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Alberto Piazza

Other affiliations: University of Verona, Kettering University, Stanford University  ...read more
Bio: Alberto Piazza is an academic researcher from University of Turin. The author has contributed to research in topics: Population & Haplotype. The author has an hindex of 42, co-authored 134 publications receiving 16872 citations. Previous affiliations of Alberto Piazza include University of Verona & Kettering University.


Papers
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Book
01 Jan 1994
TL;DR: The author examines the history of human evolution in Africa, Europe, and Asia through the lens of genetic, archaeological, and linguistic information.
Abstract: The collaboration between the Forensic Community and the scholars of human population genetics, among whom I place myself, has always been fruitful and of reciprocal benefit in Italy as much as in Europe and in North-America, in the latter with a more dialectical attitude as shown by recent rather hot debates. DNA analysis is today offering new possibilities of collaboration. Case work and search for reference populations complement in the daily activity of the forensic scholars. Substantial DNA databases have already been established for a number of population groups, but the development of new standards and new reference databases is likely in the immediate future following the implementation of PCR-based DNA typing systems, and a strong argument can be made for population geneticists to share protocols and markers with the forensic community in order to type well-defined reference populations and from them to contribute to the analysis of human genetic diversity. My talk, however, does not address to the future, but rather to the past: I have had the chance to analyze, over the past ten years, many genetic data from human populations and I am going to give a short review of our recent analyses. Our main interest lies in their interpretation in terms of prehistory and history of our species: a more comprehensive treatment will appear in a forthcoming book written in collaboration with Cavalli-Sforza and Menozzi [1].

2,570 citations

Journal ArticleDOI
Sekar Kathiresan1, Benjamin F. Voight1, Shaun Purcell2, Kiran Musunuru1, Diego Ardissino, Pier Mannuccio Mannucci3, Sonia S. Anand4, James C. Engert5, Nilesh J. Samani6, Heribert Schunkert7, Jeanette Erdmann7, Muredach P. Reilly8, Daniel J. Rader8, Thomas M. Morgan9, John A. Spertus10, Monika Stoll11, Domenico Girelli12, Pascal P. McKeown13, Christopher Patterson13, David S. Siscovick14, Christopher J. O'Donnell15, Roberto Elosua, Leena Peltonen16, Veikko Salomaa17, Stephen M. Schwartz14, Olle Melander18, David Altshuler1, Pier Angelica Merlini, Carlo Berzuini19, Luisa Bernardinelli19, Flora Peyvandi3, Marco Tubaro, Patrizia Celli, Maurizio Ferrario, Raffaela Fetiveau, Nicola Marziliano, Giorgio Casari20, Michele Galli, Flavio Ribichini12, Marco Rossi, Francesco Bernardi21, Pietro Zonzin, Alberto Piazza22, Jean Yee14, Yechiel Friedlander23, Jaume Marrugat, Gavin Lucas, Isaac Subirana, Joan Sala24, Rafael Ramos, James B. Meigs1, Gordon H. Williams1, David M. Nathan1, Calum A. MacRae1, Aki S. Havulinna17, Göran Berglund18, Joel N. Hirschhorn1, Rosanna Asselta, Stefano Duga, Marta Spreafico25, Mark J. Daly1, James Nemesh2, Joshua M. Korn1, Steven A. McCarroll1, Aarti Surti2, Candace Guiducci2, Lauren Gianniny2, Daniel B. Mirel2, Melissa Parkin2, Noël P. Burtt2, Stacey Gabriel2, John R. Thompson6, Peter S. Braund6, Benjamin J. Wright6, Anthony J. Balmforth26, Stephen G. Ball26, Alistair S. Hall26, Patrick Linsel-Nitschke7, Wolfgang Lieb7, Andreas Ziegler7, Inke R. König7, Christian Hengstenberg27, Marcus Fischer27, Klaus Stark27, Anika Grosshennig7, Michael Preuss7, H-Erich Wichmann28, Stefan Schreiber29, Willem H. Ouwehand19, Panos Deloukas30, Michael Scholz, François Cambien31, Mingyao Li8, Zhen Chen8, Robert L. Wilensky8, William H. Matthai8, Atif Qasim8, Hakon Hakonarson8, Joe Devaney32, Mary-Susan Burnett32, Augusto D. Pichard32, Kenneth M. Kent32, Lowell F. Satler32, Joseph M. Lindsay32, Ron Waksman32, Stephen E. Epstein32, Thomas Scheffold, Klaus Berger11, Andreas Huge11, Nicola Martinelli12, Oliviero Olivieri12, Roberto Corrocher12, Hilma Holm33, Gudmar Thorleifsson33, Unnur Thorsteinsdottir34, Kari Stefansson34, Ron Do5, Changchun Xie4, David S. Siscovick14 
TL;DR: SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with my Cardiovascular Infarction risk.
Abstract: We conducted a genome-wide association study testing single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for association with early-onset myocardial infarction in 2,967 cases and 3,075 controls We carried out replication in an independent sample with an effective sample size of up to 19,492 SNPs at nine loci reached genome-wide significance: three are newly identified (21q22 near MRPS6-SLC5A3-KCNE2, 6p24 in PHACTR1 and 2q33 in WDR12) and six replicated prior observations1, 2, 3, 4 (9p21, 1p13 near CELSR2-PSRC1-SORT1, 10q11 near CXCL12, 1q41 in MIA3, 19p13 near LDLR and 1p32 near PCSK9) We tested 554 common copy number polymorphisms (>1% allele frequency) and none met the pre-specified threshold for replication (P < 10-3) We identified 8,065 rare CNVs but did not detect a greater CNV burden in cases compared to controls, in genes compared to the genome as a whole, or at any individual locus SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with myocardial infarction risk

