FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2033973364> ?p ?o ?g. }

• W2033973364 endingPage "1023" @default.
• W2033973364 startingPage "1014" @default.
• W2033973364 abstract "For admixture mapping studies in Mexican Americans (MAM), we define a genomewide single-nucleotide–polymorphism (SNP) panel that can distinguish between chromosomal segments of Amerindian (AMI) or European (EUR) ancestry. These studies used genotypes for >400,000 SNPs, defined in EUR and both Pima and Mayan AMI, to define a set of ancestry-informative markers (AIMs). The use of two AMI populations was necessary to remove a subset of SNPs that distinguished genotypes of only one AMI subgroup from EUR genotypes. The AIMs set contained 8,144 SNPs separated by a minimum of 50 kb with only three intermarker intervals >1 Mb and had EUR/AMI FST values >0.30 (mean FST=0.48) and Mayan/Pima FST values <0.05 (mean FST<0.01). Analysis of a subset of these SNP AIMs suggested that this panel may also distinguish ancestry between EUR and other disparate AMI groups, including Quechuan from South America. We show, using realistic simulation parameters that are based on our analyses of MAM genotyping results, that this panel of SNP AIMs provides good power for detecting disease-associated chromosomal segments for genes with modest ethnicity risk ratios. A reduced set of 5,287 SNP AIMs captured almost the same admixture mapping information, but smaller SNP sets showed substantial drop-off in admixture mapping information and power. The results will enable studies of type 2 diabetes, rheumatoid arthritis, and other diseases among which epidemiological studies suggest differences in the distribution of ancestry-associated susceptibility. For admixture mapping studies in Mexican Americans (MAM), we define a genomewide single-nucleotide–polymorphism (SNP) panel that can distinguish between chromosomal segments of Amerindian (AMI) or European (EUR) ancestry. These studies used genotypes for >400,000 SNPs, defined in EUR and both Pima and Mayan AMI, to define a set of ancestry-informative markers (AIMs). The use of two AMI populations was necessary to remove a subset of SNPs that distinguished genotypes of only one AMI subgroup from EUR genotypes. The AIMs set contained 8,144 SNPs separated by a minimum of 50 kb with only three intermarker intervals >1 Mb and had EUR/AMI FST values >0.30 (mean FST=0.48) and Mayan/Pima FST values <0.05 (mean FST<0.01). Analysis of a subset of these SNP AIMs suggested that this panel may also distinguish ancestry between EUR and other disparate AMI groups, including Quechuan from South America. We show, using realistic simulation parameters that are based on our analyses of MAM genotyping results, that this panel of SNP AIMs provides good power for detecting disease-associated chromosomal segments for genes with modest ethnicity risk ratios. A reduced set of 5,287 SNP AIMs captured almost the same admixture mapping information, but smaller SNP sets showed substantial drop-off in admixture mapping information and power. The results will enable studies of type 2 diabetes, rheumatoid arthritis, and other diseases among which epidemiological studies suggest differences in the distribution of ancestry-associated susceptibility. Admixture mapping is a promising method for identifying chromosomal regions containing ancestry-linked traits when the distribution of the susceptibility genes is different among the founding populations.1Hoggart CJ Shriver MD Kittles RA Clayton DG McKeigue PM Design and analysis of admixture mapping studies.Am J Hum Genet. 2004; 74: 965-978Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar, 2Patterson N Hattangadi N Lane B Lohmueller KE Hafler DA Oksenberg JR Hauser SL Smith MW O’Brien SJ Altshuler D et al.Methods for high-density admixture mapping of disease genes.Am J Hum Genet. 2004; 74: 979-1000Abstract Full Text Full Text PDF PubMed Scopus (376) Google Scholar, 3Zhu X Cooper RS Elston RC Linkage analysis of a complex disease through use of admixed populations.Am J Hum Genet. 2004; 74: 1136-1153Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar, 4Zhang C Chen K Seldin MF Li H A hidden Markov modeling approach for admixture mapping based on case-control data.Genet Epidemiol. 