In the name of the father: surnames and genetics
TL;DR: Recent studies involving Y-chromosomal haplotyping and surname analysis are promising and indicate that genealogists of the future could be turning to records written in DNA, as well as in paper archives, to solve their problems.
About: This article is published in Trends in Genetics.The article was published on 2001-06-01. It has received 191 citations till now. The article focuses on the topics: Patronymic surname.
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Posted Content•
08 May 2014
TL;DR: In this paper, the authors highlight the significance of genomic data and the threats for genomic privacy and present the high level descriptions of the proposed solutions to protect the privacy of the genomic data.
Abstract: With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer's or Parkinson's), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person's genome is correlated to his family members' genomes, thus leading to interdependent privacy risks. This short tutorial will help computer scientists better understand the privacy and security challenges in today's genomic era. We will first highlight the significance of genomic data and the threats for genomic privacy. Then, we will present the high level descriptions of the proposed solutions to protect the privacy of genomic data and we will discuss future research directions. No prerequisite knowledge on biology or genomics is required for the attendees of this proposal. We only require the attendees to have a slight background on cryptography and statistics.
25 citations
TL;DR: The SMF generally increased due to less mismatches when encountering deep Y-subhaplogroups, less frequently occurring surnames, and small geographical distances between relatives, which enabled the design of a surname prediction model based on genetic and geographical distances of a kinship.
Abstract: The Y-chromosome is a widely studied and useful small part of the genome providing different applications for interdisciplinary research. In many (Western) societies, the Y-chromosome and surnames are paternally co-inherited, suggesting a corresponding Y-haplotype for every namesake. While it has already been observed that this correlation may be disrupted by a false-paternity event, adoption, anonymous sperm donor or the co-founding of surnames, extensive information on the strength of the surname match frequency (SMF) with the Y-chromosome remains rather unknown. For the first time in Belgium and the Netherlands, we were able to study this correlation using 2,401 males genotyped for 46 Y-STRs and 183 Y-SNPs. The SMF was observed to be dependent on the number of Y-STRs analyzed, their mutation rates and the number of Y-STR differences allowed for a kinship. For a perfect match, the Yfiler® Plus and our in-house YForGen kit gave a similar high SMF of 98%, but for non-perfect matches, the latter could overall be identified as the best kit. The SMF generally increased due to less mismatches when encountering [1] deep Y-subhaplogroups, [2] less frequently occurring surnames, and [3] small geographical distances between relatives. This novel information enabled the design of a surname prediction model based on genetic and geographical distances of a kinship. The prediction model has an area under the curve (AUC) of 0.9 and is therefore useable for DNA kinship priority listing in estimation applications like forensic familial searching.
24 citations
TL;DR: High-resolution haplogroup and haplotype data will improve the understanding of regional Y-chromosome variation or recent migration routes and will also help to infer haplogroups background or common ancestry.
Abstract: We performed a molecular characterization of Korean Y-chromosomal haplogroups using a combination of Y-chromosomal single nucleotide polymorphisms (Y-SNPs) and Y-chromosomal short tandem repeats (Y-STRs). In a test using DNA samples from 706 Korean males, a total of 19 different haplogroups were identified by 26 Y-SNPs including the newly redefined markers (PK4, KL2, and P164) in haplogroup O. When genotyping the SNPs, phylogenetic nonequivalence was found between SNPs M117 and M133, which define haplogroup O3a3c1 (O3a2c1a according to the updated tree of haplogroup O by Yan et al. (European Journal of Human Genetics 19:1013–1015, 2011)), suggesting that the position of the M133 marker should be corrected. We have shown that the haplotypes consisted of DYS392, DYS393, DYS437, DYS438, DYS448, and DYS388 loci, which exhibit a relatively lower mutation rate, can preserve phylogenetic information and hence can be used to roughly distinguish Y-chromosome haplogroups, whereas more rapidly mutating Y-STRs such as DYS449 and DYS458 are useful for differentiating male lineages. However, at the relatively rapidly mutating DYS447, DYS449, DYS458, and DYS464 loci, unusually short alleles and intermediate alleles with common sequence structures are informative for elucidating the substructure within the context of a particular haplogroup. In addition, some deletion mutations in the DYS385 flanking region and the null allele at DYS448 were associated with a single haplogroup background. These high-resolution haplogroup and haplotype data will improve our understanding of regional Y-chromosome variation or recent migration routes and will also help to infer haplogroup background or common ancestry.
