Rare diseases

Rare diseases constitute a large number of different disorders, of which the vast majority are severe genetic chromosomal or single-gene disorders. Although individually rare, these disorders are collectively common with an estimated population prevalence of 3.5–6% (Nguengang Wakap et al., Reference Nguengang Wakap, Lambert, Olry, Rodwell, Gueydan, Lanneau, Murphy, Le Cam and Rath2020). The most important factor for effective treatment and preventive measures is a molecular diagnosis. With an exact diagnosis, patients can be offered personalised management, healthy relatives may receive carrier testing and prenatal diagnosis is possible.

To identify the disease-causing pathogenic variants in rare genetic diseases, WGS is emerging as a powerful first-line screening test regularly used in routine diagnostics (Lionel et al., Reference Lionel, Costain, Monfared, Walker, Reuter, Hosseini, Thiruvahindrapuram, Merico, Jobling, Nalpathamkalam, Pellecchia, Sung, Wang, Bikangaga, Boelman, Carter, Cordeiro, Cytrynbaum, Dell, Dhir, Dowling, Heon, Hewson, Hiraki, Inbar-Feigenberg, Klatt, Kronick, Laxer, Licht, MacDonald, Mercimek-Andrews, Mendoza-Londono, Piscione, Schneider, Schulze, Silverman, Siriwardena, Snead, Sondheimer, Sutherland, Vincent, Wasserman, Weksberg, Shuman, Carew, Szego, Hayeems, Basran, Stavropoulos, Ray, Bowdin, Meyn, Cohn, Scherer and Marshall2018; Investigators et al., Reference Smedley, Smith, Martin, Thomas, McDonagh, Cipriani, Ellingford, Arno, Tucci, Vandrovcova, Chan, Williams, Ratnaike, Wei, Stirrups, Ibanez, Moutsianas, Wielscher, Need, Barnes, Vestito, Buchanan, Wordsworth, Ashford, Rehmstrom, Li, Fuller, Twiss, Spasic-Boskovic, Halsall, Floto, Poole, Wagner, Mehta, Gurnell, Burrows, James, Penkett, Dewhurst, Graf, Mapeta, Kasanicki, Haworth, Savage, Babcock, Reese, Bale, Baple, Boustred, Brittain, de Burca, Bleda, Devereau, Halai, Haraldsdottir, Hyder, Kasperaviciute, Patch, Polychronopoulos, Matchan, Sultana, Ryten, Tavares, Tregidgo, Turnbull, Welland, Wood, Snow, Williams, Leigh, Foulger, Daugherty, Niblock, Leong, Wright, Davies, Crichton, Welch, Woods, Abulhoul, Aurora, Bockenhauer, Broomfield, Cleary, Lam, Dattani, Footitt, Ganesan, Grunewald, Compeyrot-Lacassagne, Muntoni, Pilkington, Quinlivan, Thapar, Wallis, Wedderburn, Worth, Bueser, Compton, Deshpande, Fassihi, Haque, Izatt, Josifova, Mohammed, Robert, Rose, Ruddy, Sarkany, Say, Shaw, Wolejko, Habib, Burns, Hunter, Grocock, Humphray, Robinson, Haendel, Simpson, Banka, Clayton-Smith, Douzgou, Hall, Thomas, O’Keefe, Michaelides, Moore, Malka, Pontikos, Browning, Straub, Gorman, Horvath, Quinton, Schaefer, Yu-Wai-Man, Turnbull, McFarland, Taylor, O’Connor, Yip, Newland, Morris, Polke, Wood, Campbell, Camps, Gibson, Koelling, Lester, Nemeth, Palles, Patel, Roy, Sen, Taylor, Cacheiro, Jacobsen, Seaby, Davison, Chitty, Douglas, Naresh, McMullan, Ellard, Temple, Mumford, Wilson, Beales, Bitner-Glindzicz, Black, Bradley, Brennan, Burn, Chinnery, Elliott, Flinter, Houlden, Irving, Newman, Rahman, Sayer, Taylor, Webster, Wilkie, Ouwehand, Raymond, Chisholm, Hill, Bentley, Scott, Fowler, Rendon and Caulfield2021; Stranneheim et al., Reference Stranneheim, Lagerstedt-Robinson, Magnusson, Kvarnung, Nilsson, Lesko, Engvall, Anderlid, Arnell, Johansson, Barbaro, Bjorck, Bruhn, Eisfeldt, Freyer, Grigelioniene, Gustavsson, Hammarsjo, Hellstrom-Pigg, Iwarsson, Jemt, Laaksonen, Enoksson, Malmgren, Naess, Nordenskjold, Oscarson, Pettersson, Rasi, Rosenbaum, Sahlin, Sardh, Stodberg, Tesi, Tham, Thonberg, Tohonen, von Dobeln, Vassiliou, Vonlanthen, Wikstrom, Wincent, Winqvist, Wredenberg, Ygberg, Zetterstrom, Marits, Soller, Nordgren, Wirta, Lindstrand and Wedell2021). The diagnostic yields vary between different disease categories and centres, depending on inclusion criteria and whether a trio or singleton approach was used (Figure 3). For individuals with intellectual disability, WGS yields diagnostic rates between 30 and 40% (Investigators et al., Reference Smedley, Smith, Martin, Thomas, McDonagh, Cipriani, Ellingford, Arno, Tucci, Vandrovcova, Chan, Williams, Ratnaike, Wei, Stirrups, Ibanez, Moutsianas, Wielscher, Need, Barnes, Vestito, Buchanan, Wordsworth, Ashford, Rehmstrom, Li, Fuller, Twiss, Spasic-Boskovic, Halsall, Floto, Poole, Wagner, Mehta, Gurnell, Burrows, James, Penkett, Dewhurst, Graf, Mapeta, Kasanicki, Haworth, Savage, Babcock, Reese, Bale, Baple, Boustred, Brittain, de Burca, Bleda, Devereau, Halai, Haraldsdottir, Hyder, Kasperaviciute, Patch, Polychronopoulos, Matchan, Sultana, Ryten, Tavares, Tregidgo, Turnbull, Welland, Wood, Snow, Williams, Leigh, Foulger, Daugherty, Niblock, Leong, Wright, Davies, Crichton, Welch, Woods, Abulhoul, Aurora, Bockenhauer, Broomfield, Cleary, Lam, Dattani, Footitt, Ganesan, Grunewald, Compeyrot-Lacassagne, Muntoni, Pilkington, Quinlivan, Thapar, Wallis, Wedderburn, Worth, Bueser, Compton, Deshpande, Fassihi, Haque, Izatt, Josifova, Mohammed, Robert, Rose, Ruddy, Sarkany, Say, Shaw, Wolejko, Habib, Burns, Hunter, Grocock, Humphray, Robinson, Haendel, Simpson, Banka, Clayton-Smith, Douzgou, Hall, Thomas, O’Keefe, Michaelides, Moore, Malka, Pontikos, Browning, Straub, Gorman, Horvath, Quinton, Schaefer, Yu-Wai-Man, Turnbull, McFarland, Taylor, O’Connor, Yip, Newland, Morris, Polke, Wood, Campbell, Camps, Gibson, Koelling, Lester, Nemeth, Palles, Patel, Roy, Sen, Taylor, Cacheiro, Jacobsen, Seaby, Davison, Chitty, Douglas, Naresh, McMullan, Ellard, Temple, Mumford, Wilson, Beales, Bitner-Glindzicz, Black, Bradley, Brennan, Burn, Chinnery, Elliott, Flinter, Houlden, Irving, Newman, Rahman, Sayer, Taylor, Webster, Wilkie, Ouwehand, Raymond, Chisholm, Hill, Bentley, Scott, Fowler, Rendon and Caulfield2021; Stranneheim et al., Reference Stranneheim, Lagerstedt-Robinson, Magnusson, Kvarnung, Nilsson, Lesko, Engvall, Anderlid, Arnell, Johansson, Barbaro, Bjorck, Bruhn, Eisfeldt, Freyer, Grigelioniene, Gustavsson, Hammarsjo, Hellstrom-Pigg, Iwarsson, Jemt, Laaksonen, Enoksson, Malmgren, Naess, Nordenskjold, Oscarson, Pettersson, Rasi, Rosenbaum, Sahlin, Sardh, Stodberg, Tesi, Tham, Thonberg, Tohonen, von Dobeln, Vassiliou, Vonlanthen, Wikstrom, Wincent, Winqvist, Wredenberg, Ygberg, Zetterstrom, Marits, Soller, Nordgren, Wirta, Lindstrand and Wedell2021; Lindstrand et al., Reference Lindstrand, Ek, Kvarnung, Anderlid, Bjorck, Carlsten, Eisfeldt, Grigelioniene, Gustavsson, Hammarsjo, Helgadottir, Hellstrom-Pigg, Kuchinskaya, Lagerstedt-Robinson, Levin, Lieden, Lindelof, Malmgren, Nilsson, Svensson, Paucar, Sahlin, Tesi, Tham, Winberg, Winerdal, Wincent, Johansson Soller, Pettersson and Nordgren2022; van der Sanden et al., Reference van der Sanden, Schobers, Corominas Galbany, Koolen, Sinnema, van Reeuwijk, Stumpel, Kleefstra, de Vries, Ruiterkamp-Versteeg, Leijsten, Kwint, Derks, Swinkels, den Ouden, Pfundt, Rinne, de Leeuw, Stegmann, Stevens, van den Wijngaard, Brunner, Yntema, Gilissen, Nelen and Vissers2022), which is comparable or even superior to the combined yield after multiple genetic tests such as WES, chromosomal microarray and targeted gene panels (Lindstrand et al., Reference Lindstrand, Ek, Kvarnung, Anderlid, Bjorck, Carlsten, Eisfeldt, Grigelioniene, Gustavsson, Hammarsjo, Helgadottir, Hellstrom-Pigg, Kuchinskaya, Lagerstedt-Robinson, Levin, Lieden, Lindelof, Malmgren, Nilsson, Svensson, Paucar, Sahlin, Tesi, Tham, Winberg, Winerdal, Wincent, Johansson Soller, Pettersson and Nordgren2022; van der Sanden et al., Reference van der Sanden, Schobers, Corominas Galbany, Koolen, Sinnema, van Reeuwijk, Stumpel, Kleefstra, de Vries, Ruiterkamp-Versteeg, Leijsten, Kwint, Derks, Swinkels, den Ouden, Pfundt, Rinne, de Leeuw, Stegmann, Stevens, van den Wijngaard, Brunner, Yntema, Gilissen, Nelen and Vissers2022), while shortening the time to diagnosis substantially. Furthermore, WGS has the potential to improve the clinical yield even more in the future by adding analysis of novel genetic causes of disease, both coding and non-coding.