2004; 27: 225-239Crossref PubMed Scopus (24) Google Scholar Recent admixture mapping studies of African Americans (AFA) provide strong evidence of susceptibility regions for multiple sclerosis and prostate cancer associated with ancestry.5Reich D Patterson N De Jager PL McDonald GJ Waliszewska A Tandon A Lincoln RR DeLoa C Fruhan SA Cabre P et al.A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility.Nat Genet. 2005; 37: 1113-1118Crossref PubMed Scopus (222) Google Scholar, 6Freedman ML Haiman CA Patterson N McDonald GJ Tandon A Waliszewska A Penney K Steen RG Ardlie K John EM et al.Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men.Proc Natl Acad Sci USA. 2006; 103: 14068-14073Crossref PubMed Scopus (476) Google Scholar These investigations have underscored the potential value of applying this approach to diverse admixed populations and multiple common diseases. In particular, there is substantial interest in applying this method toward studies of type 2 diabetes mellitus (MIM #125853) and its complications7Adler SG Pahl M Seldin MF Deciphering diabetic nephropathy: progress using genetic strategies.Curr Opin Nephrol Hypertens. 2000; 9: 99-106Crossref PubMed Scopus (16) Google Scholar, 8Knowler WC Coresh J Elston RC Freedman BI Iyengar SK Kimmel PL Olson JM Plaetke R Sedor JR Seldin MF The Family Investigation of Nephropathy and Diabetes (FIND): design and methods.J Diabetes Complications. 2005; 19: 1-9PubMed Google Scholar and autoimmune diseases, including rheumatoid arthritis (MIM #180300), in Amerindian (AMI) admixed populations. Epidemiological studies indicate that AMI populations have unusually high prevalences of these diseases compared with European populations (EUR).9Del Puente A Knowler WC Pettitt DJ Bennett PH High incidence and prevalence of rheumatoid arthritis in Pima Indians.Am J Epidemiol. 1989; 129: 1170-1178PubMed Google Scholar, 10Harvey J Lotze M Stevens MB Lambert G Jacobson D Rheumatoid arthritis in a Chippewa band. I. Pilot screening study of disease prevalence.Arthritis Rheum. 1981; 24: 717-721Crossref PubMed Scopus (54) Google Scholar, 11Knowler WC Williams RC Pettitt DJ Steinberg AG Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture.Am J Hum Genet. 1988; 43: 520-526PubMed Google Scholar The very large populations of Mexican Americans (MAM) and Mestizo Mexicans with large variance in AMI and EUR12Yang N Li H Criswell LA Gregersen PK Alarcon-Riquelme ME Kittles R Shigeta R Silva G Patel PI Belmont JW et al.Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine.Hum Genet. 2005; 118: 382-392Crossref PubMed Scopus (123) Google Scholar contributions suggests that admixture mapping methods may be particularly useful for genetic analysis of these common complex diseases with high morbidity. The current study emerged from the need to develop these markers for the Family Investigation of Nephropathy and Diabetes (FIND), as described elsewhere.8Knowler WC Coresh J Elston RC Freedman BI Iyengar SK Kimmel PL Olson JM Plaetke R Sedor JR Seldin MF The Family Investigation of Nephropathy and Diabetes (FIND): design and methods.J Diabetes Complications. 2005; 19: 1-9PubMed Google Scholar In contrast with studies of African and EUR admixed populations, the application of admixture mapping in MAM populations has been limited by the relatively small number of markers that have been identified that distinguish between AMI and EUR populations. Although several hundred markers identified elsewhere have allowed analysis of the population-genetics structures of AMI admixed populations,12Yang N Li H Criswell LA Gregersen PK Alarcon-Riquelme ME Kittles R Shigeta R Silva G Patel PI Belmont JW et al.Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine.Hum Genet. 2005; 118: 382-392Crossref PubMed Scopus (123) Google Scholar, 13Collins-Schramm HE Chima B Morii T Wah K Figueroa Y Criswell LA Hanson RL Knowler WC Silva G Belmont JW et al.Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians.Hum Genet. 2004; 114: 263-271Crossref PubMed Scopus (92) Google Scholar, 14Smith MW Lautenberger JA Shin HD Chretien J-P Shrestha S Gilbert DA O’Brien SJ Markers for mapping by admixture linkage disequilibrium in African American and Hispanic populations.Am J Hum Genet. 