A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.

BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases. METHODS: We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis. FINDINGS: Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations. CONCLUSIONS: ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Rare coding variants involving MYO7A and other genes encoding stereocilia link proteins in familial meniere disease.

The MYO7A gene encodes a motor protein with a key role in the organization of stereocilia in auditory and vestibular hair cells. Rare variants in the MYO7A (myosin VIIA) gene may cause autosomal dominant (AD) or autosomal recessive (AR) sensorineural hearing loss (SNHL) accompanied by vestibular dysfunction or retinitis pigmentosa (Usher syndrome type 1B). Familial Meniere's disease (MD) is a rare inner ear syndrome mainly characterized by low-frequency sensorineural hearing loss and episodic vertigo associated with tinnitus. Familial aggregation has been found in 6-8% of sporadic cases, and most of the reported genes were involved in single families. Thus, this study aimed to search for relevant genes not previously linked to familial MD. Through exome sequencing and segregation analysis in 62 MD families, we have found a total of 1 novel and 8 rare heterozygous variants in the MYO7A gene in 9 non-related families. Carriers of rare variants in MYO7A showed autosomal dominant or autosomal recessive SNHL in familial MD. Additionally, some novel and rare variants in other genes involved in the organization of the stereocilia links such as CDH23, PCDH15 or ADGRV1 co-segregated in the same patients. Our findings reveal a co-segregation of rare variants in the MYO7A gene and other structural myosin VIIA binding proteins involved in the tip and ankle links of the hair cell stereocilia. We suggest that recessive digenic inheritance involving these genes could affect the ultrastructure of the stereocilia links in familial MD.