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• W2073899382 abstract "Human genetic linkage maps are based on rates of recombination across the genome. These rates in humans vary by the sex of the parent from whom alleles are inherited, by chromosomal position, and by genomic features, such as GC content and repeat density. We have examined—for the first time, to our knowledge—racial/ethnic differences in genetic maps of humans. We constructed genetic maps based on 353 microsatellite markers in four racial/ethnic groups: whites, African Americans, Mexican Americans, and East Asians (Chinese and Japanese). These maps were generated using 9,291 subjects from 2,900 nuclear families who participated in the National Heart, Lung, and Blood Institute–funded Family Blood Pressure Program, the largest sample used for map construction to date. Although the maps for the different groups are generally similar, we did find regional and genomewide differences across ethnic groups, including a longer genomewide map for African Americans than for other populations. Some of this variation was explained by genotyping artifacts—namely, null alleles (i.e., alleles with null phenotypes) at a number of loci—and by ethnic differences in null-allele frequencies. In particular, null alleles appear to be the likely explanation for the excess map length in African Americans. We also found that nonrandom missing data biases map results. However, we found regions on chromosome 8p and telomeric segments with significant ethnic differences and a suggestive interval on chromosome 12q that were not due to genotype artifacts. The difference on chromosome 8p is likely due to a polymorphic inversion in the region. The results of our investigation have implications for inferences of possible genetic influences on human recombination as well as for future linkage studies, especially those involving populations of nonwhite ethnicity. Human genetic linkage maps are based on rates of recombination across the genome. These rates in humans vary by the sex of the parent from whom alleles are inherited, by chromosomal position, and by genomic features, such as GC content and repeat density. We have examined—for the first time, to our knowledge—racial/ethnic differences in genetic maps of humans. We constructed genetic maps based on 353 microsatellite markers in four racial/ethnic groups: whites, African Americans, Mexican Americans, and East Asians (Chinese and Japanese). These maps were generated using 9,291 subjects from 2,900 nuclear families who participated in the National Heart, Lung, and Blood Institute–funded Family Blood Pressure Program, the largest sample used for map construction to date. Although the maps for the different groups are generally similar, we did find regional and genomewide differences across ethnic groups, including a longer genomewide map for African Americans than for other populations. Some of this variation was explained by genotyping artifacts—namely, null alleles (i.e., alleles with null phenotypes) at a number of loci—and by ethnic differences in null-allele frequencies. In particular, null alleles appear to be the likely explanation for the excess map length in African Americans. We also found that nonrandom missing data biases map results. However, we found regions on chromosome 8p and telomeric segments with significant ethnic differences and a suggestive interval on chromosome 12q that were not due to genotype artifacts. The difference on chromosome 8p is likely due to a polymorphic inversion in the region. The results of our investigation have implications for inferences of possible genetic influences on human recombination as well as for future linkage studies, especially those involving populations of nonwhite ethnicity. Genetic linkage maps describe the relative locations of genetic markers on chromosomes. Distances between genetic markers are determined by measuring the frequency of meiotic recombination between markers. Genetic linkage maps can be used to identify the location of genes responsible for traits and diseases. Human genetic linkage maps are important for two reasons. First, genetic linkage maps can be used as a tool in linkage