2001; 69: 1080-1094Abstract Full Text Full Text PDF PubMed Scopus (120) Google Scholar, 15Smith MW Patterson N Lautenberger JA Truelove AL McDonald GJ Waliszewska A Kessing BD Malasky MJ Scafe C Le E et al.A high-density admixture map for disease gene discovery in African Americans.Am J Hum Genet. 2004; 74: 1001-1013Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar, 16Conrad DF Jakobsson M Coop G Wen X Wall JD Rosenberg NA Pritchard JK A worldwide survey of haplotype variation and linkage disequilibrium in the human genome.Nat Genet. 2006; 38: 1251-1260Crossref PubMed Scopus (366) Google Scholar, 17Salzano FM Callegari-Jacques SM Amerindian and nonAmerindian autosome molecular variability—a test analysis.Genetica. 2006; 126: 237-242Crossref PubMed Scopus (5) Google Scholar admixture mapping requires several thousand ancestry-informative markers (AIMs) for genomewide definition of chromosomal segments. The number of AIMs necessary for admixture mapping is, in part, a function of the number of generations since admixture in the study population. Ascertainment of these admixture characteristics for MAM and other AMI admixed populations has likewise been hampered by the relative paucity of AIMs. Large numbers of AIMs are necessary to estimate this parameter from ancestry definition of chromosomal segments—that is, identification of ancestry recombination events that have occurred along each chromosome. The current study addresses the need for AIMs that are useful for admixture mapping in MAM and examines the parameters necessary for studying this population. A potential problem in defining AIMs and applying admixture mapping is the inability to study the actual parental populations that contributed to the current admixed populations. Although previous studies have suggested that the differences in allele frequencies within different continental populations13Collins-Schramm HE Chima B Morii T Wah K Figueroa Y Criswell LA Hanson RL Knowler WC Silva G Belmont JW et al.Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians.Hum Genet. 2004; 114: 263-271Crossref PubMed Scopus (92) Google Scholar, 15Smith MW Patterson N Lautenberger JA Truelove AL McDonald GJ Waliszewska A Kessing BD Malasky MJ Scafe C Le E et al.A high-density admixture map for disease gene discovery in African Americans.Am J Hum Genet. 2004; 74: 1001-1013Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar, 18Collins-Schramm HE Kittles RA Operario DJ Weber JL Criswell LA Cooper RS Seldin MF Markers that discriminate between European and African ancestry show limited variation within Africa.Hum Genet. 2002; 111: 566-569Crossref PubMed Scopus (41) Google Scholar, 19Tian C Hinds DA Shigeta R Kittles R Ballinger DG Seldin MF A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping.Am J Hum Genet. 2006; 79: 640-649Abstract Full Text Full Text PDF PubMed Scopus (141) Google Scholar, 20Barbujani G Magagni A Minch E Cavalli-Sforza LL An apportionment of human DNA diversity.Proc Natl Acad Sci USA. 1997; 94: 4516-4519Crossref PubMed Scopus (340) Google Scholar is relatively small compared with differences between continental populations, this issue remains a concern. In the current study, we have used Pima Indians, a northern Uto-Aztecan AMI group, as our initial representative of the AMI contribution to MAM. Importantly, we have also examined a second disparate AMI group, Mayan (a group that does not speak a Uto-Aztecan language), in our assessment of marker and population characteristics. Another problem is admixture within the presumptive parental population, a factor that deserves special consideration in indigenous AMI populations in the Americas, where many AMI groups may have a history of substantial EUR gene flow. This issue was addressed both by our careful selection of participants and by screening those subjects with small numbers of EUR/AMI/AFR AIMs, to identify and exclude clear population outliers from these studies (see the “Methods” section). Recently, we reported a set of AIMs that provide extensive genomewide coverage for admixture mapping in AFA and that took advantage of HapMap genotyping results, including genotyping data from ∼3.5 million SNPs.19Tian C Hinds DA Shigeta R Kittles R Ballinger DG Seldin MF A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping.Am J Hum Genet. 