ABCMdb

PMID: 12734544

Lao O, Andres AM, Mateu E, Bertranpetit J, Calafell F
Spatial patterns of cystic fibrosis mutation spectra in European populations.
Eur J Hum Genet. 2003 May;11(5):385-94., [PubMed]

Sentences

No. Mutations Sentence Comment

35 ABCC7 p.Gly551Asp Geographical patterns A geographical description of allele frequency patterns was obtained by drawing maps of gene frequencies for the most common CF mutations (namely F508del, G452X, G551D, N1303 K, and W1285X, which are the ones found at average frequencies 41%), as well as for genetic diversity estimates, by using Surfer 7.0 (http://www.goldensoftware. Login to comment 52 ABCC7 p.Gly551Asp ABCC7 p.Trp1282* ABCC7 p.Gly542* As for the direction of the clines, F508del peaks in NW Europe and declines towards SE Europe, G542X declines from SW to NE Europe, G551D is almost restricted to NW Europe, and N1303 K and W1282X show gradients from SW Asia and SE Europe towards NW Europe. Login to comment 66 ABCC7 p.Gly551Asp ABCC7 p.Trp1282* ABCC7 p.Asn1303Lys ABCC7 p.Gly542* The correlations with Y-chromosome-based Table 1 94 middle Eastern, North African and European populations used in the analysis Population 2N F508del G542X G551D N1303K W1282X Rare Other Unknown Hmax Ymax Incidence Referencesa Austria 592 0.660 0.022 0.012 0.005 0.002 0.064 0.019 0.216 0.562 0.96 49 Belgium 646 0.752 0.025 0.002 0.028 0.012 0.053 0.046 0.082 0.430 0.56 50 Bulgaria 208 0.654 0.034 0.000 0.067 0.000 0.096 0.067 0.082 0.563 0.97 51 Crete 26 0.462 0.077 0.000 0.038 0.000 0.231 0.038 0.154 0.785 2.87 Czech Republic 584 0.697 0.021 0.034 0.026 0.005 0.051 0.021 0.146 0.512 0.78 Denmark 678 0.872 0.006 0.001 0.010 0.001 0.034 0.035 0.040 0.239 0.23 0.000210 Great Britain 0.000414b North England 4111 0.772 0.008 0.023 0.005 0.001 0.032 0.011 0.148 0.403 0.50 Scotland 1167 0.751 0.033 0.061 0.003 0.003 0.033 0.023 0.093 0.430 0.56 0.000504 South England 3679 0.769 0.020 0.029 0.005 0.002 0.032 0.009 0.133 0.407 0.51 Wales 372 0.659 0.024 0.030 0.005 0.000 0.134 0.065 0.083 0.557 0.94 Estonia 25 0.640 0.000 0.000 0.000 0.000 0.160 0.080 0.120 0.577 1.02 Former Yugoslavia 203 0.700 0.030 0.000 0.010 0.005 0.039 0.069 0.148 0.506 0.76 Finland 52 0.462 0.019 0.000 0.000 0.000 0.288 0.019 0.212 0.713 1.90 France 0.000232 Alsace 126 0.595 0.024 0.000 0.016 0.008 0.040 0.008 0.310 0.646 1.38 Aquitaine 116 0.612 0.034 0.000 0.017 0.000 0.043 0.009 0.284 0.626 1.26 Auvergne 102 0.725 0.039 0.000 0.029 0.010 0.020 0.000 0.176 0.474 0.67 Burgundy 168 0.702 0.024 0.000 0.006 0.000 0.060 0.006 0.202 0.507 0.77 Brittany 582 0.744 0.009 0.024 0.017 0.003 0.064 0.002 0.137 0.444 0.60 0.000343 Centre 218 0.716 0.050 0.000 0.023 0.000 0.023 0.000 0.188 0.486 0.71 Champagne 182 0.665 0.049 0.000 0.016 0.000 0.055 0.005 0.209 0.556 0.94 Franche-Comt ´ e 118 0.746 0.085 0.000 0.085 0.025 0.059 0.000 0.000 0.431 0.56 Languedoc 90 0.700 0.022 0.011 0.033 0.000 0.044 0.000 0.189 0.511 0.78 Llimousin 44 0.545 0.023 0.000 0.068 0.000 0.023 0.023 0.318 0.705 1.83 Loire