Figure 3. Number of analysed patients using WGS analysis in specific disease groups of rare disease. Positive (light colour) and negative (dark colour) genetic findings with corresponding diagnostic yield above each bar. Based on WGS analysis performed at three GMCs during 2021.

To maximise the diagnostic yield multiple variant types need to be called and interpreted clinically. As such, there is a need to optimise the analysis pipelines, databases and interpretation tools. Furthermore, the diagnosis of patients is still dependent on linking the genetic variants to clinical symptoms. This is reflected in the need for multidisciplinary collaborative environments, where laboratory specialists interact with both bioinformaticians and clinical specialists, a necessary prerequisite to implement genomics in healthcare (Investigators et al., Reference Smedley, Smith, Martin, Thomas, McDonagh, Cipriani, Ellingford, Arno, Tucci, Vandrovcova, Chan, Williams, Ratnaike, Wei, Stirrups, Ibanez, Moutsianas, Wielscher, Need, Barnes, Vestito, Buchanan, Wordsworth, Ashford, Rehmstrom, Li, Fuller, Twiss, Spasic-Boskovic, Halsall, Floto, Poole, Wagner, Mehta, Gurnell, Burrows, James, Penkett, Dewhurst, Graf, Mapeta, Kasanicki, Haworth, Savage, Babcock, Reese, Bale, Baple, Boustred, Brittain, de Burca, Bleda, Devereau, Halai, Haraldsdottir, Hyder, Kasperaviciute, Patch, Polychronopoulos, Matchan, Sultana, Ryten, Tavares, Tregidgo, Turnbull, Welland, Wood, Snow, Williams, Leigh, Foulger, Daugherty, Niblock, Leong, Wright, Davies, Crichton, Welch, Woods, Abulhoul, Aurora, Bockenhauer, Broomfield, Cleary, Lam, Dattani, Footitt, Ganesan, Grunewald, Compeyrot-Lacassagne, Muntoni, Pilkington, Quinlivan, Thapar, Wallis, Wedderburn, Worth, Bueser, Compton, Deshpande, Fassihi, Haque, Izatt, Josifova, Mohammed, Robert, Rose, Ruddy, Sarkany, Say, Shaw, Wolejko, Habib, Burns, Hunter, Grocock, Humphray, Robinson, Haendel, Simpson, Banka, Clayton-Smith, Douzgou, Hall, Thomas, O’Keefe, Michaelides, Moore, Malka, Pontikos, Browning, Straub, Gorman, Horvath, Quinton, Schaefer, Yu-Wai-Man, Turnbull, McFarland, Taylor, O’Connor, Yip, Newland, Morris, Polke, Wood, Campbell, Camps, Gibson, Koelling, Lester, Nemeth, Palles, Patel, Roy, Sen, Taylor, Cacheiro, Jacobsen, Seaby, Davison, Chitty, Douglas, Naresh, McMullan, Ellard, Temple, Mumford, Wilson, Beales, Bitner-Glindzicz, Black, Bradley, Brennan, Burn, Chinnery, Elliott, Flinter, Houlden, Irving, Newman, Rahman, Sayer, Taylor, Webster, Wilkie, Ouwehand, Raymond, Chisholm, Hill, Bentley, Scott, Fowler, Rendon and Caulfield2021; Stranneheim et al., Reference Stranneheim, Lagerstedt-Robinson, Magnusson, Kvarnung, Nilsson, Lesko, Engvall, Anderlid, Arnell, Johansson, Barbaro, Bjorck, Bruhn, Eisfeldt, Freyer, Grigelioniene, Gustavsson, Hammarsjo, Hellstrom-Pigg, Iwarsson, Jemt, Laaksonen, Enoksson, Malmgren, Naess, Nordenskjold, Oscarson, Pettersson, Rasi, Rosenbaum, Sahlin, Sardh, Stodberg, Tesi, Tham, Thonberg, Tohonen, von Dobeln, Vassiliou, Vonlanthen, Wikstrom, Wincent, Winqvist, Wredenberg, Ygberg, Zetterstrom, Marits, Soller, Nordgren, Wirta, Lindstrand and Wedell2021).

For rare disease patients, the diagnostic work is executed at the seven GMCs. As of 1 January 2022, more than 10,000 rare diseases patients have been analysed by WGS in Sweden. Of those, 22% were analysed as trios and 78% as singletons and the overall diagnostic yield was 36% and 33%, respectively. The projection is that over 5,000 additional individuals will undergo testing in 2022 resulting in a rapidly growing number of patients receiving a diagnosis.

One of the prioritised areas in the GMS rare diseases working group is to improve diagnostics by building a clinical database for genotype-phenotype matching within the NGP, as well as to establish classified variant databases and aggregated variant databases for improved variant prioritisation. This would also provide an important resource for Mendelian disease discovery as well as understanding other types of inheritance such as digenic, and mutational burden. To include as many individuals as possible we are also working on an electronic informed consent process at the national level.