2006; 79: 640-649Abstract Full Text Full Text PDF PubMed Scopus (141) Google Scholar This strategy could not be used in the present study, because large-scale analysis of SNP variations in AMI populations had not yet been performed. In the present work, we used two different strategies to screen and partially validate a set of AIMs for MAM admixture mapping: (1) a screen of the Illumina 100K gene-rich and 317K HapMap SNP–enriched SNP arrays and (2) a set of ∼20,000 SNPs selected for informativeness between East Asian and EUR populations. The latter strategy was suggested by our previous studies13Collins-Schramm HE Chima B Morii T Wah K Figueroa Y Criswell LA Hanson RL Knowler WC Silva G Belmont JW et al.Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians.Hum Genet. 2004; 114: 263-271Crossref PubMed Scopus (92) Google Scholar in which a 10-fold enrichment in the frequency of EUR/AMI SNP AIMs was achieved by selecting SNPs with East Asian/EUR FST>0.30, compared with random SNPs. Together, these strategies identified large numbers of SNP AIMs and provide a strong basis for admixture mapping studies in MAM. DNA samples or genotyping results from 230 European Americans (EURNY), 274 EURs (EURNIHLN), 60 CEPH EURs (CEU), 72 Pima AMIs, 29 Mayan AMIs, 48 Quechuan AMIs, 24 Nahua AMIs, 24 MAM, and 90 East Asians (Japanese from Tokyo [JPT] and Han Chinese from Beijing [CHB]) were included in various aspects of this study. These populations were based on self-identified ethnic affiliation. The EURNY were from New York City; Pima individuals were from Arizona (samples provided by R.L.H. and W.C.K.); Mayan and Quechuan individuals were from Guatemala and Peru, respectively (samples provided by G.S. and J.B.); the Nahua were from central Mexico (samples donated by Dr. David Smith of the University of California–Davis [UC-Davis]), and the MAM were from California. (The admixture characteristics of this Mayan group is very different from the Mayan subjects in the Human Diversity Panel who have been reported to have European admixture.16Conrad DF Jakobsson M Coop G Wen X Wall JD Rosenberg NA Pritchard JK A worldwide survey of haplotype variation and linkage disequilibrium in the human genome.Nat Genet. 2006; 38: 1251-1260Crossref PubMed Scopus (366) Google Scholar) The CEU, JPT, and CHB were the HapMap panel genotypes,21Altshuler D Brooks LD Chakravarti A Collins FS Daly MJ Donnelly P A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4545) Google Scholar and the EURNIHLN genotypes were available from the National Institutes of Health (NIH) Laboratory of Neurogenetics at the Queue at Coriell Web site. All DNA and blood samples were obtained in accordance with protocols and informed-consent procedures approved by institutional review boards and were labeled with an anonymous code number or, in the case of the MAM, in accordance with approved procedures. The studied subjects were all healthy, and they were not first-degree relatives of each other, according to self-report. For the AMI groups, the DNA samples were chosen after initial screening of samples, to exclude individuals with large EUR admixture. This was performed using AIMs and criteria (to remove outliers) as described elsewhere.13Collins-Schramm HE Chima B Morii T Wah K Figueroa Y Criswell LA Hanson RL Knowler WC Silva G Belmont JW et al.Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians.Hum Genet. 2004; 114: 263-271Crossref PubMed Scopus (92) Google Scholar Of AMI samples, <10% were excluded on this basis. MAM were randomly chosen from a previous set of subjects,12Yang N Li H Criswell LA Gregersen PK Alarcon-Riquelme ME Kittles R Shigeta R Silva G Patel PI Belmont JW et al.Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine.Hum Genet. 2005; 118: 382-392Crossref PubMed Scopus (123) Google Scholar with the exclusion of 2 individuals (of 94) with evidence of African contribution >10%. FST was determined using Genetix software, which applies the Weir and Cockerham algorithm.22Weir B Cockerham C Estimating F-statistics for the analysis of population structure.Evolution. 