Recenzia Of Genetic Testing in Obličky Choroba Pacienti: Diagnostický Výťažok Of Single Nukleotid Varianty And Copy Number Variations Evaluationd Across A Within Obličky Fenotyp Skupiny Ⅱ

4 | DISKUSIA 

Určenie pravda diagnostika výťažok z the papiere študované je ťažké pretože z variabilita v pacient, kohorta, a test charakteristiky. The diagnostický výnos pre odlišný fenotyp skupiny mal by preto be myšlienka z v termínoch rozsahoch, a odporúčaniach by mal nie byť založený na jediný štúdie. Given the enormous variation in key parameters, we wanted to avoid overinterpretation and protect did performage štatistický analýzy. Avšak, the komplexný prehľad prezentovaný vôľa pomoc zvážiť možné relevantné faktory s potenciálne užitočné informácie pre klinické prax. Toto prehľad umožňuje klinickým posúdiť ktoré štúdie sú najviac relevantné pre ich špecifické pacientov/patient skupiny a odhad an a priori pravdepodobnosť nález a genetika príčina pre ich pacientov.

OBRÁZOK 3 Bodové grafy a rámček grafy zobrazenie the vzťah medzi diagnostika výnos a kohorta charakteristiky. (a) Rozptylové grafy zobraziť the vzťah medzi diagnostický výnos a počet of prípady sekvenčný v a špecifický štúdii. Legenda popisuje a počet of štúdie pre ktoré údaje na toto kohorta charakteristika was dostupné. Farby indikovať the choroba skupina z ktoré štúdie boli odvodené. Každý bodka predstavuje pre jeden štúdia čo počet z sekvenované prípady bol v to kohorta a čo diagnostika výnos bol získaný od to to rovnaké štúdiu. b) Boxploty predstavujúci the diagnostický výnos v klinických kohortách verzus výskum kohorty. The dolný polovica predstavenia klinický verzus výskum kohorty priečny the rôzne choroby skupiny. legenda popisuje a počet z štúdie pre ktoré údaje na toto kohorta charakteristika bol dostupné.

Not surprisingly, we found that diagnostic yield was higher based on expected patient characteristics (e.g., family history, consanguinity, extrarenal features, and young age of onset) within studies. When we assessed these same characteristics plotted against the diagnostic yield between the different studies (instead of within one study), this pattern was not seen for consanguinity and young age of onset. This might be explained by a combination of other characteristics having a larger impact on the diagnostic yield. Also, within the adult-onset group, there is a large variation in disease severity (e.g., ESKD at age 20 has a higher diagnostic yield than ESKD at age 70) and the likelihood of finding a monogenic cause (i.e., in a typical ADPKD cohort a high diagnostic yield is expected (Figure 2e)). We did find an indication that young age of onset is related to a higher diagnostic yield based only on CNVs (Figure 2f). This is likely explained by genome-wide CNV analysis being performed more often in this group (Supplementary Table 3).

KLIKNITE TU DO GET TO KNOW A HERBAL FORMULÁCIA OF CISTANCHE FOR OBLIČKY A SEXUÁLNE SEXUÁLNE FUNKCIE 

This review includes an overview of CNV yield per phenotype. CNVs that are not picked up by regular sequencing can be assessed using a separate test (e.g., SNP-array) or by a CNV calling tool based on sequencing data (Knoers et al., 2022). The different types of tests that were used in the included studies varied greatly, as did the number of genes that were analyzed (i.e., covering one gene, the requested multigene panel or exome/genome-wide and the impact this has on the diagnostic yield [Supplementary Table 3, Supplementary Figure 3]). We found the highest contribution of CNVs to diagnostic yield in CAKUT, ciliopathies, and tubulopathies. However, across all phenotype groups where CNV testing was performed, CNVs did contribute to the diagnostic yield and CNV analysis should be considered when genetic testing is performed. The high contribution of CNVs to diagnostic yield in CAKUT patients confirms previous reports (Knoers et al., 2022). CNVs were extensively investigated in CAKUT patients as SNV yield is relatively low in this group. It is yet to be established for some other phenotypes whether the diagnostic yield based on CNVs is underestimated hitherto as we found that in many phenotype groups CNVs were not investigated (Figure 1c).

