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Ma D, Ye M, Hu W, Gao H, Wang L, Song Y, Nie R, Hu Z, Guo H. Large regions of homozygosity in prenatal diagnosis. Am J Med Genet A 2024:e63712. [PMID: 38757552 DOI: 10.1002/ajmg.a.63712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/26/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Chromosomal microarrays (CMA) incorporate single nucleotide polymorphisms to enable the detection of regions of homozygosity (ROH). Here, we retrospectively analyzed 6288 prenatal cases who performed CMA to explored the clinical implications of large ROH in prenatal diagnosis. We analyzed cases with ROH larger than 10 megabases and reviewed the ultrasound findings; karyotype results and pregnancy follow-up data. Cases with possible imprinting disorders were assessed by methylation-specific multiplex ligation-dependent probe amplification. In total, we identified 50 cases with large ROH and chromosomes 1 and 2 were the most affected. About 59.18% of the ROH cases had ultrasound abnormalities, with the most common findings being ultrasound soft-marker abnormalities. There were seven fetuses had ROH which covered almost the entire chromosome and four had terminal ROH that involved almost the entire long arm of the chromosomes, which indicated uniparental disomy (UPD), of which 70% showed abnormal ultrasound findings. Ten cases with multiple ROH on different chromosomes indicated the third to fifth degree of consanguinity. In this study, we highlighted the clinical relevance of large ROH related to UPD. The analysis of ROH allowed us to gain further understanding of complex cytogenetic and disease mechanisms in prenatal diagnosis.
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Affiliation(s)
- Di Ma
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Mei Ye
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Clinical Medical Research Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Wenlong Hu
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Clinical Medical Research Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Hui Gao
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Lijuan Wang
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Yaqin Song
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Rui Nie
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Zhiyang Hu
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Department of Obstetrics, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Hui Guo
- Forensic Evidence Laboratory, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Genetic and Prenatal Disease Diagnosis Center, Shenzhen People's Hospital (the Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
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Lemes RB, Nunes K, Carnavalli JEP, Kimura L, Mingroni-Netto RC, Meyer D, Otto PA. Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate. PLoS One 2018; 13:e0196360. [PMID: 29689090 PMCID: PMC5916862 DOI: 10.1371/journal.pone.0196360] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/11/2018] [Indexed: 02/06/2023] Open
Abstract
The analysis of genomic data (~400,000 autosomal SNPs) enabled the reliable estimation of inbreeding levels in a sample of 541 individuals sampled from a highly admixed Brazilian population isolate (an African-derived quilombo in the State of São Paulo). To achieve this, different methods were applied to the joint information of two sets of markers (one complete and another excluding loci in patent linkage disequilibrium). This strategy allowed the detection and exclusion of markers that biased the estimation of the average population inbreeding coefficient (Wright's fixation index FIS), which value was eventually estimated as around 1% using any of the methods we applied. Quilombo demographic inferences were made by analyzing the structure of runs of homozygosity (ROH), which were adapted to cope with a highly admixed population with a complex foundation history. Our results suggest that the amount of ROH <2Mb of admixed populations should be somehow proportional to the genetic contribution from each parental population.
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Affiliation(s)
- Renan B. Lemes
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Kelly Nunes
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Juliana E. P. Carnavalli
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Lilian Kimura
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Regina C. Mingroni-Netto
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Diogo Meyer
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
| | - Paulo A. Otto
- Department of Genetics and Evolutionary Biology, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil
- * E-mail:
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Blant A, Kwong M, Szpiech ZA, Pemberton TJ. Weighted likelihood inference of genomic autozygosity patterns in dense genotype data. BMC Genomics 2017; 18:928. [PMID: 29191164 PMCID: PMC5709839 DOI: 10.1186/s12864-017-4312-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 11/16/2017] [Indexed: 12/14/2022] Open
Abstract
Background Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. Methods We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Results Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. Conclusions This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandra Blant
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Michelle Kwong
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Zachary A Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Trevor J Pemberton
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada.