The strategy for WGS-based diagnostics of rare disease patients has initially focused on well-known variant categories such as single-nucleotide variants and insertions/deletions, followed more recently by detection of structural variants, including copy-number variants (CNVs), repeat expansions and mtDNA (Dolzhenko et al., Reference Dolzhenko, Deshpande, Schlesinger, Krusche, Petrovski, Chen, Emig-Agius, Gross, Narzisi, Bowman, Scheffler, van Vugt, French, Sanchis-Juan, Ibanez, Tucci, Lajoie, Veldink, Raymond, Taft, Bentley and Eberle2019; Lindstrand et al., Reference Lindstrand, Eisfeldt, Pettersson, Carvalho, Kvarnung, Grigelioniene, Anderlid, Bjerin, Gustavsson, Hammarsjo, Georgii-Hemming, Iwarsson, Johansson-Soller, Lagerstedt-Robinson, Lieden, Magnusson, Martin, Malmgren, Nordenskjold, Norling, Sahlin, Stranneheim, Tham, Wincent, Ygberg, Wedell, Wirta, Nordgren, Lundin and Nilsson2019; Stranneheim et al., Reference Stranneheim, Lagerstedt-Robinson, Magnusson, Kvarnung, Nilsson, Lesko, Engvall, Anderlid, Arnell, Johansson, Barbaro, Bjorck, Bruhn, Eisfeldt, Freyer, Grigelioniene, Gustavsson, Hammarsjo, Hellstrom-Pigg, Iwarsson, Jemt, Laaksonen, Enoksson, Malmgren, Naess, Nordenskjold, Oscarson, Pettersson, Rasi, Rosenbaum, Sahlin, Sardh, Stodberg, Tesi, Tham, Thonberg, Tohonen, von Dobeln, Vassiliou, Vonlanthen, Wikstrom, Wincent, Winqvist, Wredenberg, Ygberg, Zetterstrom, Marits, Soller, Nordgren, Wirta, Lindstrand and Wedell2021; Ibanez et al., Reference Ibanez, Polke, Hagelstrom, Dolzhenko, Pasko, Thomas, Daugherty, Kasperaviciute, Smith, Deans, Hill, Fowler, Scott, Hardy, Chinnery, Houlden, Rendon, Caulfield, Eberle, Taft and Tucci2022). To develop the WGS-based precision diagnostics further, two national projects are conducted aiming at validating and implementing RNA-sequencing for confirmation of WGS findings and to improve understanding on regulatory, non-coding genomic variation and, secondly, long-read sequencing for improved resolution and calling of structural variants in challenging regions such as low-complexity regions.

Solid tumours

Solid tumours make up around 90% of the 62,000 cancer cases diagnosed yearly in Sweden (Socialstyrelsen, 2020). Of these, roughly 10,000 cases are currently analysed with focused gene panels, mainly for treatment-predictive purposes. Non-small cell lung cancer is the leading indication with up front reflex testing at all seven GMCs and almost 20 targeted drugs available. Patients with colorectal carcinomas and malignant melanomas are also commonly tested, along with a number of other solid tumours tested in select cases.

Given the rapidly increasing number of biomarkers and the need to assess structural variations and to generate assessments of more complex biomarkers, such as microsatellite instability (MSI), homologous recombination deficiency (HRD) and tumour mutational burden (TMB) based on mutational signatures (Malone et al., Reference Malone, Oliva, Sabatini, Stockley and Siu2020; Mosele et al., Reference Mosele, Remon, Mateo, Westphalen, Barlesi, Lolkema, Normanno, Scarpa, Robson, Meric-Bernstam, Wagle, Stenzinger, Bonastre, Bayle, Michiels, Bieche, Rouleau, Jezdic, Douillard, Reis-Filho, Dienstmann and Andre2020), a national gene panel (GMS560) for comprehensive genomic profiling of routine clinical samples was given the highest priority in the GMS solid tumour working group (Figure 4). The need was also underscored by the tumour-agnostic indications for MSI and NTRK fusions and ongoing clinical trials on additional tumour-agnostic drugs targeting mainly gene fusions, indicating an increased need for comprehensive genomic profiling (Le et al., Reference Le, Uram, Wang, Bartlett, Kemberling, Eyring, Skora, Luber, Azad, Laheru, Biedrzycki, Donehower, Zaheer, Fisher, Crocenzi, Lee, Duffy, Goldberg, de la Chapelle, Koshiji, Bhaijee, Huebner, Hruban, Wood, Cuka, Pardoll, Papadopoulos, Kinzler, Zhou, Cornish, Taube, Anders, Eshleman, Vogelstein and Diaz2015; Hong et al., Reference Hong, DuBois, Kummar, Farago, Albert, Rohrberg, van Tilburg, Nagasubramanian, Berlin, Federman, Mascarenhas, Geoerger, Dowlati, Pappo, Bielack, Doz, McDermott, Patel, Schilder, Tahara, Pfister, Witt, Ladanyi, Rudzinski, Nanda, Childs, Laetsch, Hyman and Drilon2020).

Figure 4. Genomic profiling of solid tumours illustrating first- and second-generation gene panels. CNV, copy-number variant; indels, insertions and deletions; SNVs, single nucleotide variants.

During the design phase, a structured review of current indications and biomarkers relevant for clinical studies was supplemented with input from the national clinical consensus groups for cancer treatment. In addition, variants relevant for tumour biology identified in the TCGA consortium were added for scientific purposes and for future-proofing of the design (Sanchez-Vega et al., Reference Sanchez-Vega, Mina, Armenia, Chatila, Luna, La, Dimitriadoy, Liu, Kantheti, Saghafinia, Chakravarty, Daian, Gao, Bailey, Liang, Foltz, Shmulevich, Ding, Heins, Ochoa, Gross, Gao, Zhang, Kundra, Kandoth, Bahceci, Dervishi, Dogrusoz, Zhou, Shen, Laird, Way, Greene, Liang, Xiao, Wang, Iavarone, Berger, Bivona, Lazar, Hammer, Giordano, Kwong, McArthur, Huang, Tward, Frederick, McCormick, Meyerson, Cancer Genome Atlas Research, Van Allen, Cherniack, Ciriello, Sander and Schultz2018). Finally, a number of pharmacogenomic targets were included to enable identification of genetic aberrations impacting drug susceptibility. The responsibility for the development and validation of the various modules in the panel has been divided between the seven participating sites, using the combined resources of all involved clinical laboratories, GMCs and SciLifeLab Clinical Genomics nodes. To ensure equity of care and harmonised results for complex biomarkers, a modular bioinformatics solution has been developed as part of the national effort.

The implementation of the panel has gained considerable support via pilot projects for precision medicine funded by the Swedish Ministry of Health and Social Affairs. Starting in 2021, the pilots for solid tumours included the addition of a module for HRD testing, gene panel testing for breast cancer patients with stage 4 disease, as well as a pilot for tissue and liquid biopsy testing in lung cancer as the latest addition. The pilots have served as a valuable means to involve clinicians in each field and made it possible to discuss priorities as well as important issues on how best to convey results.

Liquid biopsies, the use of blood samples to test for biomarkers normally analysed in tissue samples, have the potential to address several important clinical questions (Markou et al., Reference Markou, Tzanikou and Lianidou2022). Currently used to detect resistance mutations, monitoring of treatment response, molecular characterisation in cases with a shortage of available material and a means to visualise molecular heterogeneity, are all potential applications in the clinical setting. A future next step includes early detection of cancer with the potential to improve treatment outcomes (Crosby et al., Reference Crosby, Bhatia, Brindle, Coussens, Dive, Emberton, Esener, Fitzgerald, Gambhir, Kuhn, Rebbeck and Balasubramanian2022).

With widened indications for targeted treatments in additional malignancies and increased need to follow treatment response and resistance development, the testing can be expected to double within the coming years. Another important trend is the focus on the diagnostic importance of molecular characterisation, mirroring the increased weight given to molecular alterations in the WHO classifications of malignant disease (WHO-Classification-of-Tumours-Editorial-Board, 2021a, 2021b).