1,092 citations

Journal ArticleDOI
12 Apr 2002-Science
TL;DR: A resource of 1064 cultured lymphoblastoid cell lines from individuals in different world populations and corresponding milligram quantities of DNA is deposited at the Foundation Jean Dausset (CEPH) in Paris.
Abstract: A resource of 1064 cultured lymphoblastoid cell lines (LCLs) ([1][1]) from individuals in different world populations and corresponding milligram quantities of DNA is deposited at the Foundation Jean Dausset (CEPH) ([2][2]) in Paris. LCLs were collected from various laboratories by the Human Genome

1,002 citations

Journal ArticleDOI
TL;DR: Binary polymorphisms associated with the non-recombining region of the human Y chromosome (NRY) preserve the paternal genetic legacy of the authors' species that has persisted to the present, permitting inference of human evolution, population affinity and demographic history.
Abstract: Binary polymorphisms associated with the non-recombining region of the human Y chromosome (NRY) preserve the paternal genetic legacy of our species that has persisted to the present, permitting inference of human evolution, population affinity and demographic history 1 . We used denaturing highperformance liquid chromatography (DHPLC; ref. 2) to identify 160 of the 166 bi-allelic and 1 tri-allelic site that formed a parsimonious genealogy of 116 haplotypes, several of which display distinct population affinities based on the analysis of 1062 globally representative individuals. A minority of contemporary East Africans and Khoisan represent the descendants of the most ancestral patrilineages of anatomically modern humans that left Africa between 35,000 and 89,000 years ago.

959 citations


Cited by
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Journal ArticleDOI
01 Jun 2000-Genetics
TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
Abstract: We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci— e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.

27,454 citations

Journal ArticleDOI
22 Dec 2000-Science
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Abstract: Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

13,652 citations

Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 citations

Journal ArticleDOI
TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Abstract: Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies 1‐8 . Because the effects of stratification vary in proportion to the number of samples 9 , stratification will be an increasing problem in the large-scale association studies of the future, which will analyze thousands of samples in an effort to detect common genetic variants of weak effect. The two prevailing methods for dealing with stratification are genomic control and structured association 9‐14 . Although genomic control and structured association have proven useful in a variety of contexts, they have limitations. Genomic control corrects for stratification by adjusting association statistics at each marker by a uniform overall inflation factor. However, some markers differ in their allele frequencies across ancestral populations more than others. Thus, the uniform adjustment applied by genomic control may be insufficient at markers having unusually strong differentiation across ancestral populations and may be superfluous at markers devoid of such differentiation, leading to a loss in power. Structured association uses a program such as STRUCTURE 15 to assign the samples to discrete subpopulation clusters and then aggregates evidence of association within each cluster. If fractional membership in more than one cluster is allowed, the method cannot currently be applied to genome-wide association studies because of its intensive computational cost on large data sets. Furthermore, assignments of individuals to clusters are highly sensitive to the number of clusters, which is not well defined 14,16 .

9,387 citations

Journal ArticleDOI
Paul Burton1, David Clayton2, Lon R. Cardon, Nicholas John Craddock3  +192 moreInstitutions (4)
07 Jun 2007-Nature
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

9,244 citations