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Molecular Biology and Evolution Aug 2023Wang et al. (2023) recently proposed an approach to infer the history of human generation intervals from changes in mutation profiles over time. As the relative...
Wang et al. (2023) recently proposed an approach to infer the history of human generation intervals from changes in mutation profiles over time. As the relative proportions of different mutation types depend on the ages of parents, binning variants by the time they arose allows for the inference of changes in average paternal and maternal generation intervals. Applying this approach to published allele age estimates, Wang et al. (2023) inferred long-lasting sex differences in average generation times and surprisingly found that ancestral generation times of West African populations remained substantially higher than those of Eurasian populations extending tens of thousands of generations into the past. Here, we argue that the results and interpretations in Wang et al. (2023) are primarily driven by noise and biases in input data and a lack of validation using independent approaches for estimating allele ages. With the recent development of methods to reconstruct genome-wide gene genealogies, coalescence times, and allele ages, we caution that downstream analyses may be strongly influenced by uncharacterized biases in their output.
Topics: Humans; Female; Male; Uncertainty; Mutation; Alleles
PubMed: 37450583
DOI: 10.1093/molbev/msad160 -
HLA Jan 2024The data enabling the estimation of the possibility of finding a matched unrelated donor (MUD) within a relatively short time is important for the success of...
The data enabling the estimation of the possibility of finding a matched unrelated donor (MUD) within a relatively short time is important for the success of hematopoietic stem cell transplantation (HSCT). In the present study, 738 unrelated Croatian patients in the program of unrelated HSCT were retrospectively analyzed for gender matching, donor origin (national or international), the distribution of HLA alleles and haplotypes, as well as for the probability of finding a 9-10/10 MUD. Almost 70% of the patients in our study group had a 10/10 MUD, while among the patients with a 9/10 MUD, a 1st field resolution level mismatched donor was selected for 55.0% of patients. The majority of pairs were HLA-A mismatched (33.8%). A comparison of HLA allele frequencies between two subgroups of patients revealed significant differences for 13 alleles. However, after p value correction, the difference in frequency remained significant only for four alleles; three HLA alleles (B*08:01, C*07:01, and DRB1*03:01) demonstrated a significantly higher frequency among patients with a 10/10 MUD (Pcorr < 0.0001, Pcorr = 0.0096, and Pcorr < 0.0001, respectively), while the B*35:08 allele was significantly more present among patients with a 9/10 MUD (Pcorr = 0.0328). The comparison of the distribution of HLA haplotypes between patients with a 10/10 MUD and patients with a 9/10 MUD showed significant differences for a number of two-locus and three-locus haplotypes, as well as for one five-locus haplotype (HLA-A*01:01~B*08:01~C*07:01~DRB1*03:01~DQB1*02:01), which was significantly more present in the group of patients with a 10/10 MUD. At least one HLA haplotype from the group of non-frequent HLA haplotypes (positions >1000) was carried by patients with a 9/10 MUD. The data obtained by the present study will contribute to a better estimation of the probability of finding a suitable 9-10/10 MUD for Croatian patients in need of HSCT.
Topics: Humans; Alleles; Retrospective Studies; Tissue Donors; Registries; HLA-A Antigens
PubMed: 38265197
DOI: 10.1111/tan.15348 -
HLA Nov 2023Compared to HLA-DRB1*09:01:02:01, the alleles HLA-DRB1*09:01:12 and HLA-DRB1*09:49 each show one nucleotide substitution, respectively.
Compared to HLA-DRB1*09:01:02:01, the alleles HLA-DRB1*09:01:12 and HLA-DRB1*09:49 each show one nucleotide substitution, respectively.
Topics: Humans; HLA-DRB1 Chains; Alleles; Base Sequence; Nucleotides; High-Throughput Nucleotide Sequencing
PubMed: 37539815
DOI: 10.1111/tan.15178 -
HLA Jan 2024One nucleotide substitution in codon 279 of HLA-A*03:02:01:01 results in the novel HLA-A*03:02:07 allele.
One nucleotide substitution in codon 279 of HLA-A*03:02:01:01 results in the novel HLA-A*03:02:07 allele.
Topics: Humans; Alleles; Codon; Nucleotides; HLA-A Antigens; Russia
PubMed: 37827856
DOI: 10.1111/tan.15259 -
HLA Feb 2024The novel HLA-C*01:65:02 allele was detected during routine HLA typing.
The novel HLA-C*01:65:02 allele was detected during routine HLA typing.
Topics: Humans; HLA-C Antigens; Alleles; Genes, MHC Class I; High-Throughput Nucleotide Sequencing; Histocompatibility Testing
PubMed: 38372620
DOI: 10.1111/tan.15415 -
Science China. Life Sciences Dec 2023Male sterility is an important trait in rice for hybrid rice (Oryza sativa) breeding. However, the factors involved in dominant male sterility are largely unknown. Here,...
Male sterility is an important trait in rice for hybrid rice (Oryza sativa) breeding. However, the factors involved in dominant male sterility are largely unknown. Here, we identified a gene from Sanming dominant genic male sterile rice, named Sanming dominant male sterility (SMS), and reported that an epi-allele of this locus contributes to male sterility. Segregation analysis attributed dominant male sterility to a single locus, SMS, which we characterized using a male-sterile near isogenic line (NIL) of rice cultivar 93-11. The SMS locus was heterozygous in the male-sterile 93-11 NIL, containing an epi-allele identical to that in 93-11, and an epi-allele identical to that in rice cultivar Nipponbare, which we refer to as SMS and SMS, respectively. SMS is silent and hyper-methylated, whereas SMS is expressed and hypo-methylated in the 93-11 NIL. Overexpressing SMS led to male sterility. Mutations in SMS rescued the male sterility of the 93-11 NIL. Interestingly, we observed the duplication of SMS in Nipponbare, but did not observe the duplication of SMS in 93-11. Together, these findings suggest that the reduced methylation and enhanced expression of the SMS epi-allele in the 93-11 NIL is responsible for its role in conferring dominant male sterility.
