American Journal of Laboratory Medicine

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

On the Basics of Pedigree Visualization and Feature Extraction for the Autosomal Recessive Inheritance Pattern

Pedigree analysis is carried out to understand the mode of inheritance of a particular disease, which includes recessive, dominant, partial dominant, autosomal, mitochondrial or sex-linked etc. It also determines the individual’s probability of affecting in a given cross. Genetic disorders are transmitted from one generation to the next by following a particular inheritance pattern. Correct investigation of the transmission patterns of a trait under different circumstances is the crucial in genetic research. That is, identification of ancestry patterns for twins, full penetrance and reduced penetrance cases etc. This paper considers autosomal recessive case. Two simulated single nucleotide polymorphisms (SNPs) based genotype data sets for 14 and 47 individuals with three and four generations, respectively, were applied for this investigation. This evaluation looks for the probable features of ancestry patterns of a genetic disorder from one generation to the next based on the specified genetic conditions. Proper visualization of the pedigree charts for autosomal recessive case having different characteristics were demonstrated here. Since, sequencing of deoxyribonucleic acid (DNA), and handling of such massive amount of data depends on the availability of funding, dedicated software, high throughput data storage capacity etc. Hence, effective simulation for data generation would be the choice to cope with this situation for realizing the pipeline of such genetic research. The main objective of this paper is to provide a useful guideline for the introductory genetic researchers to whom real data sets are not available, and once available, dealing with this massive amount of sequencing data is a big challenge due to some limitations. This guideline will help to have an idea about such research. If opportunity is given, this idea could be applied for the real data sets, and the results would be similar.

Pedigree, Inheritance, Autosomal Recessive, SNP, DNA

APA Style

Ummay Tania Akter, Sajjad Bin Sogir, Tapati Basak. (2023). On the Basics of Pedigree Visualization and Feature Extraction for the Autosomal Recessive Inheritance Pattern. American Journal of Laboratory Medicine, 8(1), 4-12. https://doi.org/10.11648/j.ajlm.20230801.12

ACS Style

Ummay Tania Akter; Sajjad Bin Sogir; Tapati Basak. On the Basics of Pedigree Visualization and Feature Extraction for the Autosomal Recessive Inheritance Pattern. Am. J. Lab. Med. 2023, 8(1), 4-12. doi: 10.11648/j.ajlm.20230801.12