Valley 308 0.737 0.006 0.019 0.013 0.003 0.032 0.000 0.188 0.457 0.63 Lorraine 286 0.717 0.031 0.000 0.000 0.000 0.042 0.000 0.210 0.486 0.70 Lower Normandie 174 0.644 0.017 0.023 0.017 0.000 0.069 0.000 0.230 0.585 1.06 Midi-Pyr ´ en ´ ees 114 0.649 0.035 0.000 0.018 0.009 0.018 0.000 0.272 0.580 1.03 Nord 468 0.660 0.019 0.004 0.015 0.002 0.053 0.006 0.239 0.563 0.97 Paris Region 830 0.643 0.027 0.007 0.010 0.012 0.035 0.000 0.266 0.585 1.06 Picardie 200 0.650 0.040 0.000 0.040 0.010 0.080 0.000 0.180 0.574 1.01 Poitou 100 0.770 0.030 0.000 0.020 0.000 0.020 0.000 0.160 0.408 0.51 Provence- Cote d`Azur 178 0.674 0.028 0.000 0.051 0.017 0.028 0.006 0.197 0.544 0.89 Rhone-Alpes 668 0.668 0.036 0.001 0.027 0.009 0.018 0.009 0.232 0.552 0.92 Upper Normandie 248 0.645 0.020 0.008 0.012 0.004 0.048 0.004 0.258 0.584 1.05 Germany Baden-W ¨ urttemberg 59 0.763 0.000 0.000 0.034 0.000 0.051 0.102 0.051 0.412 0.52 Bavaria 177 0.740 0.017 0.017 0.000 0.000 0.040 0.011 0.175 0.453 0.62 Berlinc 132 0.773 0.015 0.000 0.023 0.000 0.038 0.015 0.136 0.403 0.50 Bremend 74 0.689 0.014 0.014 0.000 0.000 0.054 0.014 0.216 0.528 0.84 Lower Saxony 198 0.803 0.005 0.005 0.015 0.000 0.015 0.030 0.126 0.355 0.41 North-Rhine/ Westphalia 174 0.736 0.006 0.006 0.000 0.006 0.069 0.034 0.144 0.458 0.63 Saxonye 83 0.639 0.012 0.012 0.024 0.000 0.036 0.036 0.241 0.594 1.10 Rhineland-Palatinaf 59 0.525 0.017 0.000 0.051 0.000 0.085 0.068 0.254 0.721 1.99 Greece Ipiros/Ionian Islands 46 0.609 0.000 0.000 0.043 0.000 0.087 0.043 0.217 0.632 1.30 Peloponese/Attica 89 0.573 0.000 0.022 0.045 0.000 0.146 0.045 0.169 0.667 1.52 Thesalia/Macedonia/ Thrace 61 0.672 0.066 0.000 0.033 0.000 0.033 0.082 0.115 0.543 0.89 Hungary 57 0.439 0.018 0.000 0.018 0.018 0.070 0.018 0.421 0.811 3.43 Italy Abruzzo 66 0.500 0.061 0.000 0.091 0.076 0.030 0.000 0.242 0.739 2.19 Basilicata 75 0.467 0.107 0.000 0.067 0.027 0.067 0.013 0.253 0.769 2.61 Calabria 149 0.430 0.034 0.000 0.047 0.020 0.054 0.034 0.383 0.813 3.46 distances were similar (r ¼ 0.147, P ¼ 0.116 and after controlling for geographical distance r ¼ 0.054, P ¼ 0.296). Login to comment 68 ABCC7 p.Gly551Asp ABCC7 p.Gly551Asp ABCC7 p.Trp1282* ABCC7 p.Trp1282* ABCC7 p.Asn1303Lys ABCC7 p.Gly542* ABCC7 p.Gly542* Table 1 (continued) Population 2N F508del G542X G551D N1303K W1282X Rare Other Unknown Hmax Ymax Incidence Referencesa Campania 223 0.610 0.040 0.000 0.067 0.018 0.040 0.004 0.220 0.623 1.25 Emilia-Romagna 242 0.541 0.058 0.000 0.025 0.008 0.050 0.000 0.318 0.704 1.82 0.000170 Friuli 24 0.375 0.125 0.000 0.042 0.042 0.083 0.083 0.250 0.855 4.85 Lazio 236 0.462 0.030 0.000 0.093 0.013 0.034 0.013 0.356 0.778 2.75 Liguria 44 0.591 0.114 0.000 0.023 0.000 0.045 0.000 0.227 0.646 1.38 Lombardia 399 0.499 0.038 0.000 0.038 0.010 0.090 0.050 0.276 0.743 2.24 Marche 144 0.389 0.056 0.000 0.083 0.014 0.063 0.007 0.389 0.841 4.29 Molise 27 0.481 0.037 0.000 0.074 0.000 0.037 0.000 0.370 0.775 2.70 Piemonte 117 0.675 0.034 0.000 0.000 0.000 0.043 0.017 0.231 0.544 0.89 Puglia 245 