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Genome-Wide Association Studies (GWAS) in Complex Diseases: Advantages and Limitations
Enfermedades complejas y análisis genéticos por el método GWAS. Ventajas y limitaciones
José A. Riancho
Departamento de Medicina Interna, Hospital Universitario Marqués de Valdecilla-IFIMAV, Universidad de Cantabria, Santander, Spain
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Riancho" "autores" => array:1 [ 0 => array:3 [ "nombre" => "José A." "apellidos" => "Riancho" "email" => array:1 [ 0 => "rianchoj@unican.es" ] ] ] "afiliaciones" => array:1 [ 0 => array:1 [ "entidad" => "Departamento de Medicina Interna, Hospital Universitario Marqués de Valdecilla-IFIMAV, Universidad de Cantabria, Santander, Spain" ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Enfermedades complejas y análisis genéticos por el método GWAS. Ventajas y limitaciones" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Classic inherited diseases are caused by a single gene mutation, often with serious consequences for the organism, but are fortunately rare. Acquired diseases, on the contrary, are due to environmental factors. However, many of the most prevalent diseases are actually the result of the combination of hereditary and environmental factors. Many common disorders such as osteoporosis, arthritis, diabetes or hypertension tend to cluster in families, reflecting their hereditary component (although there may also be shared environmental factors). The importance of hereditary factors in osteoporosis is large and, for example, accounts for between 50% and 80% of the variability of bone mass.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> Genetic epidemiology studies showed that, unlike classical hereditary diseases, the risk for these disorders is not explained by the alteration of a single gene. Hence the name of “polygenic” or “complex” diseases.</p><p id="par0010" class="elsevierStylePara elsevierViewall">By sequencing the human genome it was ascertained that there are many inter-individual variations in DNA. Contrary to mutations, these variations are quite common and in many cases their functional impact is limited (in fact, most occur in non-coding regions) and were called “polymorphisms”. Among them, the single nucleotide polymorphisms (SNPs) consist of simply the change of one base for another. They are very frequent, about 15 million in the genome. There are also frequent repeat polymorphisms, which consist of groups of a few nucleotides that are repeated variable number of times in individuals. More recently, another form of DNA variation was identified and called “variation in the number of copies.” It consists of relatively large regions of the genome, thousands of nucleotides, which in some individuals are repeated on the same or on different chromosomes.</p><p id="par0015" class="elsevierStylePara elsevierViewall">The fact that complex diseases do not follow a classic pattern of inheritance, i.e. they cannot be explained by a single gene disorder, along with the discovery of the frequency of polymorphisms led to the hypothesis of “common illnesses common variants”. This assumes that common complex diseases are the result of the combined effect of many common polymorphisms in the population.</p><p id="par0020" class="elsevierStylePara elsevierViewall">After the epidemiological studies demonstrated the important genetic component of these disorders, the researchers set out to try to identify the genes and polymorphisms involved. To do this, based on knowledge of the biology and pathogenesis of disease, “candidate genes” were identified and its association with these disorders explored. For example, given the important role of vitamin D and sex hormones on bone metabolism, some of the first candidate gene studies examined the association of polymorphisms of the vitamin D and estrogen receptors with osteoporosis.