Open Access

The origin of Eastern European Jews revealed by autosomal, sex chromosomal and mtDNA polymorphisms

Biology Direct20105:57

DOI: 10.1186/1745-6150-5-57

Received: 21 September 2010

Accepted: 6 October 2010

Published: 6 October 2010

Abstract

Background

This study aims to establish the likely origin of EEJ (Eastern European Jews) by genetic distance analysis of autosomal markers and haplogroups on the X and Y chromosomes and mtDNA.

Results

According to the autosomal polymorphisms the investigated Jewish populations do not share a common origin, and EEJ are closer to Italians in particular and to Europeans in general than to the other Jewish populations. The similarity of EEJ to Italians and Europeans is also supported by the X chromosomal haplogroups. In contrast according to the Y-chromosomal haplogroups EEJ are closest to the non-Jewish populations of the Eastern Mediterranean. MtDNA shows a mixed pattern, but overall EEJ are more distant from most populations and hold a marginal rather than a central position. The autosomal genetic distance matrix has a very high correlation (0.789) with geography, whereas the X-chromosomal, Y-chromosomal and mtDNA matrices have a lower correlation (0.540, 0.395 and 0.641 respectively).

Conclusions

The close genetic resemblance to Italians accords with the historical presumption that Ashkenazi Jews started their migrations across Europe in Italy and with historical evidence that conversion to Judaism was common in ancient Rome. The reasons for the discrepancy between the biparental markers and the uniparental markers are discussed.

Reviewers

This article was reviewed by Damian Labuda (nominated by Jerzy Jurka), Kateryna Makova and Qasim Ayub (nominated by Dan Graur).

Background

The genetic affinities of the Jewish populations have been studied since the early days of genetics, yet the origin of these populations is still obscure. Some of the studies, trying to establish the origins of the Jewish populations with autosomal markers, claimed that the Jewish populations have a common origin, but others concluded that the Jews are a very diverse group. This corpus of studies has already been critically reviewed [1].

The origin of Eastern European Jews, (EEJ) by far the largest and most important Ashkenazi population, and their affinities to other Jewish and European populations are still not resolved. Studies that compared them by genetic distance analysis of autosomal markers to European Mediterranean populations revealed that they are closer to Europeans than to other Jewish populations [13].

EEJ are the largest and most investigated Jewish community, yet their history as Franco-German Jewry is known to us only since their appearance in the 9th century, and their subsequent migration a few hundred years later to Eastern Europe [4, 5]. Where did these Jews come from? It seems that they came to Germany and France from Italy [58]. It is also possible that some Jews migrated northward from the Italian colonies on the northern shore of the Black Sea [9]. All these Jews are likely the descendents of proselytes. Conversion to Judaism was common in Rome in the first centuries BC and AD. Judaism gained many followers among all ranks of Roman Society [1013].

The aim of this study is to establish the likely origin of this major Jewish population by using a larger dataset of autosomal markers, and compare the results to analyses based on the available data for the X and Y chromosomes and for mtDNA.

Methods

Six Jewish populations: EEJ, Moroccan Jews, Iraqi Jews. Iranian Jews, Yemenite Jews and Ethiopian Jews, which have been studied for all the autosomal markers used in this study, are included in the analysis. EEJ are defined on the basis of history as those Jews originating from the areas of the Polish-Lithuanian Kingdom and their descendants in bordering regions, encompassing the territories of Russia, Poland, the Baltic States, Belarus, Moldavia, Moldova (the north-eastern part of Romania) and the Ukraine. The Data on the non-autosomal markers were also available for other Jewish populations: Bulgarian Jews (X, mtDNA), Turkish Jews (X, mtDNA), Tunisian Jews (mtDNA), Libyan Jews (Y, mtDNA) and Djerban Jews (Y).

The seventeen autosomal markers are: AK, ADA, PGM1, PGD, ACP, ESD, GPT, HP, GC, J311 MspI & MetH TaqI (both on chromosome 7 near the CF locus), FV G1691A, FII G20210A, MTHFR C677T, CBS 844ins68, ACE ID and PAH XmnI. All the markers are unique-event-polymorphisms, and apart from two insertions (CBS 844ins68, ACE ID) are all SNPs. The first nine markers are polymorphisms of red cell enzymes and serum proteins, and were typed mostly by protein electrophoresis, but the variation at the protein level is directly related in a 1:1 manner to the SNP variation at the DNA level. Indeed, some of the results for the Jewish populations were obtained by PCR methods [1, 14]. The polymorphism of the remaining eight markers can only be detected at the DNA level. J311 MspI and MetH TaqI were typed in all the populations including the Israeli populations (unpublished results) by Southern blotting and hybridization [15, 16]. The other 6 markers were typed in the Israeli populations by PCR methods. The data on FV G1691A, FII G20210A, MTHFR C677T and CBS 844ins68 have already been published [3, 17]. The data on ACE ID and PAH XmnI are still unpublished. These polymorphisms were typed according to the methods of Rigat et al. [18] and Goltsov et al. [19] respectively. Allele frequencies for all the populations are given in Additional file 1: tables S1-4. Table S2 (Additional file 1) presents four markers on both sides of the CF locus. Because of the linkage between them, I chose to use only the two most distal markers, which are separated by a few centimorgans. Haplogroup frequencies of the non-recombining Y chromosome (NRY), the X chromosome (dystrophin locus, dys44, on Xp21.3) and mtDNA are given in Additional file 1: tables S5, S6 and S7 respectively.