AMA Style

Ummay Tania Akter, Sajjad Bin Sogir, Tapati Basak. On the Basics of Pedigree Visualization and Feature Extraction for the Autosomal Recessive Inheritance Pattern. Am J Lab Med. 2023;8(1):4-12. doi: 10.11648/j.ajlm.20230801.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Vinci T, Robert JS (2005) Aristotle and modern genetics. Journal of the History of Ideas 66 (2): 201–221. https://doi.org/10.1353/jhi.2005.0041
2. Ziegler A, König IR (2010) A Statistical Approach to Genetic Epidemiology: Concepts and Applications. Weinheim: Wiley-VCH, Germany. https://www.10.1002/9783527633654
3. Porter TM (2014) The curious case of blending inheritance. Studies in History and Philosophy of Science Part C : Studies in History and Philosophy of Biological and Biomedical Sciences 46 (1): 125–132. https://doi.org/10.1016/j.shpsc.2014.02.003
4. Abbott S, Fairbanks DJ (2016) Experiments on Plant Hybrids by Gregor Mendel. Genetics 204 (2): 4.7-422. https://doi.org/10.1534/genetics.116.195198
5. Galla SJ, Brown L, Couch-Lewis Y, Cubrinovska I, Eason D, Gooley RM et al. (2021) The relevance of pedigrees in the conservation genomics era. Molecular Biology 31 (1): 41-54. https://doi.org/10.1111/mec.16192
6. Wijsman EM (2012) The role of large pedigrees in an era of high-throughput sequencing Hum Genet. 131 (10): 1555–1563. https://doi.org/10.1007/s00439-012-1190-2
7. Smýkal PK, Varshney RK, Singh V, Coyne CJ, Domoney C, Kejnovský E, Warkentin T (2016) From Mendel’s discovery on pea to today’s plant genetics and breeding. Theoretical and Applied Genetics 129 (12): 2267–2280. https://doi.org/10.1007/s00122-016-2803-2
8. Timm J, Oberste N, Schmiemann P (2022) Which factors influence the success in pedigree analysis? International Journal of Science Education. https://doi.org/10.1080/09500693.2022.2155494
9. Middeldorp CM, de Moor MHM, McGrath LM, Gordon SD, Blackwood DH, Costa PT et al. (2011) The genetic association between personality and major depression or bipolar disorder. A polygenic score analysis using genome-wide association data. Translational Psychiatry, 1 (e50). https://doi.org/10.1038/tp.2011.45
10. Singh R, Sophiarani Y (2020) A report on DNA sequence determinants in gene expression. Bioinformation, 16 (5), 422. https://doi.org/10.6026/97320630016422
11. Waller RG, Darlington TM, Wei X, Madsen MJ, Thomas A, Curtin K et al. (2018) Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk. PLoS Genet 14 (2): e1007111. https://doi.org/10.1371/journal.pgen.1007111
12. Xia C, Amador C, Huffman J, Trochet H, Campbell A, Porteous, D et al. (2016) Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation PLoS Genet 12 (2): e1005804. https://doi.org/10.1371/journal.pgen.1005804
13. Xu J (2005) The inheritance of organelle genes and genomes: Patterns and mechanisms. Genome 48 (6): 951–958. https://doi.org/10.1139/g05-082
14. Rafeeq MM, Murad HAS (2017) Cystic fibrosis: Current therapeutic targets and future approaches. Journal of Translational Medicine 15 (84). https://doi.org/10.1186/s12967-017-1193-9
15. Miller CH, Soucie MJ, Byams VR, Payne AB, Sidonio RF (Jr.), Buckner TW, Bean CJ (2021) Women and girls with haemophilia receiving care at specialized haemophilia treatment centres in the United States. Haemophilia 27 (6): 1037-1044. https://doi.org/10.1111/hae.14403
16. Cohen MM, Gorlin RJ (1991) Pseudo-Trisomy 13 Syndrome. Am J Med Genet 39 (3): 332-5. https://doi.org/10.1002/ajmg.1320390316.
17. Emery AEH (1989) Emery-Dreifuss syndrome. Med Genet 26 (10): 637-41. https://doi.org/10.1136/jmg.26.10.637
18. Pace BS, Starlard-Davenport A, Kutlar A (2021) Sickle cell disease: progress towards combination drug therapy. British Journal of Haematology 194 (2): 240–251. https://doi.org/10.1111/bjh.17312
19. McColgan P, Tabrizi SJ (2018) Huntington’s disease: a clinical review. European Journal of Neurology 25 (1): 24–34. https://doi.org/10.1111/ene.13413
20. Kabra M, Gulati S, Ghosh M, Menon PSN (2000) Fraser-Cryptophthalmos Syndrome. The Indian Journal of Pediatrics 67: 775–778. https://doi.org/10.1007/BF02723939
21. Lacy RC (2012) Extending pedigree analysis for uncertain parentage and diverse breeding systems. Journal of Heredity 103 (2): 197–205. https://doi.org/10.1093/jhered/esr135
22. Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. Journal of Applied Genetics 52 (4): 413–435. https://doi.org/10.1007/s13353-011-0057-x
23. Ellingford JM, Barton S, Bhaskar S, Williams SG, Sergouniotis PI, O’Sullivan J et al. (2016) Whole Genome Sequencing Increases Molecular Diagnostic Yield Compared with Current Diagnostic Testing for Inherited Retinal Disease. Ophthalmology 123 (5): 1143–1150. https://doi.org/10.1016/j.ophtha.2016.01.009
24. Alberts B, Bray D, Hopkin K, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2013) Essential Cell Biology. Taylor & Francis Group: LLC, New York, USA. https://cdn.bc-pf.org/resources/biology/Molecular_biology/Alberts-Essential_Cell_Biology_4th_Edition.pdf
25. Bush WS, Moore JH (2012) Chapter 11: Genome-Wide Association Studies. PLoS Computational Biology, 8, Article ID: e1002822. https://doi.org/10.1371/journal.pcbi.1002822
26. Clarke GM, Anderson CA, Pettersson FH, Cardon LR, Morris AP, Zondervan KT (2011) Basic Statistical Analysis in Genetic Case-Control Studies. Nature Protocols, 6: 121-133. https://doi.org/10.1038/nprot.2010.182
27. Horita N, Kanek T (2015) Genetic Model Selection for a Case-Control Study and a Meta-Analysis. Meta Gene, 5: 1-8. https://doi.org/10.1016/j.mgene.2015.04.003
28. Setu TJ, Basak T (2021) An Introduction to Basic Statistical Models in Genetics. Open Journal of Statistics 11: 1017-1025. https://doi.org/10.4236/ojs.2021.116060
29. Moore JH, Hahn LW, Ritchie MD, Thornton TA, White BC (2004) Routine Discovery of Complex Genetic Models using Genetic Algorithms. Applied Soft Computing, 4: 79-86. https://doi.org/10.1016/j.asoc.2003.08.003
30. Cooper DN, Krawczak M, Polychronakos C, Tyler-Smith C, Kehrer-Sawatzk H (2013) Where Genotype Is Not Predictive of Phenotype: Towards an Understanding of the Molecular Basis of Reduced Penetrance in Human Inherited Disease. Human Genetics, 132: 1077-1130. https://doi.org/10.1007/s00439-013-1331-2
31. Ford D, Easton DF, Stratton M, Narod S, Goldgar D, Devilee P, Bishop DT, Weber B, Lenoir G, Chang-Claude J, Sobol H, Teare MD, Struewing J, Arason A, Scherneck S, Peto J, Rebbeck TR, Tonin P, Neuhausen S, Barkardottir R, Eyfjord J, Lynch H, Ponder BAJ, Gayther SA, Birch JM, Lindblom A, Stoppa-Lyonnet D, Bignon Y, Borg A, Hamann U, Haites N, Scott RJ, Maugard CM, Vasen H, Seitz S, Cannon-Albright LA, Schofield A, Zelada-Hedman M, The Breast Cancer Linkage Consortium (1998) Genetic Heterogeneity and Penetrance Analysis of the BRCA1 and BRCA2 Genes in Breast Cancer Families. American Journal of Human Genetics, 62: 676-689. https://doi.org/10.1086/301749
32. Pérez-Rodríguez P, Campos G (2022) Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package. Genetics, 222 (1): iyac112. https://doi.org/10.1093/genetics/iyac112