Another challenge has been to establish precision medicine trials for cancer to ensure that the data generated from the comprehensive molecular profiling can be translated into treatment and that novel treatments can be evaluated. In this work, GMS and its founding partners has joined forces with Zero Vision Cancer, MEGALiT, SciLifeLab and several relevant authorities in the field of life science in Sweden to form the “Test Bed Sweden for Clinical Trials and Implementation of Precision Health in Cancer Care.” Through this network, Nordic and European collaborations have been established and support for joint projects have been secured, allowing for work on setting up drug repurposing studies in additional European countries and refining molecular diagnostics and molecular tumour boards.

Haematological malignancies

Haematologic malignancies represent highly molecularly heterogeneous disorders with an increasing number of disease-driving alterations being described and incorporated into classification and treatment guidelines (Alaggio et al., Reference Alaggio, Amador, Anagnostopoulos, Attygalle, Araujo, Berti, Bhagat, Borges, Boyer, Calaminici, Chadburn, Chan, Cheuk, Chng, Choi, Chuang, Coupland, Czader, Dave, de Jong, Du, Elenitoba-Johnson, Ferry, Geyer, Gratzinger, Guitart, Gujral, Harris, Harrison, Hartmann, Hochhaus, Jansen, Karube, Kempf, Khoury, Kimura, Klapper, Kovach, Kumar, Lazar, Lazzi, Leoncini, Leung, Leventaki, Li, Lim, Liu, Louissaint, Marcogliese, Medeiros, Michal, Miranda, Mitteldorf, Montes-Moreno, Morice, Nardi, Naresh, Natkunam, Ng, Oschlies, Ott, Parrens, Pulitzer, Rajkumar, Rawstron, Rech, Rosenwald, Said, Sarkozy, Sayed, Saygin, Schuh, Sewell, Siebert, Sohani, Tooze, Traverse-Glehen, Vega, Vergier, Wechalekar, Wood, Xerri and Xiao2022; Arber et al., Reference Arber, Orazi, Hasserjian, Borowitz, Calvo, Kvasnicka, Wang, Bagg, Barbui, Branford, Bueso-Ramos, Cortes, Dal Cin, DiNardo, Dombret, Duncavage, Ebert, Estey, Facchetti, Foucar, Gangat, Gianelli, Godley, Gokbuget, Gotlib, Hellstrom-Lindberg, Hobbs, Hoffman, Jabbour, Kiladjian, Larson, Le Beau, Loh, Lowenberg, Macintyre, Malcovati, Mullighan, Niemeyer, Odenike, Ogawa, Orfao, Papaemmanuil, Passamonti, Porkka, Pui, Radich, Reiter, Rozman, Rudelius, Savona, Schiffer, Schmitt-Graeff, Shimamura, Sierra, Stock, Stone, Tallman, Thiele, Tien, Tzankov, Vannucchi, Vyas, Wei, Weinberg, Wierzbowska, Cazzola, Dohner and Tefferi2022; de Leval et al., Reference de Leval, Alizadeh, Bergsagel, Campo, Davies, Dogan, Fitzgibbon, Horwitz, Melnick, Morice, Morin, Nadel, Pileri, Rosenquist, Rossi, Salaverria, Steidl, Treon, Zelenetz, Advani, Allen, Ansell, Chan, Cook, Cook, d’Amore, Dirnhofer, Dreyling, Dunleavy, Feldman, Fend, Gaulard, Ghia, Gribben, Hermine, Hodson, Hsi, Inghirami, Jaffe, Karube, Kataoka, Klapper, Kim, King, Ko, LaCasce, Lenz, Martin-Subero, Piris, Pittaluga, Pasqualucci, Quintanilla-Martinez, Rodig, Rosenwald, Salles, San-Miguel, Savage, Sehn, Semenzato, Staudt, Swerdlow, Tam, Trotman, Vose, Weigert, Wilson, Winter, Wu, Zinzani, Zucca, Bagg and Scott2022; Dohner et al., Reference Dohner, Wei, Appelbaum, Craddock, DiNardo, Dombret, Ebert, Fenaux, Godley, Hasserjian, Larson, Levine, Miyazaki, Niederwieser, Ossenkoppele, Rollig, Sierra, Stein, Tallman, Tien, Wang, Wierzbowska and Lowenberg2022; Khoury et al., Reference Khoury, Solary, Abla, Akkari, Alaggio, Apperley, Bejar, Berti, Busque, Chan, Chen, Chen, Chng, Choi, Colmenero, Coupland, Cross, De Jong, Elghetany, Takahashi, Emile, Ferry, Fogelstrand, Fontenay, Germing, Gujral, Haferlach, Harrison, Hodge, Hu, Jansen, Kanagal-Shamanna, Kantarjian, Kratz, Li, Lim, Loeb, Loghavi, Marcogliese, Meshinchi, Michaels, Naresh, Natkunam, Nejati, Ott, Padron, Patel, Patkar, Picarsic, Platzbecker, Roberts, Schuh, Sewell, Siebert, Tembhare, Tyner, Verstovsek, Wang, Wood, Xiao, Yeung and Hochhaus2022). Traditionally, these disorders are divided into myeloid and lymphoid neoplasms depending on the cellular lineage affected by the disease-driving alterations.

Early on, GMS recognised that in an evolving landscape with new genomic markers being described at a rapid pace in haematological malignancies, the design of a comprehensive and highly flexible gene panel was needed. Apart from allowing the detection of a high number of single nucleotide variants (SNVs), smaller insertions/deletions and CNVs, there was a need to seamlessly incorporate new diagnostic markers in the gene panel design. GMS decided to use a targeted capture-based approach, allowing an even and deep coverage of disease variants, as well as the detection of smaller insertions/deletions (Figure 5). The first gene panel introduced in clinical diagnostics was the GMS-Myeloid Gene Panel (GMS-MGP), which allows the detection of variants in 199 genes together with a backbone of single nucleotide polymorphisms (SNPs), allowing the detection of CNVs at a 10-Mb resolution, as chromosomal imbalances form an important basis for risk-stratification and treatment prediction in haematological malignancies. The gene panel also includes genes that are associated with hereditary predisposition to haematological malignancies, as well as pharmacogenetic genes of importance for drug susceptibility. The analytical and clinical performance of the GMS-MGP was recently investigated in a national interlaboratory study, showing a high concordance of the results between the participating GMCs (Orsmark-Pietras et al., manuscript in preparation). To date, >5,000 tests have been performed in a clinical diagnostic setting using the GMS-MGP.

Figure 5. National strategy for precision diagnostics in haematological malignancies. ALL, acute lymphoblastic leukaemia; AML, acute myeloid leukaemia; CLL, chronic lymphocytic leukaemia; CML, chronic myeloid leukaemia; indels, insertions and deletions; MDS, myelodysplastic syndrome; MPN, myeloproliferative neoplasias; SNVs, single nucleotide variants.

Using a similar capture-based protocol as the GMS-MGP, a lymphoid gene panel (GMS-LGP) was designed allowing the detection of variants in 252 genes. The design of the second version is ongoing for improved detection of CNVs relevant for lymphoid malignancies, and will also include structural variation and immunoglobulin/T-cell receptor genes.