Gower (cited in [20]) recommends, that for microevolutionary studies, when sample sizes are quite variable and gene frequencies do not differ greatly, Sanghvi's G2 [21] would be the most appropriate, and this is the measure I used. Distances were also calculated with Nei's [22] formula and the results were very similar (r = 0.990, genetic distance matrix not shown). The neighbor joining tree was computed by PHYLIP 3.66. Since it does not calculate Sanghvi's G2, I used Reynolds et al. distance [23], which is also based on the assumption that gene frequencies change by genetic drift alone, solely for the calculation of the tree (genetic distance matrix not shown). The significance of nodes in the tree and the standard errors of the genetic distances were computed by bootstrapping 10,000 times. Multidimensional scaling plots and Mantel tests for correlation between genetic distance matrices and between them and matrices of geographic distances were computed by NTSYS 1.70. Geographic distances were calculated as great circle distances between the capitals of the countries of origin of the populations (Warsaw was chosen for EEJ). Mantel test significance was assessed by 10,000 permutations.

Results

The autosomal genetic distances (table 1) do not show any particular resemblance between the Jewish populations. EEJ are closer to Italians in particular and to Europeans in general than to the other Jewish populations. All of the distances, apart from one, differ from zero by more than twice their standard error. A difference between two distances can be considered meaningful, if it is more than twice their largest standard error. The differences between the distance of EEJ from Italians and their distances from the other Jewish populations are meaningful according to this criterion, and the same is also true for all the Non-Jewish populations except for Greeks and Russians. In fact the distance between EEJ and Italians is the smallest distance in the matrix. A multidimensional scaling plot of the genetic distance matrix (figure 1) captures the proximity of EEJ to Italians and other European populations. The same is also true for the neighbor joining tree (figure 2). It should be noted that multidimensional scaling plots are a way to present graphically the intricate relationships of genetic distance matrices. As such they are necessarily less accurate than the matrices on which they are based. In order to understand the genetic affinities of a particular population, one must examine its distances in the matrix itself, not in the plot. The same also applies to the neighbor joining tree. The bootstrap values indicate the robustness of the clustering, but not the significance of individual genetic distances.
Table 1

Autosomal genetic distance matrix (×1000) (standard errors above the diagonal)

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1) EEJ

 

103

94

52

180

348

76

57

38

11

35

73

42

94

58

2) Iraqi Jews

277

 

68

131

87

330

58

147

117

87

64

125

138

141

99

3) Iranian Jews

275

218

 

131

118

391

125

112

97

105

119

149

142

146

139

4) Moroccan Jews

243

330

325

 

148

263

105

115

89

36

66

71

55

80

78

5) Yemenite Jews

498

366

335

447

 

263

87

104

92

162

133

123

114

155

168

6) Ethiopian Jews

1240

1127

1004

809

696

 

233

322

333

349

396

373

341

381

463

7) Palestinians

277

223

425

298

323

972

 

43

44

60

65

131

63

87

122

8) Turks

170

243

305

314

400

1244

182

 

15

54

56

113

117

64

68

9) Greeks

105

270

316

311

356

1246

202

56

 

36

38

83

76

42

52

10) Italians

44

243

255

167

452

1083

231

157

101

 

25

48

34

81

40

11) Germans

131

268

294

237

511

1067

299

179

148

71

 

25

19

34

12

12) British

238

395

373

239

592

977

434

332

267

151

53

 

41

46

13

13) French

144

339

398

216

545

974

288

265

192

91

48

75

 

59

33

14) Russians

230

420

430

289

513

1144

375

175

139

193

102

112

134

 

25

15) Poles

195

405

365

264

600

1204

465

255

197

139

50

46

102

66

 
https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig1_HTML.jpg
Figure 1

A multidimensional scaling plot of the autosomal genetic distance matrix excluding Ethiopian Jews. Stress = 0.100. Populations names are: EEJ - Eastern European Jews, IqJ - Iraqi Jews, InJ - Iranian Jews, MJ - Moroccan Jews, YJ - Yemenite Jews, Pa - Palestinians, Tur - Turks, Gr - Greeks, It - Italians, Ge - Germans, Br - British, Fr - French, Ru - Russians, Po - Poles. Squares represent Jews and circles non-Jews. Colour indicates geographic region: red - Europe, green - Eastern Mediterranean, blue - Iran-Iraq, purpule - Arabian peninsula, yellow - North-Africa.

https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig2_HTML.jpg
Figure 2

A neighbor joining tree based on the autosomal polymorphisms. A number next to a node indicates the majority bootstrap support for that node out of 10,000 repetitions.

