European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases

Figure 1. The genetic aetiology of cardiovascular diseases. Mendelian disease variants (upper left panel) are ultra-rare in the population and have large effect sizes, though often not sufficient in isolation to yield a disease phenotype. Mendelian genes and variants can be identified through analysis of family pedigrees or burden analysis in case–control studies and further validated with functional assays. Common variants (upper right panel) with individually small effect sizes may collectively contribute to disease burden or modulate the effects of Mendelian variants. Intermediate effect variants (upper middle panel) are emerging variant classes that usually have population frequencies and effect sizes between rare Mendelian and common variants and may act to increase severity and penetrance. Such variants can be identified by demonstrating enrichment in case cohorts and deleterious effects in established functional assays. These different variant classes can combine to reach the threshold of disease in patients with rare cardiovascular diseases and contribute to the variable severity observed in patients. Diseases such as HCM and LQTS are often Mendelian [1] or near-Mendelian where…

Figure 2. Genome-wide association studies (GWAS) test the association of common genetic variants with traits or diseases. Results are shown as a Manhattan plot (A) where the P-value (y-axis) is plotted against the genomic position (x-axis) for millions of common variants across the genome (blue markers). Polygenic risk scores (B) are generally derived from GWAS and calculated for an individual i (PRSi) as the sum of the products of allelic dosage (dosageij) by the regression coefficient/weight (bj) for all M genetic variants (j). Created with Biorender.com.

Figure 3. Clinical algorithm for genetic testing and family screening in long-QT syndrome.

Figure 4. Clinical algorithm for genetic testing and family screening in Brugada syndrome.

Figure 5. Clinical algorithm for genetic testing and family screening in short-QT syndrome. aAdapted from HRS/EHRA/APHRS Expert consensus recommendations on diagnosis of SQTS.15 bAdapted from Gollob et al.,219 see Supplementary material online, Table S9.

Figure 6. Flowchart of the work up of a sudden cardiac death or non-fatal cardiac arrest.

Figure 7. Genetic causes of congenital heart defects. Non-syndromic (lower panel) and syndromic (upper panel) cohorts. The diagram in the left panel displays the relative prevalence of the three broad CHD subgroups, namely syndromic CHD, sporadic non-syndromic CHD, and familial non-syndromic CHD. The diagrams in the central panel display the current yield of standard karyotyping, CMA and WES/WGS in the non-syndromic (lower panel) and syndromic (upper panel) cohorts, respectively, illustrating the low diagnostic yield in sporadic non-syndromic CHD, compared to the syndromic cohort. The pie diagrams at the right display the most common causes of aneuploidies and of CNVs, and the inheritance pattern of single gene defects. The percentages displayed in the diagrams are based on.440,441,445,463,464 CHD, congenital heart defect; CNV, copy number variant; T13, trisomy 13; T18, Trisomy 18; T21, trisomy 21; WBS, Williams–Beuren syndrome.