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">While their design allows multiple variations, these studies in essence choose a candidate gene, identify some of its polymorphisms and examine whether polymorphic alleles at these loci are associated with a particular phenotypic trait or frequency of a disease. Subsequently, there have been many studies on other candidate genes in relation to a variety of disorders, but overall they have not responded to the expectations generated by the attractive hypothesis that sustained them. The results obtained by some researchers are often not replicated in other studies and the strength of association between genotype and phenotype has generally been small.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Further development of microarrays made it possible to analyze hundreds of thousands of SNPs in an efficient manner, with a small DNA sample and a much lower cost than would be needed to study SNP individually. Furthermore, since these SNPs distributed throughout the chromosomes, a new approach was possible: to explore the whole genome without prior hypothesis, i.e. without previously selecting candidate genes. SNPs included in the microarray were selected considering the patterns of linkage, so that other polymorphisms that are not directly related are also captured. These genome-wide association studies (GWAS) raised high expectations. It was thought that they would finally allow identifying all the genes in the heritability of complex diseases. In fact, since 2005, some 1200 GWAS SNP associations have been published with more than 200 diseases or phenotypic traits described (you can find a listing on <a href="http://www.genome.gov/GWAStudies">www.genome.gov/GWAStudies</a>).</p><p id="par0035" class="elsevierStylePara elsevierViewall">However, the results of GWAS have not responded to initial expectations. Many genes found associated with a particular disease have no known biological effects to explain this relationship. However, this may not be but a reflection of the incompleteness of our knowledge. In fact, those findings are being used to identify new pathogenic mechanisms and new therapeutic targets.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> More surprising is the fact that, even when combining all available GWAS on a particular disorder, polymorphisms usually associated explain less than 5%–10% of the risk of disease.</p><p id="par0040" class="elsevierStylePara elsevierViewall">How is it possible that studies that analyzed 500<span class="elsevierStyleHsp" style=""></span>000 SNPs in several thousand individuals shed these poor results in terms of risk prediction? One reason may be the power of the studies. By exploring many SNPs there is a high risk of finding false significant associations according to conventional criteria of <span class="elsevierStyleItalic">P</span><0.05. In fact, when 500<span class="elsevierStyleHsp" style=""></span>000 SNPs were analyzed, one would expect to find over 25<span class="elsevierStyleHsp" style=""></span>000 diseases associated with a <span class="elsevierStyleItalic">P</span>-value <.05 simply by chance. To avoid this error, a correction for multiple comparisons is employed, so that associations are considered significant with a <span class="elsevierStyleItalic">P</span> less than 10<span class="elsevierStyleSup">−7</span> or 10<span class="elsevierStyleSup">−8</span>. This approach reduces false positives, but also markedly decreases the power to detect SNPs associated with disease.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> To increase power one can only increase the sample size, or use larger studies, which is increasingly common, using meta-analysis of several studies. However, it is estimated that GWAS have been identified and presumably more than 80%–90% of common SNPs are associated with prevalent disorders such as osteoporosis, with an odds ratio greater than 1.1–1.2. Therefore, we think that if studies are extended, it is likely that other SNPs are also associated with disease, but the individual influence of each of them will presumably be very small.