X-chromosomal haplogroups demonstrate the same relatedness of EEJ to Italians and other Europeans (table 2, figure 3). In contrast, according to the Y-chromosomal haplogroups EEJ are closest to the non-Jewish populations of the Eastern Mediterranean (table 3, figure 4). MtDNA shows a mixed pattern where EEJ are about equally close to Moroccan Jews, Palestinians, Italians and Bulgarian Jews, but overall are more distant from most populations and hold a marginal position in the MDS plot, rather than a central one like in the other plots (table 4, figure 5).
Table 2

X chromosomal genetic distance matrix (×1000)

1) EE Jews

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

2) Iraqi Jews

402

                

3) Iranian Jews

497

351

               

4) Moroccan Jews

302

211

480

              

5) Yemenite Jews

555

406

512

439

             

6) Ethiopian Jews

533

617

683

676

709

            

7) Bulgarian Jews

409

276

440

299

611

672

           

8) Turkish Jews

288

519

474

452

403

599

625

          

9) Palestinians

573

506

512

464

666

754

350

712

         

10) Italians

223

374

488

184

493

741

337

395

478

        

11) Germans

263

483

497

358

715

701

318

518

502

282

       

12) Poles

233

482

531

336

570

741

406

476

484

235

266

      

13) Basques

311

597

548

513

827

702

378

479

503

369

349

359

     

14) Spaniards

252

385

457

313

609

554

297

406

487

334

315

365

337

    

15) French

313

332

454

284

649

706

206

401

483

285

308

347

249

244

   

16) Bretons

186

410

483

386

615

611

288

376

492

288

238

246

234

219

162

  

17) Ethiopians Oromo

771

918

892

906

977

1243

847

745

1002

753

816

797

840

840

717

727

 

18) Ethiopians Amhara

490

618

619

504

471

798

695

433

702

449

614

490

680

579

555

524

791

https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig3_HTML.jpg
Figure 3

A multidimensional scaling plot of the X-chromosomal genetic distance matrix. Stress = 0.125. Populations names are: EEJ - Eastern European Jews, IqJ - Iraqi Jews, InJ - Iranian Jews, MJ - Moroccan Jews, YJ - Yemenite Jews, EJ - Ethiopian Jews, BJ - Bulgarian Jews, TrJ - Turkish Jews, Pa - Palestinians, It - Italians, Ge - Germans, Po - Poles, Fr - French, Bre - Bretons, Sp - Spaniards, Ba - Basques, EO - Ethiopians Oromo, EA - Ethiopians Amhara. Squares represent Jews and circles non-Jews. Colour indicates geographic region: red - Europe, green - Eastern Mediterranean, blue - Iran-Iraq, purpule - Arabian peninsula, yellow - North-Africa, brown - Ethiopia.

Table 3

Y chromosomal genetic distance matrix (×1000)*

1) EEJ

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

2) IqJ

341

                                     

3) InJ

574

236

                                    

4) MJ

245

335

764

                                   

5) LJ

242

626

863

465

                                  

6) DJ

582

813

1025

667

402

                                 

7) YJ

185

244

472

304

418

545

                                

8) EJ

1296

1373

1444

1386

1308

1685

1278

                               

9) Pa

192

469

728

362

351

411

215

1254

                              

10) It

357

720

1022

332

538

928

669

1427

611

                             

11) Ge

815

1209

1356

933

1194

1614

1179

1644

1196

424

                            

12) Br

1233

1504

1801

1060

1494

1727

1475

1860

1474

499

398

                           

13) Fr

754

1053

1177

749

1034

1299

971

1622

1043

307

399

346

                          

14) Ru

1150

1303

1299

1384

1504

1811

1498

1737

1406

1159

595

1364

1255

                         

15) Po

1030

1388

1430

1316

1359

1740

1388

1687

1337

971

388

1119

1058

185

                        

16) SC

834

1212

1179

1216

1058

1516

1161

1466

1021

890

511

1166

910

676

615

                       

17) Alb

349

838

844

677

514

1099

730

1316

622

366

441

993

613

749

618

341

                      

18) Gr

380

904

1064

658

512

1104

782

1312

686

255

311

819

498

774

563

531

136

                     

19) Ma

517

965

1135

792

713

1337

887

1323

783

440

266

841

592

667

500

222

144

138

                    

20) Ro

570

1029

1221

833

745

1193

942

1476

819

502

409

828

620

889

715

198

274

341

180

                   

21) Tur

159

447

700

265

413

696

460

1421

438

217

599

1008

622

899

891

845

352

303

490

535

                  

22) Irn

494

424

717

369

727

805

601

1756

820

478

916

1134

813

1233

1285

1376

869

766

994

990

270

                 

23) Irs

311

509

621

418

516

675

538

1528

587

566

860

1410

1042

874

896

991

529

592

781

773

217

370

                

24) Iq

245

516

628

374

406

444

320

1422

265

510

970

1397

915

1127

1113

1051

557

550

754

859

270

541

315

               

25) Cy

127

448

791

196

176

534

246

1241

240

395

1064

1239

799

1539

1359

1099

531

531

714

699

326

595

486

378

              

26) Sy

152

464

637

398

322

421

336

1304

177

508

947

1429

941

1043

1045

911

481

487

655

712

197

562

277

114

329

             

27) Lb

71

256

480

281

334

493

173

1330

191

426

925

1288

739

1213

1146

956

492

494

651

694

180

416

354

215

211

116

            

28) Jo

183

513

704

373

451

489

141

139

123

561

1026

1296

840

1365

1247

988

577

661

758

758

410

765

578

246

266

255

204

           

29) SA

448

580

605

606

724

565

372

1302

339

924

1286

1728

1256

1302

1357

1208

889

962

1115

1103

553

757

420

254

610

262

380

334

          