In a national study, GMS is currently also evaluating if WGS in combination with whole-transcriptome sequencing (WTS) can replace current standard of care (SoC), including multiple, often time-consuming and costly diagnostic genetic tests (Figure 5; Berglund et al., Reference Berglund, Barbany, Orsmark-Pietras, Fogelstrand, Abrahamsson, Golovleva, Hallbook, Hoglund, Lazarevic, Levin, Nordlund, Noren-Nystrom, Palle, Thangavelu, Palmqvist, Wirta, Cavelier, Fioretos and Rosenquist2022). In this study, 700 cases of adult and paediatric acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL) are subjected to WGS and WTS and the results are compared to SoC using chromosome banding, fluorescence in situ hybridization (FISH), SNP-arrays, directed molecular tests and the GMS myeloid and lymphoid gene panels. In parallel to evaluating the ability of WGS and WTS to replace SoC as a diagnostic tool, health-economy studies are performed within the study. Other upcoming studies within GMS will include the evaluation of single-cell technologies for clinical applications, a rapidly advancing field (Pfisterer et al., Reference Pfisterer, Braunig, Brattas, Heidenblad, Karlsson and Fioretos2021), as well as methods allowing ultrasensitive detection of measurable residual disease of critical importance for monitoring treatment effects and to predict disease relapse at an earlier stage (Chen et al., Reference Chen, Eriksson, Westrom, Pandzic, Lehmann, Cavelier and Landegren2022; de Leval et al., Reference de Leval, Alizadeh, Bergsagel, Campo, Davies, Dogan, Fitzgibbon, Horwitz, Melnick, Morice, Morin, Nadel, Pileri, Rosenquist, Rossi, Salaverria, Steidl, Treon, Zelenetz, Advani, Allen, Ansell, Chan, Cook, Cook, d’Amore, Dirnhofer, Dreyling, Dunleavy, Feldman, Fend, Gaulard, Ghia, Gribben, Hermine, Hodson, Hsi, Inghirami, Jaffe, Karube, Kataoka, Klapper, Kim, King, Ko, LaCasce, Lenz, Martin-Subero, Piris, Pittaluga, Pasqualucci, Quintanilla-Martinez, Rodig, Rosenwald, Salles, San-Miguel, Savage, Sehn, Semenzato, Staudt, Swerdlow, Tam, Trotman, Vose, Weigert, Wilson, Winter, Wu, Zinzani, Zucca, Bagg and Scott2022).

Childhood cancer

In children, leukaemia is the most common malignancy, while CNS tumours dominate among solid neoplasms. However, there is also a myriad of rare childhood cancers. Many of these rare entities have diagnostic markers at the genomic level, such as somatic gene fusions, point mutations or characteristic patterns of copy-number imbalances. Also, within diagnostic entities, genetic profiling is often used for prognostication and risk stratification. The diversity of diagnostic entities combined with the many types of clinically relevant genomic aberrations have made it difficult to set up comprehensive gene panel-based approaches for childhood cancer. The situation is further complicated by the constant discovery of new genomic markers that have to be included in diagnostic platforms. Taken together, this situation prompted a broad technical approach when considering a standardised clinical genomics pipeline for children with cancer.

The GMS Childhood Cancer working group resolved this issue by nationwide implementation of WGS coupled with WTS as part of the routine clinical work-up of paediatric malignancies. To achieve unbiased geographic coverage, patients from all of Sweden’s six paediatric oncology centres, of which some are very small, were included from the start of implementation in 2021. To create resilience by redundancy and to disseminate expertise though the country, WGS and WTS for childhood cancers was established at the GMCs at the large university hospitals in Stockholm, Gothenburg and Lund, as well as for leukaemia in Uppsala. The nationwide coverage and the fact that all paediatric malignancies are includible, makes our effort distinct from previous studies having focused on high-risk patients or large tertiary care centre cohorts (Wong et al., Reference Wong, Mayoh, Lau, Khuong-Quang, Pinese, Kumar, Barahona, Wilkie, Sullivan, Bowen-James, Syed, Martincorena, Abascal, Sherstyuk, Bolanos, Baber, Priestley, Dolman, Fleuren, Gauthier, Mould, Gayevskiy, Gifford, Grebert-Wade, Strong, Manouvrier, Warby, Thomas, Kirk, Tucker, O’Brien, Alvaro, McCowage, Dalla-Pozza, Gottardo, Tapp, Wood, Khaw, Hansford, Moore, Norris, Trahair, Lock, Tyrrell, Haber, Marshall, Ziegler, Ekert and Cowley2020; Newman et al., Reference Newman, Nakitandwe, Kesserwan, Azzato, Wheeler, Rusch, Shurtleff, Hedges, Hamilton, Foy, Edmonson, Thrasher, Bahrami, Orr, Klco, Gu, Harrison, Wang, Clay, Ouma, Silkov, Liu, Zhang, Liu, Brady, Zhou, Chang, Pande, Davis, Becksfort, Patel, Wilkinson, Rahbarinia, Kubal, Maciaszek, Pastor, Knight, Gout, Wang, Gu, Mullighan, McGee, Quinn, Nuccio, Mostafavi, Gerhardt, Taylor, Valdez, Hines-Dowell, Pappo, Robinson, Johnson, Pui, Ellison, Downing, Zhang and Nichols2021; Trotman et al., Reference Trotman, Armstrong, Firth, Trayers, Watkins, Allinson, Jacques, Nicholson, Burke, Genomics England Research, Behjati, Murray, Hook and Tarpey2022).

Irrespective of where the patient was diagnosed and cared for primarily, the same steps were taken from inclusion to clinical reporting (Figure 6). In a parallel project, germline variants of clinical importance are identified from normal sample WGS data (peripheral blood leukocytes for solid tumours, skin biopsies for leukaemias). Also, for CNS tumours, DNA methylation classification is performed on a regular basis. At the date of writing, around 150 children in Sweden have gone through this clinical pipeline. In the vast majority of cases, WGS and RNA sequencing provided information that corroborated, revised or refined the original diagnosis (manuscript in preparation).

Figure 6. Infographics of the GMS Childhood Cancer pipeline. The upper panel outlines the main steps for each patient’s sample and the resulting information: (1) inclusion based on informed consent and tumour cell content in biopsy >40%, (2) WGS of tumour DNA (minimum 90×), normal sample DNA (30×), and tumour RNA-sequencing, (3) filtering of tumour WGS data against normal sample WGS data to identify somatic mutations, (4) further filtering of non-synonymous coding variants against a flexible gene list of somatic mutations of clinical importance in childhood cancer, also including potential druggable targets, (5) fusion gene capture from RNA-sequencing data, (6) creation of whole genome profiles of copy numbers and allelic states, (7) discussion of findings at a molecular tumour board and (8) formulation of a written report, added as a complement to the standard pathology report. The bottom panel itemises passed milestones and future plans.

Microbiology/infectious diseases

The importance of implementing precision medicine in healthcare to prevent and treat infectious diseases is underscored by the drastic emergence of both antimicrobial resistance (AMR) and pandemics that affects and kills millions of people globally every year (Marani et al., Reference Marani, Katul, Pan and Parolari2021; Antimicrobial-Resistance-Collaborators, 2022; Fink et al., Reference Fink, Tediosi and Felder2022). A functional infrastructure for pandemic preparedness, including AMR monitoring, should encompass a broad portfolio of genetic technologies that can be employed into the healthcare system. This requires new technical solutions and IT structures for data processing. The SARS-CoV-2 pandemic has accelerated this, where GMS has been setting up a joint automated workflow for bioinformatics analysis of SARS-CoV-2 on the NGP. The platform will also be the basis for other pathogenic microorganisms in future, where a nationwide implementation of WGS enables fast and reliable identification and typing of microorganisms, that is, bacteria, viruses, parasites and fungi. Methods and results can easily be shared in real time between regions affected by outbreaks, both to map the spread of infectious diseases but also to detect emergent spread of antibiotic resistance. International comparisons can thus be facilitated, which has been shown to be of great importance during the pandemic (Pires et al., Reference Pires, Wyper, Wengler, Penalvo, Haneef, Moran, Cuschieri, Redondo, De Pauw, McDonald, Moon, Shedrawy, Pallari, Charalampous, Devleesschauwer and Von Der Lippe2022).