<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7,8</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">So what determines that much of the risk of disease still remains unexplained? The answer to this question is uncertain. One possibility is that the prevalent diseases are not the result of common variants with relatively small individual effects (such as assuming the hypothesis of common diseases, common variants), but rare variants with relatively large effects are not identified in GWAS. Keep in mind that, by design, the microarrays used in GWAS are unable to detect the influence of SNPs with rare alleles with populations that have frequencies of less than 1%–10%. It is also possible that differences in risk are due to the interaction between different SNPs or between SNPs and environmental factors. Neither individual GWAS studies nor the meta-analyses conducted so far have enough power to unravel these interactions. We cannot exclude that other forms of genetic variation, such as repeat polymorphisms or variations in the number of copies, which have hardly been explored, play an important role. Possibly epigenetic mechanisms (which are potentially heritable and can modulate the expression of genes without involving changes in DNA sequence) also significantly influence the risk of disease. It is also possible that there is a high genetic heterogeneity in the pathogenesis of these diseases. Indeed, the combination of several studies using meta-analysis increases power to detect genetic factors common to all the populations studied. But that strategy is not necessarily effective when combining studies with groups of individuals in which the influential genes are different. For example, polymorphisms of the aromatase enzyme that converts androgenic precursors into estrogens in peripheral tissues are associated with bone mass in postmenopausal women, but not in young women with active ovarian estrogen production.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> Clearly, this association may be masked in a meta-analysis with mixed pre-and postmenopausal women.</p><p id="par0050" class="elsevierStylePara elsevierViewall">In short, GWAS represent a huge technological advance that has identified new genes associated with various diseases. This offers interesting possibilities for the development of new treatments, but so far the results have been disappointing in predicting the overall risk of disease. Thus, researchers in this field have before finding out what this still unknown ‘dark matter’ explains regarding the heritability of complex diseases.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Disclosure</span><p id="par0055" class="elsevierStylePara elsevierViewall">The authors have no disclosures to make.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Disclosure" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara">Please, cite this article as: Riancho JA. Enfermedades complejas y análisis genéticos por el método GWAS. Ventajas y limitaciones. Reumatol Clin. 2012; <span class="elsevierStyleBold">8(2)</span>: 56–7.</p>" ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:9 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Osteoporosis as an hereditary disease" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "S.H. 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Amado" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Eur J Endocrinol" "fecha" => "2004" "volumen" => "150" "paginaInicial" => "699" "paginaFinal" => "704" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/15132727" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/21735743/0000000800000002/v1_201305061636/S217357431200007X/v1_201305061636/en/main.assets" "Apartado" => array:4 [ "identificador" => "17335" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Editorial" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/21735743/0000000800000002/v1_201305061636/S217357431200007X/v1_201305061636/en/main.pdf?idApp=UINPBA00004M&text.app=https://reumatologiaclinica.org/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S217357431200007X?idApp=UINPBA00004M" ]
| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 15 | 17 | 32 |