analysis, association studies, and the building of physical maps. The first constructed maps of the human genome were genetic linkage maps, built by measuring the recombination rates between genetic markers, which usually were blood groups and serum proteins. Second, genetic linkage maps can be used to study rates and patterns of recombination across the genome. Variation exists in human genetic map length. Rates of recombination vary by chromosome position, GC content, and the density of selected repeat units (Yu et al. Yu et al., 2001Yu A Zhao C Fan Y Jang W Mungall AJ Deloukas P Olsen A Doggett NA Ghebranious N Broman KW Weber JL Comparison of human genetic and sequence-based physical maps.Nature. 2001; 409: 140-146Crossref PubMed Scopus (218) Google Scholar). Genetic linkage maps based on maternal inheritance are, on average, >50% longer than maps based on paternal inheritance. This difference is likely due to the different underlying biological processes of male and female meiotic recombination. Some differences in recombination rates have been shown to be under genetic control. In humans, variation in maternal recombination rates has been shown to be specific to individuals and is not explained by maternal age (Broman et al. Broman et al., 1998Broman KW Murray JC Sheffield VC White RL Weber JL Comprehensive human genetic maps: individual and sex-specific variation in recombination.Am J Hum Genet. 1998; 63: 861-869Abstract Full Text Full Text PDF PubMed Scopus (893) Google Scholar). Significant variation between individuals has been noted for human spermatocytes (Cullen et al. Cullen et al., 2002Cullen M Perfetto SP Klitz W Nelson G Carrington M High-resolution patterns of meiotic recombination across the human major histocompatibility complex.Am J Hum Genet. 2002; 71: 759-776Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar; Lynn et al. Lynn et al., 2002Lynn A Koehler KE Judis L Chan ER Cherry JP Schwartz S Seftel A Hunt PA Hassold TJ Covariation of synaptonemal complex length and mammalian meiotic exchange rates.Science. 2002; 296: 2222-2225Crossref PubMed Scopus (209) Google Scholar) and oocytes (Tease et al. Tease et al., 2002Tease C Hartshorne GM Hultén MA Patterns of meiotic recombination in human fetal oocytes.Am J Hum Genet. 2002; 70: 1469-1479Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar). Rates of recombination have also been shown to be subject to genetic control in other organisms (Page and Hawley Page and Hawley, 2003Page SL Hawley RS Chromosome choreography: the meiotic ballet.Science. 2003; 301: 785-789Crossref PubMed Scopus (305) Google Scholar). Specifically, the existence of both genomewide control (Catcheside Catcheside, 1977Catcheside DG The genetics of recombination. University Park Press, London1977Google Scholar) and chromosome-wide control (Hillers and Villeneuve Hillers and Villeneuve, 2003Hillers KJ Villeneuve AM Chromosome-wide control of meiotic crossing over in C. elegans.Curr Biol. 2003; 13: 1641-1647Abstract Full Text Full Text PDF PubMed Scopus (130) Google Scholar) has been demonstrated in other species. Variation in recombination rates across different strains of mice has been noted elsewhere (Koehler et al. Koehler et al., 2002Koehler KE Cherry JP Lynn A Hunt PA Hassold TJ Genetic control of mammalian meiotic recombination. I. Variation in exchange frequencies among males from inbred mouse strains.Genetics. 2002; 162: 297-301PubMed Google Scholar). Variation in recombination rates (and, therefore, in genetic map length) across human ethnic groups has not been studied. The most recent and extensive genomewide human genetic linkage maps (Broman et al. Broman et al., 1998Broman KW Murray JC Sheffield VC White RL Weber JL Comprehensive human genetic maps: individual and sex-specific variation in recombination.Am J Hum Genet. 1998; 63: 861-869Abstract Full Text Full Text PDF PubMed Scopus (893) Google Scholar; Kong et al. Kong et al., 2002Kong A Gudbjartsson DF Sainz J Jonsdottir GM Gudjonsson SA Richardsson B Sigurdardottir S Barnard J Hallbeck B Masson G Shlien A Palsson ST Frigge ML Thorgeirsson TE Gulcher JR Stefansson K A high-resolution recombination map of the human genome.Nat Genet. 