Topics: Alleles; Oryza; Phenotype; Plant Breeding; Plant Infertility
PubMed: 37930474
DOI: 10.1007/s11427-023-2457-7 -
HLA Mar 2024A single nucleotide mismatch within intron 1 differentiates HLA-A*02:01:01:251 from the HLA-A*02:01:01:01 allele.
A single nucleotide mismatch within intron 1 differentiates HLA-A*02:01:01:251 from the HLA-A*02:01:01:01 allele.
Topics: Humans; Alleles; Bone Marrow; High-Throughput Nucleotide Sequencing; Introns; HLA-A Antigens
PubMed: 38445381
DOI: 10.1111/tan.15430 -
Medicina (Kaunas, Lithuania) Aug 2023: Specific Learning Disorder (SLD) is a complex neurobiological disorder characterized by a persistent difficult in reading (dyslexia), written expression (dysgraphia),...
: Specific Learning Disorder (SLD) is a complex neurobiological disorder characterized by a persistent difficult in reading (dyslexia), written expression (dysgraphia), and mathematics (dyscalculia). The hereditary and genetic component is one of the underlying causes of SLD, but the relationship between genes and the environment should be considered. Several genetic studies were performed in different populations to identify causative genes. : Here, we show the analysis of 9 multiplex families with at least 2 individuals diagnosed with SLD per family, with a total of 37 persons, 21 of whom are young subjects with SLD, by means of Next-Generation Sequencing (NGS) to identify possible causative mutations in a panel of 15 candidate genes: , , , , , , , , , , , , , , and . We detected, in eight families out nine, SNP variants in the , , , and genes, even if in silico analysis did not show any causative effect on this behavioral condition. In all cases, the mutation was transmitted by one of the two parents, thus excluding the case of de novo mutation. Moreover, the parent carrying the allelic variant transmitted to the children, in six out of seven families, reports language difficulties. : Although the present results cannot be considered conclusive due to the limited sample size, the identification of genetic variants in the above genes can provide input for further research on the same, as well as on other genes/mutations, to better understand the genetic basis of this disorder, and from this perspective, to better understand also the neuropsychological and social aspects connected to this disorder, which affects an increasing number of young people.
Topics: Child; Humans; Adolescent; Specific Learning Disorder; Nerve Tissue Proteins; Receptors, Immunologic; Alleles; High-Throughput Nucleotide Sequencing; Microtubule-Associated Proteins
PubMed: 37629793
DOI: 10.3390/medicina59081503 -
HLA Aug 2023The novel HLA-C*01:230 allele was characterized using next generation sequencing technology.
The novel HLA-C*01:230 allele was characterized using next generation sequencing technology.
Topics: Humans; HLA-C Antigens; High-Throughput Nucleotide Sequencing; Alleles; Genes, MHC Class I; Russia
PubMed: 37127356
DOI: 10.1111/tan.15069 -
The Journal of Allergy and Clinical... Aug 2023Nearly 50 pathogenic genes and hundreds of pathogenic variants have been identified in monogenic autoinflammatory diseases (AIDs). Nonetheless, there are still many...
BACKGROUND
Nearly 50 pathogenic genes and hundreds of pathogenic variants have been identified in monogenic autoinflammatory diseases (AIDs). Nonetheless, there are still many genes for which the pathogenic mechanisms are poorly understood, and the pathogenicity of many candidate variants needs to be determined.
OBJECTIVE
Monogenic AIDs are a group of rare genetic diseases characterized by inflammation as the phenotype. With the development of next-generation sequencing, pathogenic genes have been widely reported and used for clinical screening and diagnosis. The International Society for Systemic Autoinflammatory Diseases has recognized approximately 50 pathogenic genes and hundreds of related pathogenic variants in monogenic AIDs. We plan to investigate these pathogenic variants by conducting a variant burden analysis to determine whether or not there are consistent characteristics.
METHODS
We performed a variant burden analysis on the Genome Aggregation Database cohort using the currently reported genetic variants in monogenic AIDs, analyzing the enrichment of allelic signatures and deleterious predictions at the variants. Allelic signatures were extracted from Genome Aggregation Database, and the deleterious predictions were extracted from existing tools. The features obtained from the variant burden analysis were applied to the Random Forest model to classify the pathogenicity of novel mutations.
RESULTS
Functional enrichment and network analysis of AID pathogenic genes have hinted at the possible involvement of unsuspected signals. The variant burden analysis demonstrated that the pathogenicity of a variant could not be reliably classified using only its allele frequency and deleterious predictions. However, variants of varying classifications of pathogenicity exhibited strikingly different patterns of the allelic signature in the upstream and downstream regions surrounding the variants. Furthermore, the distribution of deleterious variants surrounding the variants in the cohort varied significantly across pathogenicity categories. Finally, the cohort-based features extracted from the alleles were applied to the prediction of pathogenicity in monogenic AIDs, achieving superior prediction performance compared with other tools. The cohort-based features have potential applications across a more extensive variety of disease categories.
CONCLUSIONS
The pathogenicity of a variant can be effectively classified on the basis of variant frequency and deleterious prediction of the allele in the cohort, and this information can be used to improve the accuracy of the current classification of the pathogenicity of the variant.
Topics: Humans; Virulence; Gene Frequency; Phenotype; Alleles; Rare Diseases; Hereditary Autoinflammatory Diseases
PubMed: 37030591
DOI: 10.1016/j.jaci.2023.03.028