Surprisingly, a higher number of genes tested did not always correlate with a higher yield. In theory, this would always be the case in comparable cohorts. However, the cohorts we studied vary distinctly. On the one hand, we describe cohorts with a highly likely monogenic cause (such as ADPKD-suspected patients) requiring only a small number of tested genes to result in a high diagnostic yield. On the other hand, we find a lower diagnostic yield and an increase in the number of tested genes in less highly suspected cohorts. Cohort size did not appear to correlate with a number of tested genes (data not shown). Another explanation for this finding is an increase in the number of tested genes in patients where testing of common known disease genes did not result in a genetic diagnosis. Since it is possible that a proportion of these unsolved cases have either a genetic diagnosis in a not yet discovered gene, or a non-monogenic cause explaining their disease, a lower yield in this group can be hypothesized. Also, in some cohorts, patients with known mutations were excluded, but the total number of these patients with a mutation was not reported (Bekheirnia et al., 2017; Braun et al., 2016; Faure et al., 2016; Heidet et al., 2017; Kohl et al., 2014; Schueler et al., 2016; Vivante et al., 2017; Ziyadov et al., 2021). Finally, in some studies, genetic testing in specific known genes was not performed (e.g., known CAKUT genes (Caruana et al., 2015; Sanna-Cherchi et al., 2012)). For translation of the reported diagnostic yield to clinical practice, it is important to know these details. It was beyond the scope of this review to analyze whether for all cohorts the panel composition included all known causal genes, including appropriate phenocopy genes, for each phenotype at the time that that specific study was performed.

OBRÁZOK 4 Rozptýlené grafy a rámček grafy zobrazenie súvislosť medzi diagnostika výnos a test charakteristiky. (a) Rozptylové grafy zobrazenie vzťah medzi diagnostický výnos a počet z génov sekvenovaný v a špecifický štúdii. legenda popisuje a počet z štúdie pre ktoré údaje na toto špecifický test charakteristika bol dostupný. Nie všetky štúdie boli zahrnuté od v niektoré štúdie počet z gény sekvenované rôzne v štúdii v štúdii. Farby indikovať the choroba skupina z ktorá štúdia bola odvodená. Každý bodka predstavuje za jeden štúdia čo počet z sekvencia prípady bol v tamto kohorta a čo diagnostika výťažok bol získaný od to to rovnaká štúdia. (b) Koláč grafy vizualizácia a počet z štúdie to vykonané buď jednoraz nukleotid variant (SNV) alebo kopírovanie číslo variácia (CNV) testovanie alebo oboje. (c) Boxploty predstavujúci the diagnostický výnos v vzťah k s typ z variantov že boli testované. V the right panel, this is split na the different disease groups.

My sme našli to to diagnostik výnos znížený v väčších kohortách. Toto mohol by by vysvetliť podľa menších kohortách bytie viac jasne definované a preto mať a vyššie podozrenie z a monogénne príčiny. In pridanie na výber zaujatosť v a štúdii, publikácia zaujatosť mohol tiež vysvetliť toto zistenie. The larger kohorty pravdepodobne dať a viac spoľahlivý odhad z z the true diagnostický výnos v a relatívne nevybratý Obličky ochorenie populácia. My chceme a a bod von to a publikované kohorta s a vysoká diagnostika výnos je nie nevyhnutne the "better" kohorta na použitie pre klinické rozhodovanie. kedy podávanie prísne obmedzené kritériá genetické diagnózy sú pravdepodobné zmeškané. kedy tlmočenie publikované kohorty pre klinické prax, one should also take into account that there are many local differences in in clinical phenotyping and diagnosing, especially when seeen od an medzinárodná perspektíva. Pre príklad, a pacient s neznámym CKD v jednom strede mohol odlišný zreteľne od a pacient s neznámym CKD v ďalším centrom.

obličky choroba pacienti v a klinické nastavenie by odpoveď toto otázka

OBRÁZOK 5 Top 50% of diagnostický výnos vysvetlený by a obmedzený počet of gény and/or copy počet variácie (CNV). (a) Koláč grafy vizualizácia a číslo of gény zodpovedný za the top 50% of yield. The legend on the left popisuje pre každý kategória in the pie chart, the počet of štúdie identifikované s toto počet of gény. In medzi zátvorkami na the right are the number of studies that reported on the top 50% cauzal genes and separated by "j" the number of genes zodpovedný za the top 50% for that specific phenotype. (b) The genes that made up this  50% in eachkoľvek štúdia per phenotype group are displayed here uni genes were responsible for only one positive case and/or multiple genes made up for the final percentá. * Varianty v the large variable number tandem repeats region of MUC1 are zvyčajne missed by masívne paralelne sekvenovanie

To odvodiť a minimum diagnostika výťažok pre a špecifický diagnostický nastavenie, extrapolovaný the diagnostický výnos na väčšia kohorta z ktorá the testovaná (klinická) kohorta bola odvodená. An zaujímavý príklad z toto je prezentovaný podľa štúdií of Snoek et al. and Schrezenmeier et al., who oboje report a diagnostický výnos of 21% in a oblička transplantácia (waitlisted) cohort (Schrezenmeier et al., 2021; Snoek et al., 2022).