2002; 31: 241-247Crossref PubMed Scopus (1329) Google Scholar) have been constructed using either entirely white samples or combined information from individuals of various ethnicities (Matise et al. Matise et al., 2003Matise TC Sachidanandam R Clark AG Kruglyak L Wijsman E Kakol J Buyske S et al.A 3.9-centimorgan-resolution human single-nucleotide polymorphism linkage map and screening set.Am J Hum Genet. 2003; 73: 271-284Abstract Full Text Full Text PDF PubMed Scopus (99) Google Scholar), making ethnic comparisons impossible. Here, we made such comparisons on the basis of large samples from four major racial/ethnic groups: whites, African Americans, Mexican Americans (Hispanics), and East Asians (Chinese and Japanese). The FBPP consists of four component networks: GenNet, GENOA, HyperGEN, and SAPPHIRe. Recruitment strategies for each network have been described elsewhere (FBPP Investigators FBPP Investigators, 2002FBPP Investigators Multi-center genetic study of hypertension: the Family Blood Pressure Program (FBPP).Hypertension. 2002; 39: 3-9Crossref PubMed Scopus (165) Google Scholar). The genetic maps were constructed using the pooled data version 3.15 from the FBPP. A total of 353 microsatellite markers were selected that had been genotyped in all four racial/ethnic groups. A total of 9,291 subjects from families with two or more children were included in the construction of the maps (table A1[online only]). The total number of children examined for the combined sample of all four ethnicities was 8,428—including 3,301 in the white sample, 1,564 in the African American sample, 1,610 in the Hispanic sample, and 1,953 in the Asian sample (table A2[online only]. A total of 863 parents were available, including 220 in the African American sample, 408 in the white sample, and 235 in the Asian sample. There were no parents available for the Hispanic sample. The FBPP data set contains nuclear families with primarily full sibships, as well as some half siblings. The families with half sibs were split into independent full sibships. Whereas a parent can be a member of more than one nuclear family subdivision, children appear only once in the final data set, and thus all families provide independent information.Table A1Subjects Included in the Map Construction (Families with Two or More Children)Race/EthnicityNo. of FamiliesNo. of ChildrenNo. of ParentsNo. of SubjectsAfrican American6741,5642201,784White1,2423,3014083,709Hispanic2081,61001,610Asian5761,9532352,188 Total2,7008,4288639,291 Open table in a new tab Table A2Distribution of Families, by Race/Ethnicity, Number of Children, and Parental GenotypesNo. of Families with Available Parental Genotypes forRace/Ethnicity and No. of ChildrenNo ParentsOne ParentOne FatherOne MotherTwo ParentsTotal No. of FamiliesAfrican American: 23771292710214520 3732131810104 4261019440 5620208 6021102 Total4821643213228674White: 2579123398467769 320659253420285 484154118107 535743345 614211218 7810109 8310104 9200002 10200002 11000000 12000000 13000000 14100001 Total934208731351001,242Hispanic: 21000000100 31000000100 481000081 548000048 636000036 725000025 8900009 9700007 10200002 Total4080000408Asian: 210645162914165 312257104713192 475327258115 54314113461 622321126 710202012 8120203 9200002 Total3811553611940576 Open table in a new tab Maps were constructed using 353 autosomal microsatellite markers from Marshfield screening set 8. All genotyping was performed by the Mammalian Genotyping Service of the Marshfield Center for Medical Genetics (see Marshfield Web site). Genetic maps were constructed using the ASPEX package program sib_map (see ASPEX Web site). The sib_map program generates two-point and multipoint maximum-likelihood estimates of map distances between markers, on the basis of data from nuclear families. The multipoint maximization algorithm determines the complete set of distances that gives the maximum likelihood globally for the marker data across all markers on a given chromosome. The two-point algorithm considers each pair of adjacent markers separately, ignoring information from more-distant markers. In a second mode of operation, do_shuffle, the sib_map program calculates three-point distances for one marker against all other