30) Qa

647

819

805

973

948

696

454

1483

518

1196

1405

1769

1360

1506

1450

1351

1132

1216

1327

1225

903

1081

690

499

800

546

623

392

153

         

31) UA

324

457

419

513

676

712

266

1304

367

818

1106

1575

1125

1233

1206

1050

671

825

956

954

488

694

365

290

500

305

315

295

130

249

        

32) Om

477

626

625

651

745

765

417

1144

366

955

1223

1754

1313

1146

1227

1097

804

880

1001

1086

586

900

524

289

653

303

474

381

99

279

157

       

33) Ye

769

913

1000

854

920

586

483

1438

383

1240

1664

1825

1407

1816

1734

1502

1252

1310

1412

1367

1088

1341

1066

542

768

645

710

369

365

238

475

410

      

34) Eg

185

365

655

289

355

742

205

988

183

598

1128

1481

1068

1305

1285

1036

593

647

728

839

384

724

502

350

197

260

242

283

430

677

364

358

672

     

35) Mo

764

999

1220

944

611

1264

801

933

715

1067

1543

1715

1365

1775

1652

1258

888

938

991

1125

1092

1492

1251

1098

559

970

911

903

1157

1282

1055

996

1105

454

    

36) Alg

437

641

1001

456

502

958

425

1101

350

812

1325

1458

1240

1632

1487

1208

801

874

907

999

793

1120

909

697

289

654

578

487

783

895

643

676

735

215

272

   

37) Tun

456

676

952

522

580

911

345

1153

316

893

1335

1485

1179

1653

1501

1250

853

919

968

1078

854

1193

969

604

332

626

554

356

643

664

528

551

497

251

379

71

  

38) EO

1089

1203

1319

1207

1056

1659

1090

452

1021

1332

1648

1851

1582

1701

1686

1387

1134

1186

1191

1418

1330

1726

1442

1319

967

1187

1186

1231

1197

1394

1190

998

1319

651

323

569

666

 

39) EA

826

932

1018

907

969

1107

622

555

569

1274

1653

1857

1497

1715

1692

1425

1170

1261

1290

1430

1154

1516

1172

819

790

803

844

676

617

693

677

500

581

449

638

473

397

346

*- For populations names see figure 4.

https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig4_HTML.jpg
Figure 4

A multidimensional scaling plot of the Y-chromosomal genetic distance matrix. Stress = 0.133. Populations names are: EEJ - Eastern European Jews, IqJ - Iraqi Jews, InJ - Iranian Jews, MJ - Moroccan Jews, LJ - Libyan Jews, DJ - Djerban Jews, YJ - Yemenite Jews, EJ - Ethiopian Jews, Pa - Palestinians, It - Italians, Fr - French, Br - British, Ge - Germans, Ru - Russians, Po - Poles, SC - Serbo-Croats, Alb - Albanians, Gr - Greeks, Ma - Macedonians, Ro - Romanians, Tur - Turks, Inn - Iranians-North, Ins - Iranians-South, Iq - Iraqis, Cy - Cypriots, Sy - Syrians, Lb - Lebanese, Jo - Jordanians, SA - Saudi-Arabians, Qa - Qataris, UA - United Arab Emirates, Om - Omanis, Ye - Yemenites, Eg - Egyptians, Mo - Moroccans, Alg - Algerians, Tun - Tunisians, EO - Ethiopians Oromo, EA - Ethiopians Amhara. Squares represent Jews and circles non-Jews. Colour indicates geographic region: red - Europe, green - Eastern Mediterranean, blue - Iran-Iraq, purpule - Arabian peninsula, yellow - North-Africa, brown - Ethiopia.

Table 4

mtDNA genetic distance matrix (×1000)*

1) EEJ

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

2) IqJ

916

                             

3) IqJ

892

627

                            

4) MJ

400

1020

814

                           

5) LJ

1016

1303

770

741

                          

6) TnJ

908

1336

973

438

487

                         

7) BJ

453

817

676

381

727

605

                        

8) TrJ

591

813

445

287

605

530

300

                       

9) YJ

1020

1058

1257

1124

1349

1323

1287

1264

                      

10) EJ

1685

1789

1794

1882

1701

1662

1844

1916

1251

                     

11) Pa

417

976

941

674

1005

812

501

690

843

1382

                    

12) Tur

531

478

419

499

767

795

406

379

985

1726

556

                   

13) Gr

540

676

443

302

680

465

365

228

1138

1771

627

199

                  

14) It

437

698

516

324

705

574

295

226

1247

1759

582

237

135

                 

15) Ge

606

745

533

360

791

528

360

275

1299

1867

701

357

112

176

                

16) Fr

504

836

646

334

814

590

379

316

1374

1880

710

379

173

126

93

               

17) Br

610

761

562

341

822

602

454

295

1310

1927

806

410

166

220

70

84

              

18) Ru

650

785

510

411

716

534

432

300

1355

1854

697

303

124

148

105

96

142

             

19) Po

687

749

585

453

810

561

428

308

1414

1886

752

355

156

167

77

100

126

66

            

20) Sp

557

778

680

370

843

657

445

339

1294

1719

712

368

251

181

214

167

207

184

206

           

21) Cy

520

732

539

374

626

600

302

335

1141

1689

616

269

244

199

374

363

425

370

407

364

          

22) Lb

543

736

618

502

729

633

390

456

1095

1520

383

233

348

288

485

463

554

425

482

412

270

         