0.543 0.053 0.000 0.073 0.000 0.041 0.012 0.278 0.698 1.77 Sardegna 141 0.582 0.057 0.000 0.028 0.000 0.028 0.142 0.163 0.641 1.35 Sicilia 387 0.525 0.062 0.000 0.034 0.023 0.067 0.021 0.269 0.719 1.97 Toscana 191 0.508 0.042 0.000 0.037 0.010 0.031 0.005 0.366 0.740 2.21 Trentino 113 0.513 0.027 0.009 0.009 0.009 0.204 0.053 0.177 0.718 1.96 Umbria 37 0.676 0.081 0.000 0.027 0.000 0.027 0.000 0.189 0.545 0.90 Veneto 552 0.449 0.014 0.000 0.031 0.000 0.188 0.033 0.284 0.785 2.87 0.000370 Ireland Republic of Ireland 509 0.727 0.010 0.069 0.004 0.000 0.037 0.014 0.139 0.467 0.65 0.000684 Northern Ireland 876 0.619 0.021 0.045 0.001 0.000 0.063 0.047 0.205 0.612 1.19 0.000553 52 Israel 367 0.322 0.054 0.000 0.030 0.362 0.065 0.082 0.084 0.754 2.39 0.000304 Lebanon 40 0.350 0.000 0.000 0.100 0.200 0.025 0.225 0.100 0.794 3.04 0.000390 53 Netherlands 1442 0.744 0.013 0.001 0.009 0.007 0.072 0.019 0.135 0.444 0.60 0.000252 Norway 168 0.667 0.006 0.012 0.006 0.000 0.071 0.000 0.238 0.555 0.93 0.000152 Poland 444 0.662 0.023 0.007 0.020 0.002 0.043 0.020 0.223 0.560 0.96 Portugal Faro/Beja 25 0.680 0.000 0.000 0.000 0.000 0.040 0.000 0.280 0.547 0.90 Lisboag 100 0.480 0.030 0.000 0.000 0.000 0.080 0.060 0.350 0.767 2.57 Setubal/Evora 33 0.485 0.000 0.000 0.000 0.000 0.121 0.091 0.303 0.767 2.57 Russia 445 0.618 0.007 0.002 0.004 0.004 0.031 0.031 0.301 0.617 1.22 0.000051 Slovakia 254 0.559 0.075 0.000 0.035 0.016 0.075 0.016 0.224 0.680 1.62 Spain Andalucı´a 314 0.538 0.086 0.013 0.013 0.013 0.083 0.096 0.159 0.694 1.73 Arago´n 65 0.523 0.031 0.000 0.015 0.000 0.123 0.138 0.169 0.708 1.86 Castilla la Mancha 69 0.478 0.058 0.000 0.043 0.000 0.014 0.029 0.377 0.771 2.63 Paı´s Valencia` 125 0.464 0.104 0.000 0.056 0.000 0.096 0.040 0.240 0.771 2.63 Castilla Leo´n/ La Rioja 187 0.604 0.048 0.000 0.011 0.000 0.102 0.107 0.128 0.623 1.24 54 Catalonia 109 0.642 0.055 0.000 0.037 0.009 0.083 0.064 0.110 0.582 1.05 0.000187 Extremadura 63 0.460 0.048 0.000 0.016 0.000 0.079 0.127 0.270 0.776 2.72 Galicia 93 0.624 0.097 0.000 0.011 0.000 0.161 0.075 0.032 0.596 1.11 Madrid 51 0.510 0.059 0.020 0.039 0.000 0.059 0.020 0.294 0.742 2.23 Murcia 40 0.250 0.125 0.000 0.025 0.025 0.175 0.200 0.200 0.889 6.74 Basque Country 31 0.710 0.000 0.000 0.000 0.000 0.065 0.097 0.129 0.497 0.74 Sweden 165 0.733 0.006 0.000 0.000 0.000 0.103 0.085 0.073 0.448 0.60 0.000130 Switzerland 95 0.432 0.032 0.000 0.011 0.000 0.263 0.168 0.095 0.732 2.11 Tunisia 78 0.179 0.090 0.000 0.064 0.026 0.128 0.115 0.397 0.941 14.51 55 Turkey 263 0.274 0.038 0.000 0.042 0.004 0.087 0.114 0.441 0.907 8.44 56 Ukraine 396 0.543 0.000 0.005 0.000 0.000 0.018 0.000 0.434 0.706 1.84 57 Total 29131 0.674 0.025 0.015 0.017 0.009 0.053 0.024 0.182 0.586 1.06 N, sample size (in number of CF chromosomes); F508del, G542X, G551D, 1303K, and W1282X, relative frequencies of the main mutations; rare, relative frequency of mutations not listed in Table 2 of reference 58; other, relative frequency of mutations listed in Table 2 of reference 