Genealogical data in population medical genetics: Field guidelines

Iterative Level-0: A new and fast algorithm to traverse mating networks calculating the inbreeding and relationship coefficients

In population medical genetics, the study of autosomal recessive disorders in highly endogamous populations is a major topic where calculating the inbreeding and relationship coefficients on mating networks is crucial. However, a challenge arises when dealing with large and complex mating networks, making their traversal difficult during the calculation process. For this calculation, we propose using Iterative Level-0 (IL0) as a new and faster algorithm that traverses mating networks more efficiently. The purpose of this work is to explain in detail the IL0 algorithm and prove its superiority by comparing it with two algorithms based on the best-known algorithms in the area: Depth First Search (DFS) and Breadth First Search (BFS). A Cytoscape application has been developed to calculate the inbreeding and relationship coefficients of individuals composing any mating network. In this application, the IL0 proposal together with DFS-based and BFS-based algorithms have been implemented. Any user can access this freely available Cytoscape application (https://apps.cytoscape.org/apps/inbreeding) that allows the comparison between the IL0 proposal and the best-known algorithms (based on DFS and BFS). In addition, a diverse set of mating networks has been collected in terms of complexity (number of edges) and species (humans, primates, and dogs) for the experiments. The runtime obtained by the IL0, DFS-based, and BFS-based algorithms when calculating the inbreeding and relationship coefficients proved the improvement of IL0. In fact, a speedup study reflected that the IL0 algorithm is 7.60 to 127.50 times faster than DFS-based and BFS-based algorithms. Moreover, a scalability study found that the growth of the IL0 runtime has a linear dependence on the number of edges of the mating network, while the DFS-based and BFS-based runtimes have a quadratic dependence. Therefore, the IL0 algorithm can solve the problem of calculating the inbreeding and relationship coefficients many times faster (up to 127.50) than the two algorithms based on the famous DFS and BFS. Furthermore, our results demonstrate that IL0 scales much better as the complexity of mating networks increases.

Clusters of rare disorders and congenital anomalies in South America

Objective

To map geographic clusters of rare disorders and congenital anomalies reported in South America.

Methods

Qualitative systematic review conducted in Medline/PubMed, Lilacs, and Scielo electronic databases to identify studies meeting eligibility criteria. The strategy resulted in 1 672 unique articles, from which 164 were selected for full reading by a pair of reviewers.

Results

Fifty-five articles reported at least one cluster of genetic disorders or congenital anomalies in South American territory. From these papers, 122 clusters were identified, of which half (61) were related to autosomal recessive disorders. Sixty-five (53.3%) of the clusters were located in Brazil.

Conclusions

The results of the review reinforce that rare diseases and congenital anomalies can occur in a non-random way in space, which is discussed in the perspective of the complex history of formation, social organization, and genetic structure of the South American population. Mapping clusters in population medical genetics can be an important public health tool, given that such places concentrate cases of rare diseases that frequently require multiprofessional, specialized care. Therefore, these results can support important agendas in public health related to rare diseases and congenital anomalies, such as health promotion and surveillance.

Mosaicism in Fragile X syndrome: A family case series

Fragile X syndrome (FXS) has a classic phenotype, however its expression can be variable among full mutation males. This is secondary to variable methylation mosaicisms and the number of CGG triplet repeats in the non-coding region of the Fragile X Mental Retardation 1 (FMR1) gene, producing a variable expression of the Fragile X Mental Retardation Protein (FMRP). Here we report a family with several individuals affected by FXS: a boy with a hypermethylated FMR1 mutation and a classic phenotype; a man with an FMR1 gene mosaicism in the range of premutation (PM) and full mutation (FM), who has a mild phenotype due to which FXS was initially disregarded; and the cases of four women with a FM and mosaicism. This report highlights the importance of DNA molecular testing for the diagnosis of FXS in patients with developmental delay, intellectual disability and/or autism due to the variable phenotype that occurs in individuals with FMR1 mosaicisms.

Genetic cluster of fragile X syndrome in a Colombian district

BACKGROUND

Fragile X syndrome (FXS) is the most common cause of inherited intellectual disabilities and autism. The reported prevalence of the full mutation (FM) gene FMR1 in the general population is 0.2-0.4 per 1000 males and 0.125-0.4 per 1000 females. Population screening for FMR1 expanded alleles has been performed in newborns and in an adult population. However, it has never been carried out in an entire town. Ricaurte is a Colombian district with 1186 habitants, with a high prevalence of FXS, which was first described by cytogenetic techniques in 1999.

METHODS

Using a PCR-based approach, screening for FXS was performed on blood spot samples obtained from 926 (502 males and 424 females) inhabitants from Ricaurte, accounting for 78% of total population.

RESULTS

A high prevalence of carriers of the expanded allele was observed in all FXS mutation categories. Using the Bayesian methods the carrier frequency of FM was 48.2 (95% Credibility Region CR: 36.3-61.5) per 1000 males and 20.5 (95% CR:13.5-28.6) per 1000 females; the frequency of premutation carrier was 14.1 (95% RC: 8.0-21.7) per 1000 males (95% RC: 8.0-21.7 per 1000 males) and 35.9 (95% RC: 26.5-46.2) per 1000 for females (95% RC: 26.5-46.2 per 1000 females), and gray zone carrier was 13.4 (95% RC: 7.4-20.7) per 1000 males (95% RC: 7.4-20.7 per 1000 males) and 42.2 (95% RC: 32.2-53.8) per 1000 for females (95% RC: 32.2-53.8 per 1000 females). Differences in carrier frequencies were observed for premutation and FM alleles between natives and non-natives.

CONCLUSIONS

This study shows that in Ricaurte the carrier frequencies of FMR1 expanded alleles (premutations and FMs) are higher than those reported in the literature, suggesting that Ricaurte constitutes a genetic cluster of FXS.