23) Sy

581

431

564

676

891

950

580

576

820

1465

463

283

427

444

613

659

659

609

659

609

412

339

        

24) In

583

553

464

681

879

995

561

571

888

1697

576

209

422

369

568

579

613

513

576

543

397

425

341

       

25) Jo

591

647

461

672

816

788

562

490

892

1329

419

387

449

370

613

616

711

563

614

532

355

328

285

405

      

26) SA

631

731

799

863

964

1018

745

801

745

1123

478

579

679

668

836

875

841

849

898

805

567

561

416

503

486

     

27) Ye

1064

1393

1351

1217

1310

1427

1206

1289

897

830

871

1078

1205

1154

1343

1315

1383

1314

1383

1254

1110

1125

949

943

898

770

    

28) Eg

634

721

853

751

967

895

692

763

791

985

374

556

656

620

835

868

926

801

869

714

574

449

365

572

270

398

782

   

29) MoA

736

1030

942

659

868

780

645

615

1196

1238

556

700

611

513

666

625

690

608

638

487

526

559

638

752

427

678

888

416

  

30) MoB

674

948

851

568

880

728

595

511

1208

1386

550

626

494

415

504

450

507

470

486

348

499

535

595

701

442

679

1015

495

89

 

31) Et

1394

1578

1679

1543

1492

1443

1541

1649

1008

300

1051

1470

1517

1470

1626

1612

1685

1604

1649

1461

1357

1279

1147

1406

1015

847

751

607

888

1032

*- For populations names see figure 5.

https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig5_HTML.jpg
Figure 5

A multidimensional scaling plot of the mtDNA genetic distance matrix. Stress = 0.110 for the outer plot and 0.161 for the inner one. Populations names are: EEJ - Eastern European Jews, IqJ - Iraqi Jews, InJ - Iranian Jews, MJ - Moroccan Jews, LJ - Libyan Jews, TnJ - Tunisian Jews, BJ - Bulgarian Jews, TrJ - Turkish Jews, YJ - Yemenite Jews, EJ - Ethiopian Jews, Pa - Palestinians, It - Italians, Fr - French, Br - British, Ge - Germans, Ru - Russians, Po - Poles, Sp - Spaniards, Gr - Greeks, Tur - Turks, In - Iranians, Cy - Cypriots, Sy - Syrians, Lb - Lebanese, Jo - Jordanians, SA - Saudi-Arabians, Ye - Yemenites, Eg - Egyptians, MoA - Moroccan Arabs, MoB - Moroccan Berbers, Et - Ethiopians. Squares represent Jews and circles non-Jews. Colour indicates geographic region: red - Europe, green - Eastern Mediterranean, blue - Iran-Iraq, purpule - Arabian peninsula, yellow - North-Africa, brown - Ethiopia.

Correlations between genetic distance and geography and between genetic distance matrices based on different markers (excluding the non-Caucasoid populations Ethiopians and Ethiopian Jews) are shown in table 5. The autosomal polymorphisms have a very high correlation (0.789) with geography in contrast to the more moderate correlations of the X-chromosomal, Y-chromosomal and mtDNA polymorphisms (0.540, 0.395 and 0.641 respectively). In order to compare two competing theories regarding the origin of EEJ, their geographic distances were computed as if they originated from Italy or Israel, i.e. the great circle distances for EEJ were calculated not between Warsaw and other capitals, but between Rome or Jerusalem and other capitals. The correlation between the autosomal genetic distance matrix and geography was slightly higher, 0.804, for Rome but dropped to 0.694 for Jerusalem. Autosomal distances are much better correlated with mtDNA distances (0.826) and with X-chromosomal distances (0.732) than with Y-chromosomal distances (0.437). The correlations between the mtDNA and X-chromosomal matrices and the Y-chromosomal matrix are rather poor (0.206 and 0.241 respectively) and insignificant. When the correlations with geography were only calculated for the genetic distances of EEJ and not for the entire matrix (table 6), the same trends emerge with the autosomal correlation from Rome reaching a high of 0.926. The correlations from Jerusalem are negative for the autosomes, the X chromosome and mtDNA. The reverse is true for the Y chromosome.
Table 5

Correlation and significance level between genetic distance matrices and between genetic distance and geography

 

Autosomes

Y

mtDNA

Geography

 

r

p

r

p

r

p

r

p

Autosomes*

      

0.789

0.0001

Y*

0.437

0.0021

    

0.395

0.0038

mtDNA*

0.826

0.0001

0.206

0.1200

  

0.641

0.0003

X**

0.732

0.0005

0.241

0.1399

0.633

0.0058

0.540

0.0022

* - Based on the 14 populations (excluding Ethiopian Jews) in the autosomal matrix

** - Based on the 10 populations (excluding Ethiopian Jews) common to all 4 matrices

r = correlation; p = significance level

Table 6

Correlation between the genetic distances of EEJ and geography*

 

Warsaw

Rome

Jerusalem

Autosomes**

0.778

0.926****

-0.149

X***

0.781

0.835

-0.685

Y**

-0.613

-0.213

0.556

mtDNA**

0.471

0.779

-0.190

* - Great circle distances calculated from the three alternatives for their origin

** - Based on the 14 populations (excluding Ethiopian Jews) in the autosomal matrix

*** - Based on the 10 populations (excluding Ethiopian Jews) common to all 4 matrices

**** - When the Italians are removed, the correlation still remains very high, 0.904.

Discussion

The autosomal genetic distance analysis presented here clearly demonstrates that the investigated Jewish populations do not share a common origin. The resemblance of EEJ to Italians and other European populations portrays them as an autochthonous European population. A study conducted in a New York college in the 1920s point to the same Ashkenazi - Italian similarity on basis of physical characteristics. Freshmen were asked before they knew one another to indicate the origin of their fellow students. Forty percent of the Italians were taken to be Ashkenazi Jews, and the same percentage of Ashkenazi Jews was adjudged Italians [24]. EEJ seem to be mainly Italian (Roman) in origin, which is easily understood, considering the historical evidence presented above.

