CoDE-seq, an augmented whole-exome sequencing, enables the accurate detection of CNVs and mutations in Mendelian obesity and intellectual disability

Abstract

ObjectiveThe molecular diagnosis of extreme forms of obesity, in which accurate detection of both copy number variations (CNVs) and point mutations, is crucial for an optimal care of the patients and genetic counseling for their families. Whole-exome sequencing (WES) has benefited considerably this molecular diagnosis, but its poor ability to detect CNVs remains a major limitation. We aimed to develop a method (CoDE-seq) enabling the accurate detection of both CNVs and point mutations in one step.MethodsCoDE-seq is based on an augmented WES method, using probes distributed uniformly throughout the genome. CoDE-seq was validated in 40 patients for whom chromosomal DNA microarray was available. CNVs and mutations were assessed in 82 children/young adults with suspected Mendelian obesity and/or intellectual disability and in their parents when available (ntotal = 145).ResultsCoDE-seq not only detected all of the 97 CNVs identified by chromosomal DNA microarrays but also found 84 additional CNVs, due to a better resolution. When compared to CoDE-seq and chromosomal DNA microarrays, WES failed to detect 37% and 14% of CNVs, respectively. In the 82 patients, a likely molecular diagnosis was achieved in >30% of the patients. Half of the genetic diagnoses were explained by CNVs while the other half by mutations.ConclusionsCoDE-seq has proven cost-efficient and highly effective as it avoids the sequential genetic screening approaches currently used in clinical practice for the accurate detection of CNVs and point mutations.

Genetic Causes of Severe Childhood Obesity: A Remarkably High Prevalence in an Inbred Population of Pakistan

“…A second batch of 105 probands and 121 family members were screened with CoDEseq (when the technology was available and validated). This technique combines standard capture targeting the whole exome (NimbleGen SeqCap EZ MedExome Target Enrichment) with an in-house-designed capture (NimbleGen Seq-Cap EZ Choice XL) (5). Sequencing was performed on an Illumina NovaSeq 6000 system.…”

“…A mean sequencing depth of ;1003 was achieved for each individual using 150-bp paired-end reads. Computer analyses for variants detection and annotation have previously been described (5).…”

“…For detection of CNVs from the genotyping data, integrated hidden Markov model algorithm (PennCNV) with its default method was used as described previously (8,14). For detection of CNVs from CoDE-seq data, we used eXome Hidden Markov Model (XHMM, version 1.0) as previously described (5,15). We excluded all CNVs with allele frequency .0.002 in the gnomAD structural variant data set.…”

“…Pathogenic variations causing severe obesity include not only point mutations but also copy number variations (CNVs) (4). We have recently developed a new strategy that is based on an augmented whole-exome sequencing (WES) method, named CoDE-seq, that enables accurate and cost-effective detection of point mutations and CNVs in one step, thus expediting comprehensive genetic diagnosis (5).…”

Du nouveau dans la génétique des formes monogéniques d’obésité et son impact pour mieux en comprendre la physiopathologie

“…Les mutations hétérozygotes du gène SIM1, induisant une perte de fonction de la protéine, sont la cause d'une obésité sévère, associées ou non à une déficience intellectuelle (Tableau I) [23,24]. [36]), surtout lorsque l'obésité est apparue très précocement, afin de permettre une prise en charge personnalisée.…”