58. unknown, fraction of chromosomes asociated to disease bearing unidentified mutations. Login to comment 86 ABCC7 p.Gly542* However, the effects of the two parameters are not equivalent: Figure 1 Geographical distribution (a) and spatial autocorrelogram (b) of F508del mutation in 94 middle Eastern, North African, and European populations. The X-axis represents geographic distance between samples; the Y-axis represents Moran`s index; a single asterisk (n) denotes Po0.05; double asterisks (nn) denote Po0.01. Figure 2 Geographical distribution (a) and spatial autocorrelogram (b) of the G542X mutation in 94 middle Eastern, North African, and European populations. The X-axis represents geographic distance between samples; the Y-axis represents Moran`s index; a single asterisk (n) denotes Po0.05; double asterisks (nn) denote Po0.01. for any Ne, doubling it would double y; for a typical CF allele frequency f0 ¼ 0.02, doubling it would mean just a 2% reduction in y. Login to comment 94 ABCC7 p.Gly551Asp In this process, selection will pull up the frequency of any mutant allele as long as it confers a selective advantage to the heterozygote, that is, as discussed above, any CF causing mutation. Thus, the initial spectrum of CF causing mutations is that found when the selection process started; selection will increase the frequency of those mutations already present in the different populations, and, in the end, the total frequency of the mutant class may be similar Figure 3 Geographical distribution (a) and spatial autocorrelogram (b) of the G551D mutation in 94 middle Eastern, North African, and European populations. The X-axis represents geographic distance between samples; the Y-axis represents Moran`s index; a single asterisk (n) denotes Po0.05; double asterisks (nn) denote Po0.01. Figure 4 Geographical distribution (a) and spatial autocor- relograms (b) of the N1303 K mutation in 94 middle Eastern, North African, and European populations. The X-axis represents geographic distance between samples; the Y-axis represents Moran`s index; a single asterisk (n) denotes Po0.05; double asterisks (nn) denote Po0.01. across populations (as long as the selection pressure is similar), but the actual mutations that fill up this frequency determined by selection may be different across populations. Login to comment 104 ABCC7 p.Trp1282* Thus, a correlation between distances based on CF mutation frequencies and distances based on other polymorphisms, once the shared effects of geographic distance are Figure 5 Geographical distribution (a) and spatial autocorrelogram (b) of the W1282X mutation in 94 middle Eastern, North African, and European populations. The X-axis represents geographic distance between samples; the Y-axis represents Moran`s index; a single asterisk (n) denotes Po0.05; double asterisks (nn) denote Po0.01. Figure 6 Spatial autocorrelogram of genetic diversity calculated as expected heterozygosity of CF mutations (a) and of genetic diversity calculated as Y of CF mutations (b). Login to comment