The high correlation between the autosomal genetic distances and geography and the reduced correlation when EEJ are taken to originate from the Land of Israel reinforce the European origin of EEJ. In fact the correlation of the autosomal markers with geography is higher than previously described for 49 classical markers (0.503) or ~300,000 autosomal SNPs (0.661) in Europe [25]. If for comparison, only non-Jewish European populations are included, the correlation is lower, 0.689, but still higher than the above mentioned correlations. It is also interesting to note how using the three geographic alternatives for EEJ, changes the correlation, when only European populations are included. The correlation remains almost the same, 0.679, for Rome but drops to 0.490 and 0.571 for Warsaw and Jerusalem respectively; further emphasizing the correct geographic origin of EEJ within Europe.

Biparental versus uniparental markers

At first sight it seems that there is more than one explanation for the differing results produced by the analysis of the NRY haplogroups. It thus seems possible that EEJ founder population in Rome was composed of exiled Israelite males and local Roman females. In its simple form this clearly contradicts the facts, because both the autosomal and X-chromosomal polymorphisms demonstrate that EEJ do not occupy an intermediate position between European and Middle Eastern populations, but rather a strict European one. From table 1 it is clear that Italians are as close or closer to the other Jewish populations and Palestinians as EEJ. It is possible that once the founder population was established no other males but many females joined it, thus creating a population that is almost entirely European in all genetic aspects apart from its Y chromosomes. Such phenomenon was described for the population of Antioquia, Columbia, where the autosomes point to 79% of European ancestry and only 16% of Amerindian ancestry, whereas according to mtDNA the ancestry is 90% Amerindian and only 2% European (there is also a small African component). Historical records demonstrate that local Amerindian females joined the population only at its beginning, whereas European males joined it also in later periods [26]. The suggestion that the proselyte ancestors of EEJ were almost entirely females does not however accord with what we know about conversion to Judaism [10, 12, 2729].

The inference that the NRY points to a Middle Eastern origin of EEJ is erroneous not only because the Y chromosomal analysis contradicts the analyses based on the other chromosomes, and because the NRY is a single uniparental marker that does not represent the whole history of the population, but also because its smaller effective population size makes it much more vulnerable to severe genetic drift caused by demographic bottlenecks. The demographic histories of three Jewish populations exemplify how different demographic patterns make the uniparental markers more reliable for Iraqi (Babylonian) Jews and Yemenite Jews and less reliable for EEJ. Both Yemenite Jews and Iraqi Jews resemble populations from their regions of origin according to autosomal markers [1, 3, 3032]. Yemenite Jews, who are usually considered a small isolate, were numerous enough to have an independent kingdom in the first centuries AD [33]. They numbered a few hundred thousand in the 12th century AD, and gradually declined; reaching only about 30-40,000 in the beginning of the 20th century [34]. Babylonian Jews numbered more than a million in the first century AD [35], and constituted the majority of the population in the area between the Euphrates and the Tigris in the 2nd-3rd centuries AD [36]. Gilbert [37] estimates that by 600 AD there were 806,000 Jews in Mesopotamia, and according to Sassoon [38] it was inhabited by about a million Jews in the 7th century. In the 14th century the estimates for Baghdad alone range from 70,000 to hundreds thousands [38]. By 1939, 11 years before their emigration, there were 91,000 Jews in Iraq [35]. In contrast, the Jewish population of the Polish-Lithuanian Kingdom (EEJ) went through the opposite process. Their history is one of founder effects, migrations, demographic bottlenecks and finally a rapid expansion. We know nothing about their number in the first millennium, but after their emigration from Italy to Western Europe it is estimated that they numbered 4,000 in 1000 and 20,000 a hundred years later [8]. In 1500 already in Eastern Europe they numbered 10,000-30,000, in 1648 230,000-450,000 and in 1764 750,000 [3941]. In the 19th century because of the partitions of the Polish-Lithuanian Kingdom and the immigrations of Jews to Central and Western Europe and America, the estimation of the number of EEJ becomes more difficult, but there is no doubt that the increase in numbers was impressive, as the number of EEJ under Russian rule alone was 5,200,000 in 1897 [41].