Putting the genetics into clinical practice to identify SNPs related to the severity of disease or antibiotic resistance will also pave the way for precision medicine in the managing and treatment of infectious diseases. The work within GMS has started with the bacterium multi-resistant methicillin Staphylococcus aureus (MRSA) where sequencing data from nine laboratories, generated with different sequencing technologies, have been compared regarding quality and different analysis pipelines to guide further harmonisation with the ultimate aim to set up one joint workflow on the NGP.

Genomic techniques such as metagenomics (sequencing of all DNA or RNA within a sample) is one way to identify and characterise a microorganism directly in a sample. The metagenomic approach offers rapid diagnostics and a possibility to detect unknown pathogens without culturing (Purushothaman et al., Reference Purushothaman, Meola and Egli2022). As part of the pandemic preparedness in Sweden, GMS has in collaboration with the SciLifeLab Pandemic Laboratory Preparedness capability, worked on establishing an assay and analysis pipeline for clinical metagenomics that is currently being established nationally and will be available on the NGP.

The same metagenomics technique is also used to characterise the human microbiome that will be of great importance to predict risk and treatment outcomes in various diseases including chronic inflammation and cancer. An integrative approach combining data sources will provide taxonomic profiles, diversity and functional annotations in such samples. The implementation of machine learning is needed to increase our capacity to classify and predict clinical outcomes and will be an important tool in precision medicine and infectious disease management (Peiffer-Smadja et al., Reference Peiffer-Smadja, Delliere, Rodriguez, Birgand, Lescure, Fourati and Ruppe2020).

Complex diseases

While progress has been made in precision medicine approaches in cancer and rare diseases, most common diseases, including allergies, asthma, cardiovascular disease, diabetes, neurologic diseases, psychiatric disorders and rheumatic diseases, pose more of a challenge. These diseases are referred to as complex because most patients do not carry established single-gene mutations with large effect sizes, and the disease risk is instead explained by a complex set of genomic variations in combination with lifestyle and environmental factors (Wheelock and Rappaport, Reference Wheelock and Rappaport2020; Franks et al., Reference Franks, Melen, Friedman, Sundstrom, Kockum, Klareskog, Almqvist, Bergen, Czene, Hagg, Hall, Johnell, Malarstig, Catrina, Hagstrom, Benson, Gustav Smith, Gomez, Orho-Melander, Jacobsson, Halfvarson, Repsilber, Oresic, Jern, Melin, Ohlsson, Fall, Ronnblom, Wadelius, Nordmark, Johansson, Rosenquist and Sullivan2021). For instance, while a strong genome-wide genetic correlation has been identified between asthma and allergic diseases (Lionel et al., Reference Lionel, Costain, Monfared, Walker, Reuter, Hosseini, Thiruvahindrapuram, Merico, Jobling, Nalpathamkalam, Pellecchia, Sung, Wang, Bikangaga, Boelman, Carter, Cordeiro, Cytrynbaum, Dell, Dhir, Dowling, Heon, Hewson, Hiraki, Inbar-Feigenberg, Klatt, Kronick, Laxer, Licht, MacDonald, Mercimek-Andrews, Mendoza-Londono, Piscione, Schneider, Schulze, Silverman, Siriwardena, Snead, Sondheimer, Sutherland, Vincent, Wasserman, Weksberg, Shuman, Carew, Szego, Hayeems, Basran, Stavropoulos, Ray, Bowdin, Meyn, Cohn, Scherer and Marshall2018), attempts to explain the incidence of obstructive lung disease or allergy solely by genetics have not been successful. Collectively, complex diseases account for ~70% of global deaths and the majority of healthcare costs (GBD-Disease-Injury-Incidence-Prevalence-Collaborators, 2017). To address this challenge, GMS has formed a working group to identify common complex diseases where a genetic link has been identified, but where this information is not yet used optimally in clinical practice.

During the last few years, the possibility of combining genetic associations across the genome into a polygenic risk score (PRS) has been investigated for many complex diseases (O’Sullivan et al., Reference O’Sullivan, Raghavan, Marquez-Luna, Luzum, Damrauer, Ashley, O’Donnell, Willer and Natarajan2022). For example, in coronary artery disease, it has been shown that a high PRS confers an equal risk as monogenic effects (Khera et al., Reference Khera, Chaffin, Aragam, Haas, Roselli, Choi, Natarajan, Lander, Lubitz, Ellinor and Kathiresan2018), and a higher risk than conventional risk factors. For preventive purposes, many international organisations already recommend to estimate the 10-year cardiovascular risk for adult patients 40 to 75, and including the PRS will contribute to increased accuracy of the current risk prediction tools (O’Sullivan et al., Reference O’Sullivan, Raghavan, Marquez-Luna, Luzum, Damrauer, Ashley, O’Donnell, Willer and Natarajan2022). PRS has also been used to further refine risk assessment among women with suspected predisposition to breast cancer (Mavaddat et al., Reference Mavaddat, Michailidou, Dennis, Lush, Fachal, Lee, Tyrer, Chen, Wang, Bolla, Yang, Adank, Ahearn, Aittomaki, Allen, Andrulis, Anton-Culver, Antonenkova, Arndt, Aronson, Auer, Auvinen, Barrdahl, Beane Freeman, Beckmann, Behrens, Benitez, Bermisheva, Bernstein, Blomqvist, Bogdanova, Bojesen, Bonanni, Borresen-Dale, Brauch, Bremer, Brenner, Brentnall, Brock, Brooks-Wilson, Brucker, Bruning, Burwinkel, Campa, Carter, Castelao, Chanock, Chlebowski, Christiansen, Clarke, Collee, Cordina-Duverger, Cornelissen, Couch, Cox, Cross, Czene, Daly, Devilee, Dork, Dos-Santos-Silva, Dumont, Durcan, Dwek, Eccles, Ekici, Eliassen, Ellberg, Engel, Eriksson, Evans, Fasching, Figueroa, Fletcher, Flyger, Forsti, Fritschi, Gabrielson, Gago-Dominguez, Gapstur, Garcia-Saenz, Gaudet, Georgoulias, Giles, Gilyazova, Glendon, Goldberg, Goldgar, Gonzalez-Neira, Grenaker Alnaes, Grip, Gronwald, Grundy, Guenel, Haeberle, Hahnen, Haiman, Hakansson, Hamann, Hankinson, Harkness, Hart, He, Hein, Heyworth, Hillemanns, Hollestelle, Hooning, Hoover, Hopper, Howell, Huang, Humphreys, Hunter, Jakimovska, Jakubowska, Janni, John, Johnson, Jones, Jukkola-Vuorinen, Jung, Kaaks, Kaczmarek, Kataja, Keeman, Kerin, Khusnutdinova, Kiiski, Knight, Ko, Kosma, Koutros, Kristensen, Kruger, Kuhl, Lambrechts, Le Marchand, Lee, Lejbkowicz, Lilyquist, Lindblom, Lindstrom, Lissowska, Lo, Loibl, Long, Lubinski, Lux, MacInnis, Maishman, Makalic, Maleva Kostovska, Mannermaa, Manoukian, Margolin, Martens, Martinez, Mavroudis, McLean, Meindl, Menon, Middha, Miller, Moreno, Mulligan, Mulot, Munoz-Garzon, Neuhausen, Nevanlinna, Neven, Newman, Nielsen, Nordestgaard, Norman, Offit, Olson, Olsson, Orr, Pankratz, Park-Simon, Perez, Perez-Barrios, Peterlongo, Peto, Pinchev, Plaseska-Karanfilska, Polley, Prentice, Presneau, Prokofyeva, Purrington, Pylkas, Rack, Radice, Rau-Murthy, Rennert, Rennert, Rhenius, Robson, Romero, Ruddy, Ruebner, Saloustros, Sandler, Sawyer, Schmidt, Schmutzler, Schneeweiss, Schoemaker, Schumacher, Schurmann, Schwentner, Scott, Scott, Seynaeve, Shah, Sherman, Shrubsole, Shu, Slager, Smeets, Sohn, Soucy, Southey, Spinelli, Stegmaier, Stone, Swerdlow, Tamimi, Tapper, Taylor, Terry, Thone, Tollenaar, Tomlinson, Truong, Tzardi, Ulmer, Untch, Vachon, van Veen, Vijai, Weinberg, Wendt, Whittemore, Wildiers, Willett, Winqvist, Wolk, Yang, Yannoukakos, Zhang, Zheng, Ziogas, Dunning, Thompson, Chenevix-Trench, Chang-Claude, Schmidt, Hall, Milne, Pharoah, Antoniou, Chatterjee, Kraft, Garcia-Closas, Simard and Easton2019; Lakeman et al., Reference Lakeman, Rodriguez-Girondo, Lee, Ruiter, Stricker, Wijnant, Kavousi, Antoniou, Schmidt, Uitterlinden, van Rooij and Devilee2020), and a web-based risk assessment tool, CanRisk Web Tool, based on PRS, family history and rare pathogenic variants in cancer susceptibility genes has been developed (Carver et al., Reference Carver, Hartley, Lee, Cunningham, Archer, Babb de Villiers, Roberts, Ruston, Walter, Tischkowitz, Easton and Antoniou2021).