24 citations
TL;DR: The results suggest that surnames may provide a simple means to stratify, and thereby to render more efficient, Y-chromosomal analyses of Central Europeans that target more ancient events.
Abstract: In human populations, the correct historical interpretation of a genetic structure is often hampered by an almost inherent inability to differentiate between ancient and more recent influences upon extant gene pools. One method to trace recent population movements is the analysis of surnames, which, at least in Central Europe, can be thought of as traits ‘linked’ to the Y chromosome. Illegitimacy, extramarital birth and changes of surnames may have substantially obscured this linkage. In order to assess the actual extent of correlation between surnames and Y-chromosomal haplotypes in Central Europe, we typed Y-chromosomal short tandem repeat markers in 419 German males from Halle. These individuals were subdivided into three groups according to the origin of their respective surname, namely German (G), Slavic (S) or ‘Mixed’ (M). The distribution of the haplotypes was compared by Analysis of Molecular Variance. While the M group was indistinguishable from group G (ΦST=−0.0008, P>0.5), a highly significant difference (ΦST=0.0277, P<0.001) was observed between the S group and the combined G+M group. This surprisingly strong differentiation is comparable to that of European populations of much larger geographic and linguistic difference. In view of the major migration from Slavic countries into Germany in the 19th century, it appears likely that the observed concurrence of Slavic surnames and Y chromosomes is of a recent rather than an early origin. Our results suggest that surnames may provide a simple means to stratify, and thereby to render more efficient, Y-chromosomal analyses of Central Europeans that target more ancient events.
24 citations
Dissertation•
31 Oct 2007
TL;DR: The authors developed an alternative ontology of ethnicity, using personal names to ascribe population ethnicity, at very fine geographical levels, and using a very detailed typology of ethnic groups optimised for the UK population.
Abstract: Understanding of the nature and detailed composition of ethnic groups remains key to a vast swathe of social science and human natural science. Yet ethnic origin is not easy to define, much less measure, and ascribing ethnic origins is one of the most contested and unstable research concepts of the last decade - not only in the social sciences, but also in human biology and medicine. As a result, much research remains hamstrung by the quality and availability of ethnicity classifications, constraining the meaningful subdivision of populations.
This PhD thesis develops an alternative ontology of ethnicity, using personal names to ascribe population ethnicity, at very fine geographical levels, and using a very detailed typology of ethnic groups optimised for the UK population. The outcome is an improved methodology for classifying population registers, as well as small areas, into cultural, ethnic and linguistic groups (CEL). This in turn makes possible the creation of much more detailed, frequently updatable representations of the ethnic kaleidoscope of UK cities, and can be further applied to other countries.
The thesis includes a review of the literature on ethnicity measurement and name analysis, and their applications in ethnic inequalities and geographical research. It presents the development of the new name to ethnicity classification methodology using both a heuristic and an automated and integrated approach. It is based on the UK Electoral Register as well as several health registers in London. Furthermore, a validation of the proposed name-based classification using different datasets is offered, as well as examples of applications in profiling neighbourhoods by ethnicity, in particular the measurement of residential segregation in London. The main study area is London, UK.
23 citations
References
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TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
22,269 citations
TL;DR: Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems are indicated.
Abstract: A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.
12,098 citations
TL;DR: A method for constructing networks from recombination-free population data that combines features of Kruskal's algorithm for finding minimum spanning trees by favoring short connections, and Farris's maximum-parsimony (MP) heuristic algorithm, which sequentially adds new vertices called "median vectors", except that the MJ method does not resolve ties.
