Table 2 Currently applied methods in human genetic diagnostics of endocrine disorders: Applications, advantages and limitations. The methods can roughly be discriminated in respect to main type of molecular alteration they address, though some of them can also identify other changes. (*The currently used conventional diagnostic often address either copy number variants (CNVs, i.e. deletions and duplications) or single nucleotide variants (SNVs). In fact, CNVs represent a mutational burden in several genetic disorders. Therefore, parallel CNV assessment using alternate supplemental methods is normally required. For their identification, (semi)quantitative assays have been developed, and in human genetic testing multiplex ligation-dependent probe amplification (MLPA) is a broadly implemented diagnostic tool. However, the development of bioinformatics CNV pipelines for NGS data is in progress (e.g. [7]), and CNV detection by NGS is already in establishment. (*Multigene panels can either be based on targeted enrichment assays by which only the regions of interest are enriched in the wetlab, or they can be defined as a virtual WES dataset which has been filtered and analysed for the region of interest only. FISH: fluorescence in-situ hybridization, ASO: allele-specific oligonucleotide, MLPA: multiplex ligation-dependent probe amplification, SNP: single nucleotide polymorphism, CGH: comparative genome hybridization; WES: whole exome sequencing; WGS: whole genome sequencing; TGS: third generation sequencing; VUS: variant of unknown significance)

 Conventional cytogenetics

Whole genome

General overview on chromosomal number and structure; Mosaicism might be detected.

Resolution is > 5 Mb, smaller CNVs escape detection. SNVs not detectable. Cell culture required. Time and work consuming.

 FISH

Specific chromosomal regions, whole chromosomes

Identification of structural rearragements. Detection of mosaicism.

Target region has to be known or should be suspected. Low resolution. Intact cells required.

 Multiplex Ligation-dependent Probe Amplification (MLPA)

Single gene testing; specific genomic regions (60–100 bp)

Specific detection of genomic CNVs, appropriate for identification of deletions/duplications of selected exons.

Only targeted fragments are quantified. Restricted number of fragments per analysis (up to 60).

 Whole genome imaging

Whole genome, specific chromosomal regions

General overview on chromosomal number and structure; Identification of structural rearrangements.

Detection of both numerical and structural aberrations with a relative high resolution (> 150 kb). Fresh samples required.

 Microarray (SNP array, array CGH)

Whole genome

General overview on copy number variants, resolution of few kilobases.

Balanced chromosomal aberrations not detectable. Resolution on single gene level might be difficult.

 NGS assays (Panels, WES, WGS, TGS)

See below

Comprehensive overview, dependent on the bioinformatics pipeline CNVs and structural variants can be detected

See below

 Single variant testing / Hotspot-mutation: e.g. ASO, single fragment sequencing, fragment analysis

SNVs, Trinucleotide repeat expansion

Very specific, fast, cheap.

Only single variants or trinucleotide repeats are addressed.

 Single gene testing (e.g. Sanger sequencing)

Single genes

Target specific, appropriate and economic tool for monogenetic single locus disorders with characteristic clinical signs.

Large genes difficult to analyze. Not appropriate for heterogeneous disorders.

 Multigene panel*

Genomic sequences (mainly coding regions and neighbored intronic regions) of selected genes associated with specific phenotypes

Target analyses of a group of genes associated with specific phenotypes. Low chance for incidental findings. Suitable for heterogeneous disorders with specific clinical features.

In case new genes are identified, adaption of a panel might be difficult or delayed in time. Variants in genes associated with overlapping phenotypes (differential diagnoses) might not be included in a panel. Non-coding regions are not covered.

 Clinical exome

Coding and regulatory domains of all genes known to harbor clinically relevant variants

Analysis of a huge number of clinically relevant genes. Both disease-specific genes as well as differential diagnostic genes are analyzed. Suitable for disorders with unspecific clinical features

Increased probability to detect incidental findings. Increased probability for VUS. Fixed panel, new disease-associated genes are integrated after a delay. Non-coding regions are not covered.

 Whole Exome sequencing/WES

Coding regions of ~ 19,000 protein coding genes (~ 180,000 exons); 1–2% of the human genome

All protein coding regions are covered. Identification of new disease-causing genes possible. Suitable for disorders with unspecific phenotypes

Detection of VUS and incidental findings probable. Non-coding regions are not covered. Analysis, interpretation and storage of large datasets required.

 Whole Genome sequencing/WGS (short read)

Total human genome

Whole genome is analyzed.

New genes as well as genomic variants in non-coding regions can be identified. Suitable for disorders with unspecific phenotypes.

Detection of VUS and incidental findings very probable. Analysis, interpretation and storage of very large datasets required.

 Third Generation Sequencing (long read, TGS)

Ranging from defined chromosomal region to whole genome

Identification of chromosomal rearrangements and CNVs. Determination of physical breakpoints.

Resolution on single nucleotide level currently difficult.

 Single testing of imprinted loci (MS MLPA, MS pyrosequencing)

Single differentially methylated regions

Target specific, appropriate and economic tool for specific imprinting disorders.

Not appropriate for heterogeneous phenotypes. Multilocus disturbances are not detected.

 Methylation-specific tests/Methylome

Ranging from single CpGs (e.g. PCR) and multilocus tests (e.g. MLPA) to genomewide analyses (array, NGS)

Identification of imbalanced methylation at selected CpGs.

Different causes aberrant methylation pattern can be identified (UPD, CNV, epimutation). New and/or rare entities associated with disturbed imprinting can be identified.

Dependent on the test, different causes of aberrant methylation cannot be discriminated. In case of single and multilocus analyses non-targeted loci escape detection. In case of genome-wide analyses large datasets require comprehensive analyses and control data.

 NGS assays: Panels, WES, WGS, TGS

See above

Comprehensive overview on altered methylation patterns.

See above

 Transcriptome

Set of all RNA molecules in one cell or a population of cells

Identification of variants affecting splicing and causing allelic imbalances. Enhancement of the efficiency to identify functionally relevant variants. Complementary tool for WES and WGS.

Detected RNAs depend on the used tissues/cells.

RNAs which are not expressed in this tissue are missed. Integration with data from other omic assays required