pairs of adjacent markers along a map. This method can be used to verify map orders or to position new markers on an already determined map. The order of the microsatellite markers has been established elsewhere and is available at the Marshfield Web site. The first step in the construction of the FBPP genetic maps was to verify the previously established order for whites by use of the do_shuffle function of sib_map. A typographical mistake in the Marshfield map was identified, and its correction placed marker GATA7G07 on chromosome 6 rather than chromosome 8. No other inconsistencies were noted. Sex-averaged and sex-specific genetic intermarker distances were estimated using the Kosambi map function of sib_map. Maps were constructed for each of the four racial/ethnic groups. An earlier report describing the Marshfield map (Broman et al. Broman et al., 1998Broman KW Murray JC Sheffield VC White RL Weber JL Comprehensive human genetic maps: individual and sex-specific variation in recombination.Am J Hum Genet. 1998; 63: 861-869Abstract Full Text Full Text PDF PubMed Scopus (893) Google Scholar) determined that errors in genotyping that lead to misinheritance can dramatically inflate genetic map distances, by as much as 25% in that reported case. Misinheritances were eliminated during several rounds of data cleaning of the FBPP microsatellite data. Genotype errors can persist even after the elimination of inheritance errors. Because of observed racial/ethnic differences in genetic maps in our preliminary analyses, we decided to examine more carefully the genotype data for systematic problems. We tested for deviation from Hardy-Weinberg expectations that results from the presence of null alleles (i.e., alleles with null phenotypes). Null alleles leave two marks on genotype data: apparent excess homozygosity and an increased proportion of null phenotypes (subjects having no value for a particular marker genotype). We estimated the frequency of null alleles for each marker and each race/ethnicity by use of maximum-likelihood analysis. We tested all markers for each racial/ethnic group by use of the Null Allele Test (NAT), which tests whether the frequency of null alleles is different from 0 at a given marker, by use of a likelihood-ratio test. The test is one sided because we constrained the frequency of null alleles at >0; the statistic is then distributed as a 50:50 mixture of a χ2 distribution with 1 df and a point mass at 0. Allele frequencies were calculated using (independent) subjects from each ethnic group. Frequencies were calculated separately for the Japanese and Chinese groups, and the resulting null-allele frequencies for the two groups were found to be quite similar. The total number of individuals in each group was 1,818 African Americans, 1,657 whites, 416 Hispanics, 162 Japanese, and 409 Chinese. In the construction of the genetic maps, it was also determined that nonrandom missing genotype data inflated intermarker distances. In particular, subjects at one site—Jackson, MS (1,725 subjects)—had a greater proportion of missing genotypes than the other groups. For this reason, we dropped this site from the map construction, and subjects from this site are not included in the sample size counts, although they are included in the genotype error calculations. Although the maximum-likelihood algorithm that was used to determine map lengths provides unbiased results in the presence of random missing data, results are not necessarily unbiased in the presence of nonrandom missing data (Little and Rubin Little and Rubin, 2002Little R Rubin D The problem of missing data.in: Statistical analysis with missing data. 2nd ed. John Wiley and Sons, Hoboken, NJ2002: 3-19Google Scholar). We add nonrandom missing data to the list of caveats to be considered in constructing genetic maps. ASPEX provides marker-allele frequency estimates from the individuals in the sample analyzed. Allele frequencies of microsatellite markers vary by race. For the purpose of comparison of ethnic-specific maps, a combined map for a pair of ethnicities was constructed to allow for different allele frequencies for each racial/ethnic group in the analysis, by use of a modification of the