Abstract: Reconstructing phylogenies from intraspecific data (such as human mitochondrial DNA variation) is often a challenging task because of large sample sizes and small genetic distances between individuals. The resulting multitude of plausible trees is best expressed by a network which displays alternative potential evolutionary paths in the form of cycles. We present a method ("median joining" [MJ]) for constructing networks from recombination-free population data that combines features of Kruskal's algorithm for finding minimum spanning trees by favoring short connections, and Farris's maximum-parsimony (MP) heuristic algorithm, which sequentially adds new vertices called "median vectors", except that our MJ method does not resolve ties. The MJ method is hence closely related to the earlier approach of Foulds, Hendy, and Penny for estimating MP trees but can be adjusted to the level of homoplasy by setting a parameter epsilon. Unlike our earlier reduced median (RM) network method, MJ is applicable to multistate characters (e.g., amino acid sequences). An additional feature is the speed of the implemented algorithm: a sample of 800 worldwide mtDNA hypervariable segment I sequences requires less than 3 h on a Pentium 120 PC. The MJ method is demonstrated on a Tibetan mitochondrial DNA RFLP data set.
9,937 citations
TL;DR: A new algorithm for finding tandem repeats which works without the need to specify either the pattern or pattern size is presented and its ability to detect tandem repeats that have undergone extensive mutational change is demonstrated.
Abstract: A tandem repeat in DNA is two or more contiguous, approximate copies of a pattern of nucleotides. Tandem repeats have been shown to cause human disease, may play a variety of regulatory and evolutionary roles and are important laboratory and analytic tools. Extensive knowledge about pattern size, copy number, mutational history, etc. for tandem repeats has been limited by the inability to easily detect them in genomic sequence data. In this paper, we present a new algorithm for finding tandem repeats which works without the need to specify either the pattern or pattern size. We model tandem repeats by percent identity and frequency of indels between adjacent pattern copies and use statistically based recognition criteria. We demonstrate the algorithm’s speed and its ability to detect tandem repeats that have undergone extensive mutational change by analyzing four sequences: the human frataxin gene, the human β T cell receptor locus sequence and two yeast chromosomes. These sequences range in size from 3 kb up to 700 kb. A World Wide Web server interface at c3.biomath.mssm.edu/trf.html has been established for automated use of the program.
6,577 citations
TL;DR: This book aims to provide a history of Chinese modern art from 17th Century to the present day through the lens of 20th Century critics, practitioners, journalists, and mediaeval and modern-day critics.
Abstract: J. Craig Venter,* Mark D. Adams, Eugene W. Myers, Peter W. Li, Richard J. Mural, Granger G. Sutton, Hamilton O. Smith, Mark Yandell, Cheryl A. Evans, Robert A. Holt, Jeannine D. Gocayne, Peter Amanatides, Richard M. Ballew, Daniel H. Huson, Jennifer Russo Wortman, Qing Zhang, Chinnappa D. Kodira, Xiangqun H. Zheng, Lin Chen, Marian Skupski, Gangadharan Subramanian, Paul D. Thomas, Jinghui Zhang, George L. Gabor Miklos, Catherine Nelson, Samuel Broder, Andrew G. Clark, Joe Nadeau, Victor A. McKusick, Norton Zinder, Arnold J. Levine, Richard J. Roberts, Mel Simon, Carolyn Slayman, Michael Hunkapiller, Randall