1984; 38: 1358-1370Crossref Google Scholar Hardy-Weinberg equilibrium was examined using an exact test implemented in the FINETTI software, which can be accessed interactively (Institut fur Humangenetik). Population admixture proportions were determined using the Bayesian clustering algorithms developed by Pritchard and implemented in the program STRUCTURE (v. 2.1).23Pritchard JK Stephens M Donnelly P Inference of population structure using multilocus genotype data.Genetics. 2000; 155: 945-959PubMed Google Scholar, 24Falush D Stephens M Pritchard JK Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies.Genetics. 2003; 164: 1567-1587PubMed Google Scholar Individual admixture proportions and the number of generations since admixture were determined using STRUCTURE 2.1 and ADMIXMAP. For STRUCTURE, each analysis was performed without any prior population assignment and was performed at least three times, with similar results, with use of >5,000 replicates and 2,000 burn-in cycles under the admixture model. We used the “infer α” option with a separate α estimated for each population (where α is the Dirichlet parameter for degree of admixture). Runs were performed under the λ=1 option, where λ parameterizes the allele-frequency prior and is based on the Dirichlet distribution of allele frequencies. The log likelihood of each analysis at varying numbers of population groups (k) is also estimated in the STRUCTURE analysis and, as expected, favored two population groups in the MAM. For analyses using different values of k (k=2, 3, 4, 5, or 6), at least 95% of the ancestry in the MAM population was derived from two clusters that corresponded to the AMI and EUR clusters. For ADMIXMAP analysis of the MAM genotypes, 23,000 iterations and 2,000 burn-in cycles were used under the random-mating model. The runs were performed under prior allele-frequency estimation with use of the results of the parental allele–frequency determinations. The number of generations was allowed to vary and thus was determined for each gamete by the Markov chain–Monte Carlo (MCMC) algorithm. Admixture mapping of simulated data sets was performed using ADMIXMAP.1Hoggart CJ Shriver MD Kittles RA Clayton DG McKeigue PM Design and analysis of admixture mapping studies.Am J Hum Genet. 2004; 74: 965-978Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar This program evaluates evidence of ancestry linkage by application of a score test to either case-only or case-control analyses. For case-only analysis, the null hypothesis is that the risk ratio between populations at each locus equals one. For case-control analysis, the null hypothesis is that there is no effect on locus ancestry, compared with individual admixture. For both analyses, the ancestral transitions are derived from application of the MCMC algorithm. The simulations were performed by modification of a program developed elsewhere.4Zhang C Chen K Seldin MF Li H A hidden Markov modeling approach for admixture mapping based on case-control data.Genet Epidemiol. 2004; 27: 225-239Crossref PubMed Scopus (24) Google Scholar Most runs were performed using 2,000 iterations and 400 burn-in cycles. Similar results were obtained using longer runs (23,000 iterations and 2,000 burn-in cycles), and monitoring of ergodic averages showed the sampler had run long enough for the posterior means to have been estimated accurately. A normalized score of 4.0 was found to approximate a conservative genomewide α level (∼0.01), on the basis of large numbers of simulations run under different conditions (i.e., with different ethnicity risk ratios [ERRs] and genomic intervals). Three different genotyping platforms and four different sets of SNPs were used in this study: (1) Illumina array platform and 100K gene set (Illumina), (2) Illumina array platform and 317K SNP set (HumanHap 300 BeadChip), (3) Perlegen Sciences photolithographic array and 20K SNPs selected for EUR/East Asian allele-frequency differences, and (4) TaqMan assays and 40 SNP AIMs. For the Illumina arrays, Perlegen Sciences arrays, and TaqMan assays, the genotyping was performed and genotypes assigned as described elsewhere.12Yang N Li H Criswell LA Gregersen PK Alarcon-Riquelme ME Kittles R Shigeta R Silva G Patel PI Belmont JW et al.Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine.Hum Genet. 2005; 118: 382-392Crossref PubMed Scopus (123) Google Scholar, 25Hinds DA Seymour AB Durham LK Banerjee P Ballinger DG Milos PM Cox DR Thompson JF Frazer KA Application of pooled genotyping to scan candidate regions for association with HDL cholesterol levels.Hum Genomics. 2004; 1: 421-434Crossref PubMed Scopus (54) Google Scholar, 26Gunderson KL Steemers FJ Lee G Mendoza LG Chee MS A genome-wide scalable SNP genotyping assay using microarray technology.Nat Genet. 2005; 37: 549-554Crossref PubMed Scopus (478) Google Scholar, 27Hinds DA Stuve LL Nilsen GB Halperin E Eskin E Ballinger DG Frazer KA Cox DR Whole-genome patterns of common DNA variation in three human populations.Science. 2005; 307: 1072-1079Crossref PubMed Scopus (953) Google Scholar For the third set of SNPs, the SNPs were chosen from the HapMap Project21Altshuler D Brooks LD Chakravarti A Collins FS Daly MJ Donnelly P A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4545) Google Scholar on the basis of both EUR/East Asian FST values and genomic position. This strategy was based on our previous studies showing enrichment for AMI/EUR AIMs by use of this methodology.13Collins-Schramm HE Chima B Morii T Wah K Figueroa Y Criswell LA Hanson RL Knowler WC Silva G Belmont JW et al.Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians.Hum Genet. 2004; 114: 263-271Crossref PubMed Scopus (92) Google Scholar Genotypes from 60 unrelated subjects (parents) from CEU and 90 unrelated East Asian subjects (JPT and CHB HapMap data sets) were available for ∼4 million SNPs in the combined phase 1 and phase 2 HapMap results. Initial examination of these sets identified >300,000 SNPs with an FST>0.25. High FST values favor selection of markers that are closer to fixation in one parental population. With use of the FST measurement, ∼30,000 SNPs were selected from this set of 300,000 SNPs by choosing a maximum of 4 SNPs in 500-kb windows, with a minimum distance of 50 kb between SNPs. Additional SNPs were added in regions with lower informativeness, SNPs were thinned in regions of high informativeness, and SNPs that failed assay-design algorithms for the Perlegen Sciences lithographic array platform were replaced with other informative SNPs, to complete the set of 20,000 SNPs. For all genotyping, SNPs were excluded if the results did not meet either (1) 85% complete genotyping results in each population group or (2) Hardy-Weinberg equilibrium criterion in any of the parental populations (P<.005). These exclusion criteria reduced the total number of SNPs by <2%. As described in the “Results” section, the SNP AIMs were chosen on the basis of three criteria: (1) EUR/combined AMI FST values >0.35, (2) EUR/Pima and EUR/Mayan FST values both >0.3, and (3) Pima/Mayan FST values <0.05. For one MAM admixture mapping panel, we optimized the choice of SNP AIMs from the 317K Illumina array that were separated by a minimum of 50 kb. This selection was performed by first choosing the SNP with the greatest FST value in each successive 100-kb bin and by then removing the SNP AIM with the lower FST when two SNPs were present within a 50-kb interval. The same procedure was performed with a combination of the three sets of SNP AIMs. These SNP AIMs are provided in our Rich Text Format (RTF) files RTF1, RTF2, RTF3, RTF4, and RTF5. For the present study, the analyses were performed using the deCODE28Kong A Gudbjartsson DF Sainz J Jonsdottir GM Gudjonsson SA Richardsson B Sigurdardottir S Barnard J Hallbeck B Masson G et al.A high-resolution recombination map of the human genome.Nat Genet. 