping chips for the studied and control groups, due to which many SNPs were mistakenly associated with longevity [57] . The article was withdrawn [58] , but after revision it was nevertheless published [59] .

GWAS has been criticized more globally, mainly due to the assumption that common genetic variations play a large role in the hereditary nature of common diseases [31] . Moreover, a sharp decline in the price for shown the possibility of an alternative to GWAS based on genotyping microchips [60] .

Notes

  1. ↑ Ikram MK , Sim X. , Jensen RA , Cotch MF , Hewitt AW , Ikram MA , Wang JJ , Klein R. , Klein BE , Breteler MM , Cheung N. , Liew G. , Mitchell P. , Uitterlinden AG , Rivadeneira F CM , Hofman A. , de Jong PT , van Duijn CM , Kao L. , Cheng CY , Smith AV , Glazer NL , Lumley T. , McKnight B. , Psaty BM , Jonasson F. , Eiriksdottir G. , Aspelund T. , Harris TB , Launer LJ , Taylor KD , Li X. , Iyengar SK , Xi Q. , Sivakumaran TA , Mackey DA , Macgregor S. , Martin NG , Young TL , Bis JC , Wiggins KL , Andrew T . , Fahy S. , Attia J. , Holliday EG , Scott RJ , Islam FM , Rotter JI , McAuley AK , Boerwinkle E. , Tai ES , Gudnason V. , Siscovick DS , Vingerling JR , Wong TY Four novel Loc Loc (19q13, 6q24, 12q24, and 5q14) influence the microcirculation in vivo. (English) // PLoS genetics. - 2010. - Vol. 6, no. 10 - P. e1001184. - DOI : 10.1371 / journal.pgen.1001184 . - PMID 21060863 .
  2. ↑ Utkin Lev Vladimirovich , Zhuk Yulia Alexandrovna. A Genome-Wide Association Study using Pairwise Comparison Matrices (Eng.) // SPIIRAS Proceedings. - 2016. - 1 August ( vol. 4 , no. 47 ). - P. 225 . - ISSN 2078-9599 . - DOI : 10.15622 / sp.47.12 .
  3. 2 1 2 3 4 5 Bush WS , Moore JH Chapter 11: Genome-wide association studies. (eng.) // Public Library of Science for Computational Biology. - 2012. - Vol. 8, no. 12 - P. e1002822. - DOI : 10.1371 / journal.pcbi.1002822 . - PMID 23300413 .
  4. 2 1 2 Manolio TA Genomewide association studies and assessment of risk of disease. (English) // The New England journal of medicine. - 2010. - Vol. 363, no. 2 - P. 166-176. - DOI : 10.1056 / NEJMra0905980 . - PMID 20647212 .
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  6. ↑ Genome-Wide Association Studies (Undec.) . National Human Genome Research Institute.
  7. ↑ Fareed Mohd , Afzal Mohammad. A single nucleotide polymorphism in the genome-wide association of the human population: A tool for the broad spectrum service (Eng.) // Egyptian Journal of Medical Human Genetics. - 2013. - April ( vol. 14 , no. 2 ). - P. 123-134 . - ISSN 1110-8630 . - DOI : 10.1016 / j.ejmhg.2012.08.001 .
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Source - https://ru.wikipedia.org/w/index.php?title=Pologenomic search_associations&oldid = 101140115


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