Bolanos, Arthur Delcher, Ian Dew, Daniel Fasulo, Michael Flanigan, Liliana Florea, Aaron Halpern, Sridhar Hannenhalli, Saul Kravitz, Samuel Levy, Clark Mobarry, Knut Reinert, Karin Remington, Jane Abu-Threideh, Ellen Beasley, Kendra Biddick, Vivien Bonazzi, Rhonda Brandon, Michele Cargill, Ishwar Chandramouliswaran, Rosane Charlab, Kabir Chaturvedi, Zuoming Deng, Valentina Di Francesco, Patrick Dunn, Karen Eilbeck, Carlos Evangelista, Andrei E. Gabrielian, Weiniu Gan, Wangmao Ge, Fangcheng Gong, Zhiping Gu, Ping Guan, Thomas J. Heiman, Maureen E. Higgins, Rui-Ru Ji, Zhaoxi Ke, Karen A. Ketchum, Zhongwu Lai, Yiding Lei, Zhenya Li, Jiayin Li, Yong Liang, Xiaoying Lin, Fu Lu, Gennady V. Merkulov, Natalia Milshina, Helen M. Moore, Ashwinikumar K Naik, Vaibhav A. Narayan, Beena Neelam, Deborah Nusskern, Douglas B. Rusch, Steven Salzberg, Wei Shao, Bixiong Shue, Jingtao Sun, Zhen Yuan Wang, Aihui Wang, Xin Wang, Jian Wang, Ming-Hui Wei, Ron Wides, Chunlin Xiao, Chunhua Yan, Alison Yao, Jane Ye, Ming Zhan, Weiqing Zhang, Hongyu Zhang, Qi Zhao, Liansheng Zheng, Fei Zhong, Wenyan Zhong, Shiaoping C. Zhu, Shaying Zhao, Dennis Gilbert, Suzanna Baumhueter, Gene Spier, Christine Carter, Anibal Cravchik, Trevor Woodage, Feroze Ali, Huijin An, Aderonke Awe, Danita Baldwin, Holly Baden, Mary Barnstead, Ian Barrow, Karen Beeson, Dana Busam, Amy Carver, Angela Center, Ming Lai Cheng, Liz Curry, Steve Danaher, Lionel Davenport, Raymond Desilets, Susanne Dietz, Kristina Dodson, Lisa Doup, Steven Ferriera, Neha Garg, Andres Gluecksmann, Brit Hart, Jason Haynes, Charles Haynes, Cheryl Heiner, Suzanne Hladun, Damon Hostin, Jarrett Houck, Timothy Howland, Chinyere Ibegwam, Jeffery Johnson, Francis Kalush, Lesley Kline, Shashi Koduru, Amy Love, Felecia Mann, David May, Steven McCawley, Tina McIntosh, Ivy McMullen, Mee Moy, Linda Moy, Brian Murphy, Keith Nelson, Cynthia Pfannkoch, Eric Pratts, Vinita Puri, Hina Qureshi, Matthew Reardon, Robert Rodriguez, Yu-Hui Rogers, Deanna Romblad, Bob Ruhfel, Richard Scott, Cynthia Sitter, Michelle Smallwood, Erin Stewart, Renee Strong, Ellen Suh, Reginald Thomas, Ni Ni Tint, Sukyee Tse, Claire Vech, Gary Wang, Jeremy Wetter, Sherita Williams, Monica Williams, Sandra Windsor, Emily Winn-Deen, Keriellen Wolfe, Jayshree Zaveri, Karena Zaveri, Josep F. Abril, Roderic Guigó, Michael J. Campbell, Kimmen V. Sjolander, Brian Karlak, Anish Kejariwal, Huaiyu Mi, Betty Lazareva, Thomas Hatton, Apurva Narechania, Karen Diemer, Anushya Muruganujan, Nan Guo, Shinji Sato, Vineet Bafna, Sorin Istrail, Ross Lippert, Russell Schwartz, Brian Walenz, Shibu Yooseph, David Allen, Anand Basu, James Baxendale, Louis Blick, Marcelo Caminha, John Carnes-Stine, Parris Caulk, Yen-Hui Chiang, My Coyne, Carl Dahlke, Anne Deslattes Mays, Maria Dombroski, Michael Donnelly, Dale Ely, Shiva Esparham, Carl Fosler, Harold Gire, Stephen Glanowski, Kenneth Glasser, Anna Glodek, Mark Gorokhov, Ken Graham, Barry Gropman, Michael Harris, Jeremy Heil, Scott Henderson, Jeffrey Hoover, Donald Jennings, Catherine Jordan, James Jordan, John Kasha, Leonid Kagan, Cheryl Kraft, Alexander Levitsky, Mark Lewis, Xiangjun Liu, John Lopez, Daniel Ma, William Majoros, Joe McDaniel, Sean Murphy, Matthew Newman, Trung Nguyen, Ngoc Nguyen, Marc Nodell, Sue Pan, Jim Peck, Marshall Peterson, William Rowe, Robert Sanders, John Scott, Michael Simpson, Thomas Smith, Arlan Sprague, Timothy Stockwell, Russell Turner, Eli Venter, Mei Wang, Meiyuan Wen, David Wu, Mitchell Wu, Ashley Xia, Ali Zandieh, Xiaohong Zhu T H E H U M A N G E N O M E
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