2002; 31: 241-247Crossref PubMed Scopus (1329) Google Scholar genetic maps. The position of each SNP was determined by interpolation with use of markers that were on the genetic map and for which an unambiguous physical map position was available in National Center for Biotechnology Information build 35. Any markers that were not in the same relative order in both the genetic and physical maps were omitted as anchors for the interpolation of the genetic positions of the SNPs. Smaller subsets of SNP AIMs examined in this study were derived from the total SNP AIM set (n=8,144) described above. A set of 4,072 SNPs was chosen simply by selecting every other SNP AIM in order of chromosomal position. A set of 5,287 SNPs enriched for information content were obtained by (1) allowing a maximum of three SNPs in each 1-cM segment of the deCODE map and (2) allowing only two SNPs in 1-cM segments of the deCODE map if there were SNPs in both flanking 1-cM segments, the sum of the FST values was >1.0, and the segment was not within 10 cM of a chromosome end (RTF5). In each case, our method removed the SNP with the smallest FST value. Finally, a set of 3,000 SNP AIMs was obtained by choosing the best SNP AIM in each 1-cM bin. The set of 39 SNP AIMs used for the comparisons of multiple AMI groups by TaqMan assays included rs3768641, rs762656, rs7504, rs1426654, rs262838, rs9847748, rs730570, rs4478653,rs1880550, rs2380316, rs300152, rs6587216, rs7995033, rs9295009, rs883399, rs2065160, rs2384319, rs1638567, rs1266874, rs2165139, rs901304, rs17638989, rs814597, rs6086473, rs1475930, rs1648180, rs293553, rs1951936, rs2065982, rs1540979, rs734329, rs9937955, rs1931059,rs6601288, rs953786, rs2439522, rs1417999, rs1572396, and rs1418032. Power was assessed in MAM by simulations and ADMIXMAP analyses, by use of marker-allele frequencies and map positions (deCODE) for our current SNP sets. For each level of admixture information, the disease allele was placed in the middle of one of three different chromosomal regions that corresponded to the level of admixture information being examined (for 80%, chromosome 3 [138 cM], chromosome 5 [102 cM], and chromosome 14 [44 cM]; for 70%, chromosome 3 [80 cM], chromosome 4 [135 cM], and chromosome 13 [72 cM]; for 60%, chromosome 3 [152 cM], chromosome 6 [36 cM], and chromosome 14 [94 cM]). Simulations (>150 for each ERR) were performed using a 15-generation continuous-gene-flow (CGF) model and 50:50 EUR:AMI admixture. To develop a genomewide panel of EUR/AMI SNP AIMs, the genotypes of EUR subjects and Pima AMI subjects were initially examined for three sets of SNPs: (1) gene-rich 100K Illumina array, (2) 317K Illumina array, and (3) a set of 20,000 SNPs enriched for large allele-frequency differences between EUR and East Asian populations. Genotypes were ascertained or available for the following subject sets: set 1, 192 EUR and 24 Pima; set 2, 222 EUR and 23 Pima; and set 3, 74 EUR and 72 Pima. A total of >400,000 unique SNPs that were not excluded by our quality assessment were analyzed for allele-frequency differences. For the combined sets, analysis of the EUR/Pima allele frequencies showed a total of 46,450 SNPs with FST>0.30 and 11,999 SNPs with FST>0.5. To validate the identification of SNPs informative for EUR versus AMI ancestry, we performed a second screen, using DNAs from subjects of Mayan AMI ancestry (set 1, 16 subjects; set 2, 24 subjects; set 3, 29 subjects). Mayan subjects were chosen for this confirmation since they are part of another AMI group, distinct from Pima AMI, who are members of the larger Ito-Aztecan AMI grouping and whose ancestors are thought to have pr" @default.
• W2033973364 created "2016-06-24" @default.
• W2033973364 creator A5007210976 @default.
• W2033973364 creator A5028373176 @default.
• W2033973364 creator A5036911243 @default.
• W2033973364 creator A5046437327 @default.
• W2033973364 creator A5047263678 @default.
• W2033973364 creator A5049047999 @default.
• W2033973364 creator A5050165878 @default.
• W2033973364 creator A5056201449 @default.
• W2033973364 creator A5056317654 @default.
• W2033973364 creator A5056481945 @default.
• W2033973364 creator A5058716877 @default.
• W2033973364 creator A5066232855 @default.
• W2033973364 creator A5082248535 @default.
• W2033973364 date "2007-06-01" @default.
• W2033973364 modified "2023-09-29" @default.
• W2033973364 title "A Genomewide Single-Nucleotide–Polymorphism Panel for Mexican American Admixture Mapping" @default.
• W2033973364 cites W1608693905 @default.
• W2033973364 cites W1821053631 @default.
• W2033973364 cites W1963971765 @default.
• W2033973364 cites W1973747688 @default.
• W2033973364 cites W1982663312 @default.
• W2033973364 cites W1991450386 @default.