The existence of severe demographic bottlenecks in the history of EEJ has also been suggested by genetic studies of disease-causing-mutations and mtDNA [4246]. The comparison based on this second uniparental marker, mtDNA, may help to resolve from within genetics itself the problem of the Y chromosome reliability for inferring the origin of the male ancestors of EEJ. If the European and Middle Eastern contributions to the gene pool of EEJ were female and male respectively, then comparisons based on mtDNA must place EEJ among other European populations, distant from Middle Eastern populations. The mtDNA analysis presented in this study does not place EEJ among other European populations rather their position is more intermediate and marginal, as can be seen in figure 5 and in figure 6, where autosomal distances are correlated with mtDNA distances. This lends further support to the notion that because of the unique demographic history of EEJ, their uniparental markers were subjected to stronger genetic drift than the biparental markers and thus should not be used to trace their origin.
https://static-content.springer.com/image/art%3A10.1186%2F1745-6150-5-57/MediaObjects/13062_2010_Article_240_Fig6_HTML.jpg
Figure 6

Correlation of autosomal (X axis) and mtDNA (Y axis) distances. Red circles denote EEJ. Most of the mtDNA distances of EEJ are too high relative to their autosomal distances, in contrast to most other distances (r = 0.826), attesting the greater genetic drift, to which the uniparental markers of EEJ were subjected.

The data on the Y chromosome itself also support the unreliability of the uniparental markers for discovering the origin of EEJ. Nebel et al. [47] studied haplogroup R-M17, whose frequency is ~12% in Ashkenazi Jews. By comparing the structure of the STRs network among the various Ashkenazi populations and among the various European non-Jewish populations they reached the conclusion that a single male founder introduced this haplogroup into Ashkenazi Jews in the first millennium. Behar et al. [48] write "It is striking that whereas Ashkenazi populations are genetically more diverse at both the SNP and STR level compared with their European non-Jewish counterparts, they have greatly reduced within-haplogroup STR variability ... This contrasting pattern of diversity in Ashkenazi populations is evidence for a reduction in male effective population size, possibly resulting from a series of founder events and high rates of endogamy within Europe. This reduced effective population size may explain the high incidence of founder disease mutations despite overall high levels of NRY diversity". It is unlikely that EEJ are the descendants of a single population. Admixture coupled with small effective population size and bottlenecks can create the puzzling situation we encounter in the uniparental markers. Thus smaller contributions from several populations, including possibly the original Middle Eastern Jewish population, and a major contribution from Italy combined with the unique demography of EEJ can create the current genetic picture without the need to invoke a major contribution from the Middle East, which contradicts the autosomal and X-chromosomal data.

Comments on previous studies

Some previous studies based on classical autosomal markers concluded that EEJ are a Middle Eastern population with genetic affinities to other Jewish populations. The problems with these studies have been previously discussed in detail [1]. These studies used fewer markers (mostly the less reliable antigenic markers) and failed to include European Mediterranean populations, apart from the discriminant analysis of Carmelli and Cavalli-Sforza [49], which used only four markers and contradicts the results of the later more elaborate discriminant analysis [1], and the genetic distance analysis of Livshits et al. [32], which includes a single European Mediterranean population, Spain. Despite this when a genetic distance analysis was performed, the greater similarity of EEJ to Russians and to a lesser extent to Germans more than to Non-European Jews was evident [32]. In fact Russians were more similar to EEJ than to any Non-Jewish European population in that analysis.

Recently, Cochran et al. [50] used 251 autosomal loci to calculate genetic distances and concluded that "from the perspective of a large collection of largely neutral genetic variation Ashkenazim are essentially European, not Middle Eastern". More recently, thousands of SNPs were used by Need et al. [51] to infer the relationships between Ashkenazi Jews and non-Jewish Europeans and Middle Easterners. They concluded that Ashkenazi Jews lie approximately midway between Europeans and the Middle Easterners, implying that Ashkenazi Jews may contain mixed ancestry from these two regions, and that they are close to the Adygei population from the Caucasus. However these conclusions are ill-founded, because, they used a highly selected set of SNPs, which were selected specifically for the purpose of distinguishing between Ashkenazi Jews and other populations and they inferred the origin of Ashkenazi Jews from principal components analysis (PCA), but as Tian et al. [52] show "PCA results are highly dependent on which population groups are included in the analysis. Thus, there should be some caution in interpreting these results and other results from similar analytic methods with respect to ascribing origins of particular ethnic groups'" Tian et al. [52] also published a table of paired Fst distances based on 10,500 random SNPs, which demonstrates that Ashkenazi Jews are not at all close to the Adygei population, and similarly to what is seen in table 1, their smallest distance is to Italians and then to Greeks. Unlike the assertion of Need et al. [51] on the midway position, and again similarly to what is seen in table 1, Italians and Greeks are closer to the Middle Eastern populations than Ashkenazi Jews.