sib_map program. ASPEX provides a LOD score for each marker interval, comparing the likelihood of the estimated interval length with the likelihood for the case in which the two markers are assumed to be unlinked. By taking the LOD score for the same marker interval of two racial/ethnic group maps and the LOD score for the combined map, we were able to perform a likelihood-ratio test on each marker interval. Since the result of each likelihood-ratio test is distributed as a χ2 with 1 df, we converted the result to a normally distributed Z score by taking the square root and assigning either a positive or negative sign (+ or −) on the basis of which of the two map intervals was larger. The order of ethnicities used for this calculation was African American, white, Hispanic, and Asian, so that all intervals for which the African American map was longer were given a “+” sign, and all intervals for which the Asian map was longer were given a “−” sign. We used the derived Z scores to determine the significance of the differences in map length between racial/ethnic groups. Z scores for the difference in total genetic map length between groups were calculated by summing the Z scores of each of the 331 individual map intervals and dividing the sum by the square root of 331. The same procedure was employed to calculate Z scores for individual chromosome arms, as well as centromeric and telomeric Z scores. Centromeric intervals were defined as intervals that span the known location of the centromere. Telomeric intervals were defined as the intervals that are covered by the two most telomeric markers on a chromosome arm. Microsatellite markers do not exist in the telomere tandem repeats, and so our markers cover regions that are telomeric but are not at the physical end points of the chromosomes. The physical distance from the midpoint of the two telomeric markers to the physical end of the chromosome ranged from 1.8 Mb to 17.9 Mb, with an average distance of 6.2 Mb (table A3[online only]).Table A3Telomeric Interval LengthsInterval Length(cM) for MapAfrican AmericanWhiteAsianChromosome and Marker IntervalDistance to TelomereaPhysical distance (Mb) from the midpoint of the interval to the physical end of the chromosome.AveragedMaternalPaternalAveragedMaternalPaternalAveragedMaternalPaternalHispanic Averaged1: AFM280we5–GGAA3A075.310.711.110.412.910.615.411.411.81112 GATA4A09–GATA50F1110.64.74.54.74.32.85.943.44.55.12: GATA165C07–GATA116B014.418.11225.4181521.213.59.618.120 GATA178G09–AFM112yd47.27.23.711.19.9614.15.6.112.68.33: AFM234tf4–GATA164B084.65.55.45.54.13.34.944.83.24.9 ATA22E01–AFM254ve13.212.79.116.912.56.619.612.46.118.9114: GATA22G05–AFM157xg39.912.918.4816221114.617.112.513.5 AFM165xc11–GATA5B024.314.58.421.115.810.622.415.77.529.315.95: GATA145D10–GATA84E111.87.969.88.67.89.26.87.46.37.2 Mfd154–AFM164xb84.313.610.417.414.6111915.29.921.4156: SE30–ATA50C0510.014.621.79.115.817.414.215.719.711.914.8 GATA81B01–ATA22G074.311.78.615.310.89.212.411.59.913.210.37: GATA24F03–AFM217yc57.816.518.114.914.117.411.112.917.2915 GATA30D09–Mfd2655.06.76.377.16.18.17.159.68.18: AFM143xd8–AFM198wd24.217.19.327.912.27.61711.27.415.614.7 AFM073yb7–UT7218.521.920.623.222.223.421.218.816.521.320.99: GATA62F03–GATA27A117.825.336.618.122.422.621.8273124.227.1 ATA59H06–AFM303ZG917.915.711.520.215.610.421.715.210.720.317.910: GATA88F09–AFM063xf44.613.212.31414.38.921.113.18.118.911.8 GGAA23C05–AFM198zb46.710.67.314.394.813.57.43.811.210.111: GGAA17G05–ATA33B033.210.28.212.187.68.67.74.611.18.9 AFM157xh6–AFM109xc33.315.46.827.414.89.221.714.411.1191412: GATA4H03–GATA49D125.913.511.915.412.9917.511.26.316.713 ATA29A06–GATA13D052.57.57.87.25.53.77.443.24.72.113: GATA51B02–AFM309va94.015.59.32317.313.122.213.412.914.116.614: GATA168F06–GATA136B019.514.420.19.615.317.313.61118.86.215.515: GATA22F01–GATA27A036.5199.135.5187.832.816.57.329.316.316: ATA41E04–ATA3A079.217.713.721.715.515.11613.411.814.814.3 GATA11C06–GATA71F0912.14.42.86.35.36.44.25.246.55.917: GTAT1A05–GAAT2C032.111.42.9248.92.517.411.83.920.98.5 AFM044xg3–AFM217yd103.25.52.19.36.33.19.86.11.811.65.218: GATA178F11–AFM321xc92.54.4.67.94.53.55.63.31.84.93.8 GATA177C03–ATA1H065.45.45.65.23.43.934.33.25.14.119: GATA44F10–GATA21G054.610.69.112.210.28.411.911.4715.111.1 GATA29B01–Mfd2382.517.39.528.718.110.329.918.99.732.715.720: AFM077xd3–GATA51D032.510.82.521.311.62.823.113.65.325.49.8 GATA45B10–AFM046xf610.017.121.613.716.319.613.716.72510.418.821: GATA188F04–GATA70B084.420.917.624.718.515.122.318.412.526.517.122: GATA11B12–GGAT3C1014.94.17.7.54.672.17.48.85.85 Total length of all telomeric intervals6.2486.2410.2600475.2388.9587.6451.8366573.8469.3a Physical distance (Mb) from the midpoint of the interval to the physical end of the chromosome. Open table in a new tab The total genetic map lengths for each chromosome and for the entire genome were calculated for sex-averaged, maternal, and paternal maps (table 1). The total African American map is 1%–2.5% longer than the maps of the other groups. The difference between the African American and Hispanic sex-averaged maps was nominally significant, although not when the results of multiple testing (which allowed for six comparisons) were considered.Table 1Paternal, Maternal, and Sex-Averaged Map LengthsMap Length (cM) for GroupAfrican AmericanWhiteAsianChromosomePaternalMaternalAveragedPaternalMaternalAveragedPaternalMaternalAveragedHispanic Averaged1209359277209356276213344273272221634527421335427621333426727731652812181582812121602712102174146271201154263203157277207202515826320315427020515325919919861352451851262351761352591911797141237186135237182140227180190813321917012322016712521316417091182301611181901501212071581581013523417814123318013522017217311137209160131201157129209155159121332171711272091621232051601651310113811710515412710814812612314104126114941231079013711111115107154123110128115106140119117161001591258416312087154117123171291821491251691411251741421441889144113821491128814611411319941331089013010393136107103207913199851251018412610110121538567597465626762632231534134574437534545 Total2,7114,4153,4402,6544,3203,3792,6854,3063,3783,398 Open table in a new tab To determine whether the Z scores for individual marker-interval-length differences fit the normal distribution as expected, we created quantile-quantile (Q-Q) plots of the Z scores for individual marker-interval comparisons for each pair of racial/ethnic groups (fig. 1a–1f). Q-Q plots compare an observed distribution with an expected distribution. The expected distribution of Z scores is the normal distribution, and so deviations from the y=x line indicate deviation of the observed score from the expected distribution. Plots involving the African American sex-averaged map (fig. 1a–1c) fell largely above the y=x line, consistent with the overall greater average interval length of the African American maps. The white versus Hispanic plot (fig. 1d) shows data points very close to the y=x line, indicating little difference in map interval lengths for these two maps. We expect, a priori, to find the smallest difference between the white and Hispanic groups on the basis of genetic distances, since Hispanics have ∼60% white ancestry (Tang et al. Tang et al., 2005Tang H Quertermous T Rodriquez B Kardia SLR Zhu X Brown A Pankow JS Province MA Hunt SC Boerwinkle E Schork NJ Risch NJ Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies.Am J Hum Genet. 2005; 76 (in this issue): 268-275Abstract Full Text Full Text PDF PubMed Scopus (401) Google Scholar [in this issue]). Plots involving the Asian map (fig. 1c,1e, and 1f) show the largest number of outliers. To examine the distribution of outliers in our sample, we calculated the number of Z scores, for each comparison group, that exceeded two cutoffs (table 2). The cutoffs chosen were 1.96 (i.e., nominal significance) and 3.48 (i.e., 1 expected false positive in 1,986 tests). The total (+ and −) number of Z scores for each comparison group exceeded the expected number for all groups, except for the white-Hispanic comparison.Table 2Distribution of Nominally Significant Z Scores, by Comparison GroupNo. of SignificantZScoresFor Comparison GroupZScore CutoffExpected per GroupAfrican American– WhiteAfrican American– HispanicAfrican American– AsianWhite- HispanicWhite- AsianHispanic- AsianTotalTotal Expected+1.968.28131313911127149.65+3.48.080000101.50−1.968.28668814175949.65−3.48.081120419.50 Total: 1.9616.5519192117252913099.30 