However, for most PRS the precision is too low to be clinically relevant for individual patients and there are no clear guidelines available on how to translate PRS to the benefit of individual patients. For example, the variance in asthma liability explained by 22 distinct genome-wide-significant variants is estimated to be as low as 3.5% (Demenais et al., Reference Demenais, Margaritte-Jeannin, Barnes, Cookson, Altmuller, Ang, Barr, Beaty, Becker, Beilby, Bisgaard, Bjornsdottir, Bleecker, Bonnelykke, Boomsma, Bouzigon, Brightling, Brossard, Brusselle, Burchard, Burkart, Bush, Chan-Yeung, Chung, Couto Alves, Curtin, Custovic, Daley, de Jongste, Del-Rio-Navarro, Donohue, Duijts, Eng, Eriksson, Farrall, Fedorova, Feenstra, Ferreira, Freidin, Gajdos, Gauderman, Gehring, Geller, Genuneit, Gharib, Gilliland, Granell, Graves, Gudbjartsson, Haahtela, Heckbert, Heederik, Heinrich, Heliovaara, Henderson, Himes, Hirose, Hirschhorn, Hofman, Holt, Hottenga, Hudson, Hui, Imboden, Ivanov, Jaddoe, James, Janson, Jarvelin, Jarvis, Jones, Jonsdottir, Jousilahti, Kabesch, Kahonen, Kantor, Karunas, Khusnutdinova, Koppelman, Kozyrskyj, Kreiner, Kubo, Kumar, Kumar, Kuokkanen, Lahousse, Laitinen, Laprise, Lathrop, Lau, Lee, Lehtimaki, Letort, Levin, Li, Liang, Loehr, London, Loth, Manichaikul, Marenholz, Martinez, Matheson, Mathias, Matsumoto, Mbarek, McArdle, Melbye, Melen, Meyers, Michel, Mohamdi, Musk, Myers, Nieuwenhuis, Noguchi, O’Connor, Ogorodova, Palmer, Palotie, Park, Pennell, Pershagen, Polonikov, Postma, Probst-Hensch, Puzyrev, Raby, Raitakari, Ramasamy, Rich, Robertson, Romieu, Salam, Salomaa, Schlunssen, Scott, Selivanova, Sigsgaard, Simpson, Siroux, Smith, Solodilova, Standl, Stefansson, Strachan, Stricker, Takahashi, Thompson, Thorleifsson, Thorsteinsdottir, Tiesler, Torgerson, Tsunoda, Uitterlinden, van der Valk, Vaysse, Vedantam, von Berg, von Mutius, Vonk, Waage, Wareham, Weiss, White, Wickman, Widen, Willemsen, Williams, Wouters, Yang, Zhao, Moffatt, Ober and Nicolae2018), and 12 common genetic variants associated with airflow obstruction and risk of chronic obstructive pulmonary disease only explain about 1.5% of the phenotypic variance (Hobbs et al., Reference Hobbs, de Jong, Lamontagne, Bosse, Shrine, Artigas, Wain, Hall, Jackson, Wyss, London, North, Franceschini, Strachan, Beaty, Hokanson, Crapo, Castaldi, Chase, Bartz, Heckbert, Psaty, Gharib, Zanen, Lammers, Oudkerk, Groen, Locantore, Tal-Singer, Rennard, Vestbo, Timens, Pare, Latourelle, Dupuis, O’Connor, Wilk, Kim, Lee, Oh, Vonk, de Koning, Leng, Belinsky, Tesfaigzi, Manichaikul, Wang, Rich, Barr, Sparrow, Litonjua, Bakke, Gulsvik, Lahousse, Brusselle, Stricker, Uitterlinden, Ampleford, Bleecker, Woodruff, Meyers, Qiao, Lomas, Yim, Kim, Hawrylkiewicz, Sliwinski, Hardin, Fingerlin, Schwartz, Postma, MacNee, Tobin, Silverman, Boezen and Cho2017). For many complex traits (Yengo et al., Reference Yengo, Vedantam, Marouli, Sidorenko, Bartell, Sakaue, Graff, Eliasen, Jiang, Raghavan, Miao, Arias, Mukamel, Spracklen, Yin, Chen, Ferreira, Yingjie, Karedera, Lüll, Lin, Malden, Medina-Gomez, Machado, Moore, Rüeger, Ahluwalia, Akiyama, Allison, Alvarez, Andersen, Ani, Appadurai, Arbeeva, Bhaskar, Bielak, Bollepalli, Bonnycastle, Bork-Jensen, Bradfield, Bradford, Braund, Brody, Burgdorf, Cade, Cai, Cai, Campbell, Cañadas-Garre, Catamo, Chai, Chai, Chang, Chang, Chen, Chesi, Choi, Chung, Cocca, Concas, Couture, Cuellar-Partida, Danning, Daw, Degenhard, Delgado, Delitala, Demirkan, Deng, Devineni, Dietl, Dimitriou, Dimitrov, Dorajoo, Ekici, Engmann, Fairhurst-Hunter, Farmaki, Faul, Fernandez-Lopez, Forer, Francescatto, Freitag-Wolf, Fuchsberger, Galesloot, Gao, Gao, Geller, Giannakopoulou, Giulianini, Gjesing, Goel, Gordon, Gorski, Graham, Grove, Guo, Gustafsson, Haessler, Hansen, Havulinna, Haworth, He, Heard-Costa, Hebbar, Hindy, Ho, Hofer, Holliday, Horn, Hornsby, Hottenga, Huang, Huang, Huerta-Chagoya, Huffman, Hung, Huo, Hwang, Iha, Ikeda, Isono, Jackson, Jäger, Jansen, Johansson, Jonas, Jonsson, Jørgensen, Kalafati, Kanai, Kanoni, Kårhus, Kasturiratne, Katsuya, Kawaguchi, Kember, Kentistou, Kim, Kim, Kleber, Knol, Kurbasic, Lauzon, Le, Lea, Lee, Leonard, Li, Li, Li, Liang, Lin, Lin, Liu, Liu, Lo, Long, Lores-Motta, Luan, Lyssenko, Lyytikäinen, Mahajan, Mamakou, Mangino, Manichaikul, Marten, Mattheisen, Mavarani, McDaid, Meidtner, Melendez, Mercader, Milaneschi, Miller, Millwood, Mishra, Mitchell, Møllehave, Morgan, Mucha, Munz, Nakatochi, Nelson, Nethander, Nho, Nielsen, Nolte, Nongmaithem, Noordam, Ntalla, Nutile, Pandit, Christofidou, Pärna, Pauper, Petersen, Petersen, Pitkänen, Polašek, Poveda, Preuss, Pyarajan, Raffield, Rakugi, Ramirez, Rasheed, Raven, Rayner, Riveros, Rohde, Ruggiero, Ruotsalainen, Ryan, Sabater-Lleal, Saxena, Scholz, Sendamarai, Shen, Shi, Shin, Sidore, Sim, Sitlani, Slieker, Smit, Smith, Smith, Smyth, Southam, Steinthorsdottir, Sun, Takeuchi, Priyanka Tallapragada, Taylor, Tayo, Tcheandjieu, Terzikhan, Tesolin, Teumer, Theusch, Thompson, Thorleifsson, Timmers, Trompet, Turman, Vaccargiu, van der Laan, van der Most, van Klinken, van Setten, Verma, Verweij, Veturi, Wang, Wang, Wang, Wang, Warren, Wei, Wickremasinghe, Wielscher, Wiggins, Winsvold, Wong, Wu, Wuttke, Xia, Xie, Yamamoto, Yang, Yao, Young, Yousri, Yu, Zeng, Zhang, Zhang, Zhao, Zhao, Zhou, Zimmermann, Zoledziewska, Adair, Adams, Aguilar-Salinas, Al-Mulla, Arnett, Asselbergs, Åsvold, Attia, Banas, Bandinelli, Bennett, Bergler, Bharadwaj, Biino, Bisgaard, Boerwinkle, Böger, Bønnelykke, Boomsma, Børglum, Borja, Bouchard, Bowden, Brandslund, Brumpton, Buring, Caulfield, Chambers, Chandak, Chanock, Chaturvedi, Chen, Chen, Cheng, Christophersen, Ciullo, Cole, Collins, Cooper, Cruz, Cucca, Cupples, Cutler, Damrauer, Dantoft, Borst, de Groot, De Jager, de Kleijn, de Silva, Dedoussis, den Hollander, Du, Easton, Elders, Eliassen, Ellinor, Elmståhl, Erdmann, Evans, Fatkin, Feenstra, Feitosa, Ferrucci, Ford, Fornage, Franke, Franks, Freedman, Gasparini, Gieger, Girotto, Goddard, Golightly, Gonzalez-Villalpando, Gordon-Larsen, Grallert, Grant, Grarup, Griffiths, Groop, Gudnason, Haiman, Hakonarson, Hansen, Hartman, Hattersley, Hayward, Heckbert, Heng, Hengstenberg, Hewitt, Hishigaki, Hoyng, Huang, Huang, Hunt, Hveem, Hyppönen, Iacono, Ichihara, Ikram, Isasi, Jackson, Jarvelin, Jin, Jöckel, Joshi, Jousilahti, Jukema, Kähönen, Kamatani, Kang, Kaprio, Kardia, Karpe, Kato, Kee, Kessler, Khera, Khor, Kiemeney, Kim, Kim, Kim, Kirchhof, Kivimaki, Koh, Koistinen, Kolovou, Kooner, Kooperberg, Köttgen, Kovacs, Kraaijeveld, Kraft, Krauss, Kumari, Kutalik, Laakso, Lange, Langenberg, Launer, Marchand, Lee, Lee, Lehtimäki, Li, Li, Lieb, Lin, Lind, Linneberg, Liu, Liu, Loeffler, London, Lubitz, Lye, Mackey, Mägi, Magnusson, Marcus, Vidal, Martin, März, Matsuda, McGarrah, McGue, McKnight, Medland, Mellström, Metspalu, Mitchell, Mitchell, Mook-Kanamori, Morris, Mucci, Munroe, Nalls, Nazarian, Nelson, Neville, Newton-Cheh, Nielsen, Nöthen, Ohlsson, Oldehinkel, Orozco, Pahkala, Pajukanta, Palmer, Parra, Pattaro, Pedersen, Pennell, Penninx, Perusse, Peters, Peyser, Porteous, Posthuma, Power, Pramstaller, Province, Qi, Qu, Rader, Raitakari, Ralhan, Rallidis, Rao, Redline, Reilly, Reiner, Rhee, Ridker, Rienstra, Ripatti, Ritchie, Roden, Rosendaal, Rotter, Rudan, Rutters, Sabanayagam, Saleheen, Salomaa, Samani, Sanghera, Sattar, Schmidt, Schmidt, Schmidt, Schulze, Schunkert, Scott, Scott, Sever, Shiroma, Shoemaker, Shu, Simonsick, Sims, Singh, Singleton, Sinner, Smith, Snieder, Spector, Stampfer, Stark, Strachan, t Hart, Tabara, Tang, Tardif, Thanaraj, Timpson, Tönjes, Tremblay, Tuomi, Tuomilehto, Tusié-Luna, Uitterlinden, van Dam, van der Harst, Van der Velde, van Duijn, van Schoor, Vitart, Völker, Vollenweider, Völzke, Vrieze, Wacher-Rodarte, Walker, Wang, Wareham, Watanabe, Watkins, Weir, Werge, Widen, Wilkens, Willemsen, Willett, Wilson, Wong, Woo, Wright, Wu, Xu, Yajnik, Yokota, Yuan, Zeggini, Zemel, Zheng, Zhu, Zmuda, Zonderman, Zwart, Chasman, Cho, Heid, McCarthy, Ng, O’Donnell, Rivadeneira, Thorsteinsdottir, Sun, Tai, Boehnke, Deloukas, Justice, Lindgren, Loos, Mohlke, North, Stefansson, Walters, Winkler, Young, Loh, Yang, Esko, Assimes, Auton, Abecasis, Willer, Locke, Berndt, Lettre, Frayling, Okada, Wood, Visscher and Hirschhorn2022), a dramatically larger sample size is therefore needed in the GWAS studies before a PRS will be of high enough precision to identify high- or low-risk individuals of complex diseases. However, it is worth highlighting that environmental risk factors, and environmental exposure, remain the predominant drivers for development of several complex diseases, including, for example, obstructive lung disease.