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Karafet TM, Bulayeva KB, Bulayev OA, Gurgenova F, Omarova J, Yepiskoposyan L, Savina OV, Veeramah KR, Hammer MF. Extensive genome-wide autozygosity in the population isolates of Daghestan. Eur J Hum Genet 2015; 23:1405-12. [PMID: 25604856 DOI: 10.1038/ejhg.2014.299] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 12/09/2014] [Accepted: 12/19/2014] [Indexed: 01/01/2023] Open
Abstract
Isolated populations are valuable resources for mapping disease genes, as inbreeding increases genome-wide homozygosity and enhances the ability to map disease alleles on a genetically uniform background within a relatively homogenous environment. The populations of Daghestan are thought to have resided in the Caucasus Mountains for hundreds of generations and are characterized by a high prevalence of certain complex diseases. To explore the extent to which their unique population history led to increased levels of inbreeding, we genotyped >550 000 autosomal single-nucleotide polymorphisms (SNPs) in a set of 14 population isolates speaking Nakh-Daghestanian (ND) languages. The ND-speaking populations showed greatly elevated coefficients of inbreeding, very high numbers and long lengths of Runs of Homozygosity, and elevated linkage disequilibrium compared with surrounding groups from the Caucasus, the Near East, Europe, Central and South Asia. These results are consistent with the hypothesis that most ND-speaking groups descend from a common ancestral population that fragmented into a series of genetic isolates in the Daghestanian highlands. They have subsequently maintained a long-term small effective population size as a result of constant inbreeding and very low levels of gene flow. Given these findings, Daghestanian population isolates are likely to be useful for mapping genes associated with complex diseases.
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Affiliation(s)
- Tatiana M Karafet
- ARL Division of Biotechnology, University of Arizona, Tucson, AZ, USA
| | - Kazima B Bulayeva
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Oleg A Bulayev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Farida Gurgenova
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Jamilia Omarova
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Levon Yepiskoposyan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia
| | - Olga V Savina
- ARL Division of Biotechnology, University of Arizona, Tucson, AZ, USA
| | | | - Michael F Hammer
- ARL Division of Biotechnology, University of Arizona, Tucson, AZ, USA
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Genomic patterns of homozygosity in worldwide human populations. Am J Hum Genet 2012; 91:275-92. [PMID: 22883143 DOI: 10.1016/j.ajhg.2012.06.014] [Citation(s) in RCA: 307] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 05/09/2012] [Accepted: 06/25/2012] [Indexed: 12/20/2022] Open
Abstract
Genome-wide patterns of homozygosity runs and their variation across individuals provide a valuable and often untapped resource for studying human genetic diversity and evolutionary history. Using genotype data at 577,489 autosomal SNPs, we employed a likelihood-based approach to identify runs of homozygosity (ROH) in 1,839 individuals representing 64 worldwide populations, classifying them by length into three classes-short, intermediate, and long-with a model-based clustering algorithm. For each class, the number and total length of ROH per individual show considerable variation across individuals and populations. The total lengths of short and intermediate ROH per individual increase with the distance of a population from East Africa, in agreement with similar patterns previously observed for locus-wise homozygosity and linkage disequilibrium. By contrast, total lengths of long ROH show large interindividual variations that probably reflect recent inbreeding patterns, with higher values occurring more often in populations with known high frequencies of consanguineous unions. Across the genome, distributions of ROH are not uniform, and they have distinctive continental patterns. ROH frequencies across the genome are correlated with local genomic variables such as recombination rate, as well as with signals of recent positive selection. In addition, long ROH are more frequent in genomic regions harboring genes associated with autosomal-dominant diseases than in regions not implicated in Mendelian diseases. These results provide insight into the way in which homozygosity patterns are produced, and they generate baseline homozygosity patterns that can be used to aid homozygosity mapping of genes associated with recessive diseases.
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