• W2033973364 cites W1995404520 @default.
• W2033973364 cites W1997476591 @default.
• W2033973364 cites W2001115778 @default.
• W2033973364 cites W2002256043 @default.
• W2033973364 cites W2003298782 @default.
• W2033973364 cites W2025253353 @default.
• W2033973364 cites W2026869877 @default.
• W2033973364 cites W2037710951 @default.
• W2033973364 cites W2041593701 @default.
• W2033973364 cites W2052200503 @default.
• W2033973364 cites W2057292957 @default.
• W2033973364 cites W2057801064 @default.
• W2033973364 cites W2058793433 @default.
• W2033973364 cites W2069732935 @default.
• W2033973364 cites W2082043949 @default.
• W2033973364 cites W2083333719 @default.
• W2033973364 cites W2085374147 @default.
• W2033973364 cites W2094907095 @default.
• W2033973364 cites W2098126593 @default.
• W2033973364 cites W2102926234 @default.
• W2033973364 cites W2104743461 @default.
• W2033973364 cites W2106268473 @default.
• W2033973364 cites W2114505783 @default.
• W2033973364 cites W2121308112 @default.
• W2033973364 cites W2124415683 @default.
• W2033973364 cites W2130599610 @default.
• W2033973364 cites W2141827781 @default.
• W2033973364 cites W2145371252 @default.
• W2033973364 cites W2158489424 @default.
• W2033973364 cites W2171838950 @default.
• W2033973364 cites W2217809488 @default.
• W2033973364 cites W2325754393 @default.
• W2033973364 cites W4231311503 @default.
• W2033973364 doi "https://doi.org/10.1086/513522" @default.
• W2033973364 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1867091" @default.
• W2033973364 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17557415" @default.
• W2033973364 hasPublicationYear "2007" @default.
• W2033973364 type Work @default.
• W2033973364 sameAs 2033973364 @default.
• W2033973364 citedByCount "125" @default.
• W2033973364 countsByYear W20339733642012 @default.
• W2033973364 countsByYear W20339733642013 @default.
• W2033973364 countsByYear W20339733642014 @default.
• W2033973364 countsByYear W20339733642015 @default.
• W2033973364 countsByYear W20339733642016 @default.
• W2033973364 countsByYear W20339733642017 @default.
• W2033973364 countsByYear W20339733642018 @default.
• W2033973364 countsByYear W20339733642019 @default.
• W2033973364 crossrefType "journal-article" @default.
• W2033973364 hasAuthorship W2033973364A5007210976 @default.
• W2033973364 hasAuthorship W2033973364A5028373176 @default.
• W2033973364 hasAuthorship W2033973364A5036911243 @default.
• W2033973364 hasAuthorship W2033973364A5046437327 @default.
• W2033973364 hasAuthorship W2033973364A5047263678 @default.
• W2033973364 hasAuthorship W2033973364A5049047999 @default.
• W2033973364 hasAuthorship W2033973364A5050165878 @default.
• W2033973364 hasAuthorship W2033973364A5056201449 @default.
• W2033973364 hasAuthorship W2033973364A5056317654 @default.
• W2033973364 hasAuthorship W2033973364A5056481945 @default.
• W2033973364 hasAuthorship W2033973364A5058716877 @default.
• W2033973364 hasAuthorship W2033973364A5066232855 @default.
• W2033973364 hasAuthorship W2033973364A5082248535 @default.
• W2033973364 hasBestOaLocation W20339733641 @default.
• W2033973364 hasConcept C104317684 @default.
• W2033973364 hasConcept C135763542 @default.
• W2033973364 hasConcept C149737253 @default.
• W2033973364 hasConcept C153209595 @default.
• W2033973364 hasConcept C54355233 @default.
• W2033973364 hasConcept C86803240 @default.
• W2033973364 hasConceptScore W2033973364C104317684 @default.
• W2033973364 hasConceptScore W2033973364C135763542 @default.
• W2033973364 hasConceptScore W2033973364C149737253 @default.
• W2033973364 hasConceptScore W2033973364C153209595 @default.
• W2033973364 hasConceptScore W2033973364C54355233 @default.

• next