| 2024 October | 84 | 47 | 131 |
| 2024 September | 88 | 35 | 123 |
| 2024 August | 93 | 58 | 151 |
| 2024 July | 88 | 31 | 119 |
| 2024 June | 141 | 48 | 189 |
| 2024 May | 93 | 34 | 127 |
| 2024 April | 99 | 36 | 135 |
| 2024 March | 98 | 48 | 146 |
| 2024 February | 77 | 50 | 127 |
| 2024 January | 63 | 39 | 102 |
| 2023 December | 77 | 40 | 117 |
| 2023 November | 109 | 70 | 179 |
| 2023 October | 103 | 48 | 151 |
| 2023 September | 127 | 48 | 175 |
| 2023 August | 56 | 31 | 87 |
| 2023 July | 68 | 26 | 94 |
| 2023 June | 54 | 35 | 89 |
| 2023 May | 50 | 22 | 72 |
| 2023 April | 58 | 10 | 68 |
| 2023 March | 76 | 33 | 109 |
| 2023 February | 59 | 14 | 73 |
| 2023 January | 72 | 34 | 106 |
| 2022 December | 67 | 57 | 124 |
| 2022 November | 68 | 41 | 109 |
| 2022 October | 65 | 29 | 94 |
| 2022 September | 53 | 44 | 97 |
| 2022 August | 42 | 51 | 93 |
| 2022 July | 63 | 52 | 115 |
| 2022 June | 61 | 49 | 110 |
| 2022 May | 76 | 47 | 123 |
| 2022 April | 83 | 58 | 141 |
| 2022 March | 77 | 64 | 141 |
| 2022 February | 73 | 42 | 115 |
| 2022 January | 105 | 43 | 148 |
| 2021 December | 83 | 45 | 128 |
| 2021 November | 83 | 39 | 122 |
| 2021 October | 96 | 52 | 148 |
| 2021 September | 75 | 51 | 126 |
| 2021 August | 63 | 43 | 106 |
| 2021 July | 59 | 26 | 85 |
| 2021 June | 91 | 42 | 133 |
| 2021 May | 109 | 46 | 155 |
| 2021 April | 374 | 93 | 467 |
| 2021 March | 210 | 59 | 269 |
| 2021 February | 128 | 48 | 176 |
| 2021 January | 121 | 37 | 158 |
| 2020 December | 98 | 40 | 138 |
| 2020 November | 87 | 22 | 109 |
| 2020 October | 64 | 27 | 91 |
| 2020 September | 57 | 30 | 87 |
| 2020 August | 34 | 26 | 60 |
| 2020 July | 36 | 25 | 61 |
| 2020 June | 50 | 22 | 72 |
| 2020 May | 39 | 26 | 65 |
| 2020 April | 22 | 17 | 39 |
| 2020 March | 24 | 9 | 33 |
| 2020 January | 16 | 0 | 16 |
| 2019 September | 4 | 0 | 4 |
| 2019 June | 2 | 0 | 2 |
| 2019 March | 2 | 0 | 2 |
| 2019 January | 1 | 0 | 1 |
| 2018 May | 4 | 0 | 4 |
| 2018 April | 119 | 5 | 124 |
| 2018 March | 127 | 10 | 137 |
| 2018 February | 64 | 5 | 69 |
| 2018 January | 113 | 8 | 121 |
| 2017 December | 76 | 13 | 89 |
| 2017 November | 74 | 6 | 80 |
| 2017 October | 76 | 7 | 83 |
| 2017 September | 73 | 11 | 84 |
| 2017 August | 81 | 14 | 95 |
| 2017 July | 79 | 9 | 88 |
| 2017 June | 107 | 25 | 132 |
| 2017 May | 113 | 13 | 126 |
| 2017 April | 94 | 8 | 102 |
| 2017 March | 89 | 14 | 103 |
| 2017 February | 65 | 11 | 76 |
| 2017 January | 56 | 6 | 62 |
| 2016 December | 122 | 16 | 138 |
| 2016 November | 121 | 11 | 132 |
| 2016 October | 120 | 15 | 135 |
| 2016 September | 130 | 11 | 141 |
| 2016 August | 89 | 6 | 95 |
| 2016 July | 52 | 16 | 68 |
| 2016 April | 1 | 0 | 1 |
| 2015 December | 2 | 0 | 2 |
| 2015 October | 0 | 21 | 21 |
| 2015 September | 1 | 0 | 1 |
| 2015 August | 1 | 0 | 1 |
| 2015 July | 33 | 8 | 41 |
| 2015 June | 51 | 6 | 57 |
| 2015 May | 127 | 22 | 149 |
| 2015 April | 99 | 12 | 111 |
| 2015 March | 96 | 11 | 107 |
| 2015 February | 102 | 5 | 107 |
| 2015 January | 138 | 16 | 154 |
| 2014 December | 116 | 7 | 123 |
| 2014 November | 81 | 13 | 94 |
| 2014 October | 86 | 11 | 97 |
| 2014 September | 79 | 7 | 86 |
| 2014 August | 73 | 14 | 87 |
| 2014 July | 77 | 14 | 91 |
| 2014 June | 107 | 15 | 122 |
| 2014 May | 111 | 17 | 128 |
| 2014 April | 89 | 19 | 108 |
| 2014 March | 104 | 22 | 126 |
| 2014 February | 88 | 12 | 100 |
| 2014 January | 79 | 11 | 90 |
| 2013 December | 94 | 21 | 115 |
| 2013 November | 79 | 13 | 92 |
| 2013 October | 89 | 7 | 96 |
| 2013 September | 77 | 12 | 89 |
| 2013 August | 75 | 17 | 92 |
| 2013 July | 75 | 16 | 91 |
| 2013 June | 80 | 22 | 102 |
| 2013 May | 124 | 22 | 146 |
| 2013 April | 86 | 19 | 105 |
| 2013 March | 128 | 28 | 156 |
| 2013 February | 110 | 28 | 138 |
| 2013 January | 32 | 22 | 54 |
| 2012 December | 41 | 12 | 53 |
| 2012 November | 33 | 16 | 49 |
| 2012 October | 16 | 8 | 24 |
| 2012 September | 9 | 3 | 12 |
| 2012 July | 3 | 0 | 3 |
| 2012 June | 2 | 0 | 2 |
| 2012 May | 4 | 0 | 4 |
| 2012 April | 9 | 0 | 9 |
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