5|DOPAD OF GENETICKÝ DIAGNOSTIKA 

A genetický diagnóza môžem mať a diagnostikovať, prognostic, a liečba vplyv. The štúdie my vybrané pre preskúmanie zvýraznenie toto (tabuľka 3). Viaceré štúdie hlásené na the molekulárne genetické diagnostika výsledok v korekcia z klinická diagnóza. zatiaľ čo percentá rôzne štúdie, všetko zvýraznenie potenciál dôležitosť zriaďovanie the correct diagnostika cez genetické testovanie. The therapeutic impact varies from referral and evaluation for pre-pre-nerozpoznané extrarenálne funkcie na zmena liečba plány. A clear príklad z the the therapeutic dôsledky z genetické testovanie is SRNS; most genetické formy of SRNS do not rerespond na imunosupresívne lieky a môže preto be ušetrené the potenciál toxicita z tieto neúčinné lieky. A clear príklad of prognostic impact is an extrémne low disease recidíva in many genetic kidney diseases follow oblička transplantáciaObličky ochorenia s a negenetický príčina.

Dôležité, identifikácia a genetický príčina môžem byť rozhodujúcim pre pacienta a%2pre rodičov of a pacienta s ohľadu na genetiku poradenstvo; it informs recidívu riziká a môžem podporiť pacientov' a rodičov' rozhodovanie ohľadne reprodukcie možnosti suché as prenatálne a preimplantáciu genetické diagnostiku. In Add, family members can be counseled about disease risk, presymptomatické testovanie, and screening options for secondary signs in first-grade family members whilst not performing genetic testing. A genetic diagnostika can also be of important for living for living obličky darcovstvo podľa rodiny členov. Čo dopad a genetika diagnostika má, vrátane terapeutický dopad, bude závisí na the fenotype, ale tiež na jednotlivca okolností z a pacienta a rodiny, a miestnych/regionálnych dostupnosť of liečba a rodina plánovanie možnosti.

OBRÁZOK 6 Zhrnutie of kľúč zistenia z literatúra prehľad (n=115 články). The ľavá strana z the obrázok sumarizuje charakteristika ovplyvňovanie diagnostika výnos a vplyv of a genetika diagnostika. The pravá strana z toto obrázok sumarizuje ďalší kľúč vziať domov správy. *Odhadnuté podľa autorov z toto recenzia to to je splatné do do tesne obmedzené fenotyp kritériá.

6|CONSIDERATIONS FOR GENETIC TESTING

It is important to note that the type of test that is chosen for genetic testing in a patient can have a big impact on the chance of finding a genetic cause. The technological advancements in genetic testing approaches have made (MPS-based) CNV testing and exome-based sequencing possible and the advantages are being recognized over the years (Supplementary Figure 2). Gene panel composition, number of tested genes, and CNV analysis can influence the likelihood of finding a genetic cause. In addition, in ADTKD-suspected cases, it is important to consider MUC1 testing or additional PKD1 testing in ADPKD suspected cases after MPS-based multigene panel or exome testing. In only 6 studies (including 3/4 ADTKD cohorts) additional testing was performed to detect a cytosine insertion in the variable number tandem repeats region of MUC1 that is usually missed by MPS (Supplementary Table 3) (Kirby et al., 2013). Nineteen studies reported additional tests to reach sufficient coverage of all PKD1 exons, which is challenging because of the existence of six pseudogenes (PKD1P1-6) with 97.7% sequence identity. Fifteen of these studies focused on ciliopathy phenotypes and four included mixed kidney disease phenotypes. One study reported the exclusion of ADPKD patients because PKD1 is not well-captured by WES (Lata et al., 2018). Clinicians that request genetic testing need to be aware of what disease-causing variants in what genes can be detected by what genetic test and when additional genetic tests should be requested (Knoers et al., 2022; Köttgen et al., 2022).

Niekedy klinickí lekári kto do nie zatiaľ mať odbornosť v (nefrón) genetika zdanliť na predpokladať že "vykonávajúci WES" vôľa pokrytie akékoľvek chorobu-spôsobujúce variant. Jeden aspekt z toto nepravda predpoklad is CNV detekcia. Zatiaľ čo WES-založený CNV analýza je nadchádzajúce, toto je nie zatiaľ vždy zahrnuté. It je preto dobré zvážiť či, pre špecifické pacienta, dodatočné CNV testovanie je potrebné. In the studies included in this review, many different tools were used, with differences in yield per tool being reported (Moreno-Cabrera et al., 2020; Yao et al., 2017). Some studies covered CNVs in all genes, while others focused only on genes within a gene panel (Supplementary Figure 3). Also, separate tests (napr., MLPA covering one gene or genome-wide SNP array) were performed for CNV detection. Iné aspekty z toto nepravda predpoklad are variácie v géne pokrytie, ťažko sekvenovateľné regióny (napr., opakovanie regióny, pseudogény), a nekódovanie varianty.