3.48.17112051101.00 Open table in a new tab In an effort to identify regions of the genome in which significant Z scores clustered, we identified all marker intervals that had one or more Z scores >3.48 (table 3). We found three regions, on chromosomes 6p, 8p, and 12q.Table 3Regions with Significant Differences in Interval Length for Comparison GroupsZScore for Difference in Interval Length for Comparison GroupChromosome and Marker IntervalAfrican American–WhiteAfrican American–HispanicAfrican American– AsianWhite- HispanicWhite- AsianHispanic- Asian6: ATA50C05–GATA29A012.561.55−.68−1.01−3.78−2.49 GATA29A01–GATA163B101.99.30−.71−1.94−3.06−1.17 GATA163B10–GGAA15B083.02.43−.64−2.80−3.91−1.15 GGAA15B08–GGAT3H10.21.21−3.78.00−4.53−4.388: AFM143xd8–AFM198wd22.56.913.19−1.78.832.51 AFM198wd2–GATA25C10−3.57−5.29−7.18−2.40−4.75−2.06 GATA25C10–GATA23D062.473.183.121.30.96.4312: GATA4H01–GATA32F05−.37−.212.62.643.602.56 Open table in a new tab In three of four adjoining intervals on 6p, the Asian map is significantly longer than the white map, and, in one of those intervals, the Asian map is significantly longer than all other maps. This difference is apparent in the sex-specific maps as well. The Asian maternal map is longer than both the African American and white maternal maps, whereas the white paternal map appears to be smaller than both the African American and Asian paternal maps (table 4).Table 4Interval Length for Markers on Chromosomes 6p, 8p, and 12qInterval Length (in cM) for GroupChromosome, Map Type, and Marker IntervalAfrican AmericanWhiteHispanicAsian6p: Sex-averaged: ATA50C05–GATA29A0112.28.910.212.3 GATA29A01–GATA163B109.78.39.610.9 GATA163B10–GGAA15B0813.410.512.313.4 GGAA15B08–GGAT3H104.64.53.87.7 Total39.932.235.944.3 Maternal: ATA50C05–GATA29A0117.613.6…17.9 GATA29A01–GATA163B1011.214.6…15.7 GATA163B10–GGAA15B0817.915.3…21.2 GGAA15B08–GGAT3H106.55.9…9.2 Total53.249.4…64 Paternal: ATA50C05–GATA29A017.84.9…8.1 GATA29A01–GATA163B108.23.1…6.9 GATA163B10–GGAA15B089.86.6…7.6 GGAA15B08–GGAT3H102.83.2…6.3 Total28.617.8…28.98p: Sex-averaged: AFM143xd8–AFM198wd217.112.214.711.2 AFM198wd2–GATA25C103.15.58.410.4 GATA25C10–GATA23D067.643.33.2 Total27.821.726.424.8 Maternal: AFM143xd8–AFM198wd29.37.6…7.4 AFM198wd2–GATA25C105.37.9…12.1 GATA25C10–GATA23D0610.34.8…4.3 Total24.920.3…23.8 Paternal: AFM143xd8–AFM198wd227.917…15.6 AFM198wd2–GATA25C10.43.3…8.5 GATA25C10–GATA23D0653.3…2.1 Total33.323.6…26.212q: Sex-averaged: GATA4H01–GATA32F0512.914.113.59.8 Maternal: GATA4H01–GATA32F0513.812.8…8.1 Paternal: GATA4H01–GATA32F051215.2…12.1 Open table in a new tab One interval on chromosome 8p has the largest Z scores of any interval comparison. Interestingly, the neighboring intervals on each side of the significant interval have high Z scores in the opposite direction. Examining the interval lengths, we see that the African American map, which has the shortest interval length for the middle markers, has the longest interval lengths for the neighboring markers (table 4). The opposite is true for the Asian map. The last significant interval-length difference occurs on chromosome 12q. The white map has the longest map length, whereas the Asian map has the shortest (table 4). The adjoining intervals are not significantly different. It is clear that the statistical significance is a result of the sex-averaged map in Asians being shorter than that in the other three groups; this observation is also reproduced in the maternal but not the paternal maps. Having identified marker intervals with significant differences in map length, we sought to examine chromosomal regions to see if any regional differences in map length exist. We calculated Z scores for the arms of each chromosome (39 arms in all) and identified several highly significant differences in genetic map length. The p arm of chromosome 6 gave the most significant results, including a Z score of −6.40 in the white-Asian comparison group and significant Z scores in other comparison groups. As described above, chromosome 6p contains several marker intervals with high Z scores, particularly those in the white-Asian comparison group. When considered" @default.
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