It is clear that substantially more research is required to be able to take genomic precision medicine into the clinic and many such initiatives are underway. There is a need to include other omics technologies, such as proteomics and metabolomics (Beger et al., Reference Beger, Dunn, Schmidt, Gross, Kirwan, Cascante, Brennan, Wishart, Oresic, Hankemeier, Broadhurst, Lane, Suhre, Kastenmuller, Sumner, Thiele, Fiehn and Kaddurah-Daouk2016; Duarte and Spencer, Reference Duarte and Spencer2016), which offers the advantage of being closest to the current phenotype of the patient, into precision medicine efforts to predict, diagnose and ultimately treat and prevent complex diseases. Following this approach, there have been recent efforts to investigate the utility of biomarker-based approaches to stratify asthmatics for treatment with biologics and patients with atrial fibrillation to receive oral anticoagulation (Benz et al., Reference Benz, Hijazi, Lindback, Connolly, Eikelboom, Oldgren, Siegbahn and Wallentin2021; Israel et al., Reference Israel, Denlinger, Bacharier, LaVange, Moore, Peters, Georas, Wright, Mauger, Noel, Akuthota, Bach, Bleecker, Cardet, Carr, Castro, Cinelli, Comhair, Covar, Alexander, DiMango, Erzurum, Fahy, Fajt, Gaston, Hoffman, Holguin, Jackson, Jain, Jarjour, Ji, Kenyon, Kosorok, Kraft, Krishnan, Kumar, Liu, Liu, Ly, Marquis, Martinez, Moy, O’Neal, Ortega, Peden, Phipatanakul, Ross, Smith, Szefler, Teague, Tulchinsky, Vijayanand, Wechsler, Wenzel, White, Zeki and Ivanova2021).

In summary, while the healthcare organisation of Sweden is conducive to expanding precision medicine treatment approaches into the area of complex diseases, there remains a general lack of knowledge about how molecular information can be used to benefit the diagnosis, prevention and treatment of patients with complex diseases. It is most probable that eventual precision treatment efforts for complex diseases will involve a combination of genetic and other molecular information to create biomarker panels for optimising patient treatment strategies.