In the past, gene panels were always enrichment-based, meaning that only the set of genes that were selected prior to sequencing would be sequenced and analyzed. Today, diagnostic labs are often using exome-based gene panels (Supplementary Figure 2b). With this approach, the complete exome is sequenced, but only the genes of interest from a specific gene panel are analyzed. An advantage of WES and the usage of exome-based gene panels is the efficient method in which data is derived including the possibility to analyze additional genes without having to resequence the patient's DNA (Knoers et al., 2022). WES also makes it possible to reanalyze or identify phenocopies. The studies included in this review highlight this. Warejko et al. identified phenocopies in 4% of patients in an SRNS cohort (Warejko et al., 2018). Also in the NS cohort of Landini et al., reverse phenotyping of patients let to the diagnosis of phenocopies in 28% of cases (Landini et al., 2020). Szab
o et al. found phenocopies in 22% of patients with ARPKD (Szab
o et al., 2018). In a cohort with various phenotypes, Riedhammer et al. discovered that 19% of diagnosed cases were a phenocopy (Riedhammer et al., 2020). Phenocopies and local differences in clinical phenotyping are arguments for a broader gene panel composition. Broad genetic testing also has significant challenges, including a higher chance of incidental findings, and the difficulties in interpretation of variants of unknown significance (VUS) (Bertier, Hétu, & Joly, 2016). This should be taken into account when choosing an initial smaller gene panel or a broad multigene panel or an exome-wide analysis. Also, counselors should be comfortable with, and skilled in counseling these findings and when to refer to a clinical geneticist. Availability of different genetic tests, genetic care and agreement on what test can be requested by nongeneticists will differ per country.

In all the fenotypes described in this review, only a limited number of genes are responsible za the top 50% of found diagnoses, highlighting the relevantnosť z jadro gény pre fenotypy (Martin et al., 2019). Even hoci len a limited počet of gény are zodpovedný za the top 50% of genetic diagnoses, the study of Groopman et al. found that 39/66 detected monogénne poruchy were detected detected in only a single patient (Groopman et al., 2019). In this same study, four genes were responsible za 54% of confirmed diagnoses. Rao et al. reported that 15 genes accounted for 61% of genetic diagnoses, al in total, 106 distinct monogénne poruchy were detected in in of 1,001 pediatrický patients with klinický podozrenie of genetic kidney disease (Rao et al., 2019). Preto, my odporúčame zváženie analýza of a kompletný sad of známy obličky choroba gény po a prvý negatívny zaostrený založený na exóme panel výsledok, tiež v svetlo z the možnosť of fenokópií. My odporúčame nie začíname s s toto veľký súbor z génov na vyhýbať sa zbytočným variantom z neznámy významnosť a náhodné zistenia. Toto odporúčanie je o závislá na the dostupnosť a náklady z reanalýza per krajina. Zatiaľ čo toto preskúmanie zameria primárne na diagnostika výnos priečnik a vnútri v obličky fenotyp skupiny, dodatočné genetické testovanie úvahy pre obličky choroba patients can be found in new published recommendations (Knoers et al., 2022; Köttgen et al., 2022).

7|SILNÉ STRÁNKY A OBMEDZENIA 

Our systematic approach to selection and data extraction resulted in the most extensive overview of diagnostic yield in nephrogenesis yet. Given the challenges in comparing the different studies, we only summarized and visualized the data but did not perform a statistical meta-analysis. One important variable was variant classification which was not identical in all studies. In 59/115 articles only the American College of Medical Genetics and Genomics (ACMG) criteria were used and in 4/115 ACMG criteria were used together with other filtering steps (Supplementary Table 3). Some studies that used other variant classifications than the ACMG criteria did use the same variant descriptions (i.e., pathogenic and likely pathogenic variants) as shown in the pivot table in Supplementary Table 3. We did find that most recently published papers used ACMG criteria (Supplementary Figure 2d). Benson et al. highlights the impact that different classification criteria can have on the reported yield by reporting a diagnostic yield of 68% using ACMG criteria and a yield of 81% using Mayo Clinic pathogenicity guidelines. Furthermore, it was not always reported whether both likely pathogenic and pathogenic variants were included. In some cases, VUS were included in the reported diagnostic yield and it was not possible to subtract these, which might give an unjustly high yield. With WES-based panels and ACMG criteria being used more often, comparing future studies might be easier. We chose to include studies published in the last 10 years, but even with this limitation, we expect that the diagnostic yield was likely higher in the more recent articles because of an increase in the number of known kidney disease genes and novel techniques. Although our data do not clearly support this notion (data not shown), this is likely explained by other factors masking this effect. Future reporting on diagnostic yield would also benefit from reporting of the population of which the study population was derived from. This was not always reported in the included articles in this review, rendering interpretation of the minimum diagnostic yield in these (clinical) cohorts challenging

8|WHAT IS NEXT? 

Hoci toto recenzia dáva an extenzívny prehľad of hlásený diagnostic yield, tam sú stále medzery v vedomostiach týkajúce sa the true prevalencia of dedičné obličky choroba v the general CKD populácia. To get an odhad z toto, a štúdia by potreba to be nastavenie in in ktoré všetky obličky choroba pacienti videní v iní klinickí nastavenia by dostali genetické testovanie. Odvtedy toto mohol neby by uskutočniteľné, my chceme toto to by by na najmenej a volanie akcia na správu v podrobnosti na populácia hlásený v diagnostický výnos štúdie, na hlásiť na na pacienta, kohorta, a test charakteristiky spomenuté v toto preskúmanie, a to použitie štandard variant klasifikácia protokoly. tiež, príležitosti pre the segregáciu z varianty (z neznáme význam) a CNV analýza mala nie by vynechať. ďalší príležitosť na diagnostikovanie ďalší pacienti je WGS, ktorý umožňuje to sekvenciu the celý genóm. pre konečný určujúci the true diagnostický výnos tam je stále a dlhý cestný dopredu od tam sú pravdepodobne stále mnohopočetné kódovanie a nekódovanie genetické príčiny zapojené in obličky ochorenie to potreba na byť objavené.

9|ZÁVER

Toto recenzia dáva an prehľad of the diagnosticic yield of genetic testing across and within obličky choroba fenotypy. The most important findings and key take home messages are summarized in Figure 6. We confirm that patient characteristics (e.g., family history, consanguinity, extrarenal features, and young age of onset) can positively impact the diagnostic yield. Furthermore, we emphasize the impact of the specific genetic test requested, including its ability to reveal CNVs. We also show the importance of considering the kind of cohort in which a study was performed, for interpreting the reported yield. We show that a genetic diagnosis can have a diagnostic, therapeutic, and prognostic impact. Considering reclassifications based on genetic findings and the possibility to obviate the need for a diagnostic renal biopsy in many cases, a genetics-first approach can be considered in clinical practice for establishing the patient's diagnosis. The number of genes to examine, whether and how to perform CNV analysis and, in ADTKD/ADPKD additional tests to cover for MUC1 and PKD1 need to be weighed when requesting a genetic test. Of course, it is important to note that patient and family-specific situations can also influence the decision to do a genetic test, and also what genetic test is chosen. In addition, the availability of genetic testing in different countries can have an impact on the accessibility of genetic testing. This review gives clinicians guidance on estimating an a priori probability of finding a genetic cause for kidney disease in their patients.

AUTOR PRÍSPEVKY

Rozemarijn Snoek, Laura R. Claus, and Albertien M. van Eerde set the design for the review. Nine V. A. M. Knoers poskytnuté štrukturálne spätná väzba on the study design and progress. Rozemarijn Snoek extrahované dáta z a podmnožina z článkov na na definovať the premenné of interest. Laura R. Claus extrahované, a analyzované the data and drafted the paper. Albertien M. van Eerde and Nine V. A. M. Knoers kriticky hodnotené príspevok. Všetci autori schválený the konečná verzia z rukopis. POĎAKOVANIE The autori uznanie a poďakovanie Rieko Haring a Richard van Kemenade kto ran the literatúra databáza vyhľadávanie a preverovanie články pre preverovanie pre oprávnenosť as časť z ich bakalár diplomová práca. toto práca bola podporovaná podľa the holandčina Obličky nadácia (18OKG19 na A. M. v. E.). The autori z toto publikácia sú členovia z the európsky referencia Sieť pre zriedkavé obličky choroby (ERKNet). KONFLIKT OF ZÁUJMOV The autori vyhlásiť nie konflikt of záujmov.

DATA AVAILABILITY STATEMENT Data sharing is not apply to article as no new data were created  or analyzed in this study.

REFERENCIE 

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