The same phenomenon is seen in the table of Fst distances of Atzmon et al. [53]. North Italians (Bergamo and Tuscany) are a little closer to the Jewish and Middle Eastern populations than Ashkenazi Jews. The Italians from Tuscany (surprisingly the sample from Bergamo was not used) in Behar et al. [54] are also closer to the Jewish and Middle Eastern populations than Ashkenazi Jews. The Italians from Tuscany are in fact the closest population to Ashkenazi Jews in Behar et al. [54]. There is one sample that is apparently a little closer, what they call Sephardic Jews. Unfortunately this sample is composed of two populations, Turkish Jews and Bulgarian Jews, which should have been studied separately like all other Jewish populations. Bulgarian Jews have been shown in the past based on autosomal classical markers to be closer to EEJ than to populations with Sephardic ancestry and considering their history it was concluded that the Ashkenazi component in their gene pool is at least as large or even larger that the Sephardic component [1]. From both The current study and those of Atzmon et al. [53] and Behar et al. [54] it can be seen that the only Jewish populations that are as close to Ashkenazi Jews as non-Jewish Europeans are those with a significant Sephardic (The descendants of the Jews who were expelled from the Iberian peninsula at the end of the 15th century) component in their gene pool. It is not possible at this stage to say what is the source of this resemblance, since we don't know what is the origin of Sephardic Jews, but considering all the genetic affinities of both groups it likely stems from Sephardic Jews being the descendants of converts in the Mediterranean basin rather than from a common Jewish origin in the Land of Israel. When one compares the autosomal distances of EEJ (current study) or Ashkenazi Jews (in Atzmon et al. [53] and Behar et al. [54]) from the Jewish populations that were investigated in the current study, Iraqi, Iranian, Moroccan, Yemenite and Ethiopian Jews, one finds perfect agreement. EEJ or Ashkenazi Jews are much closer to non-Jewish Europeans than to these Jewish populations in all three studies.

The studies of Atzmon et al. [53] and Behar et al. [54] are based on 164,894 and 226,839 SNPs respectively. While this impressive number reduces the errors of the distances that stem from the number of markers, the errors that stem from sampling only a small number of individuals are much larger in these studies, where sample sizes can be as small as 2-4 individuals. The effect of these errors can be seen in table 7. Despite the small number of markers the current matrix has the highest correlation with geography. Moreover it has a higher correlation with each of the two other matrices than the two of them have with each other. The high correlations between the current matrix and the other two attest for the robustness of the autosomal genetic distances in this study. The lower correlation between the two matrices, which are based on more than 150,000 SNPs, is surprising and even more so, if we remember that the four non-Jewish populations are represented by exactly the same individuals taken from the Human Genome Diversity Panel (HGDP). It is likely then that sampling more individuals, which represent more of the variation of the investigated populations, is far more important than typing many markers. It is also possible that the typing error rates of genome-wide microarray studies are much higher, as demonstrated by the genotyping errors that were discovered in 7 out of 29 (24%) reexamined SNPs [55]. It seems therefore, that good characterization of the genetic relationships between populations can be achieved by a small number of good unique-event-polymorphisms.
Table 7

Comparison of the correlations of the three autosomal genetic distance matrices*

 

Current Study

Atzmon et al.

Geography**

 

r

p

r

p

r

p

Current Study

    

0.561

0.0015

Atzmon et al. 2010

0.872

0.0003

  

0.482***

0.0192

Behar et al. 2010

0.852

0.0012

0.788

0.0029

0.437****

0.0351

* - Based on the 7 populations common to all 3 studies

** - Great circle distances for EEJ or Ashkenazi Jews calculated from Rome (in all cases this was the highest correlation)

*** - Great circle distances for Italians calculated from Parma

**** - Great circle distances for Italians calculated from Florence

r = correlation; p = significance level

Conclusions

EEJ are Europeans probably of Roman descent who converted to Judaism at times, when Judaism was the first monotheistic religion that spread in the ancient world. Any other theory about their origin is not supported by the genetic data. Future studies will have to address their genetic affinities to various Italian populations and examine the possibility of other components both European and Non-European in their gene pool.

Reviewers' comments

Reviewer's report 1

Damian Labuda, Pediatrics Department, Montreal University Sainte-Justine Hospital Research Center, Montreal, PQ Canada (nominated by Jerzy Jurka, Genetic Information Research Institute, Mountain View, California USA).

The author compiled and reanalyzed the data on autosomal and sex chromosomes polymorphisms collected by different laboratories on different Jewish and West-Eurasiatic populations. His analysis indicates much greater European component of Eastern European Jews, EEJ (essentially Ashkenazim) than of other Jewish groups. Moreover the analysis points to Italians as the closest population to EEJ.

The question is how to interpret this evidence. Imperial Rome was a very cosmopolitan city culturally and genetically diverse. To what extent a sample of contemporary Italians preserves the genetic link to its population? It can simply reflect a mixture of historical influences from different centers around the Mediterranean Sea. We should thus keep in mind that the Italian connection may simply indicate Southern European and Mediterranean links with the latter including Middle Eastern roots.

Interestingly, this analysis that is based on a limited number of markers provided results that are very similar to a paper of Atzmon and colleagues, published five days ago in the American Journal of Human Genetics, and based on the microarray-based genotyping genome of wide distributed markers. I would like the author to comment on this paper in the context of his findings and his thoughts and reflections on the origin of Jewish Diasporas. Should we go back to the single locus analyses, as in the case of uniparentally transmitted markers, but targeting one by one different individual segments of the nuclear genome? Perhaps, in this way we could partition and identify genetic ancestries of different populations, which due to their history of relative isolation, are considered as genetically homogenous.

The author refers to Sangvi's G2 as the most appropriate distance metrics. Could you make it more clear when this metric was used and when that of Reynolds (only to produce a tree?).

Author's response

The historical sources listed above show that conversion to Judaism was common in ancient Rome among all ranks of the Roman society including the imperial families. It is thus unlikely that the original Roman population did not constitute a significant portion of the proselytes. What else can explain the resemblance of EEJ to a general sample of Italians in this study and to more local samples in the two array studies [53, 54]? In all three studies the genetic affinities of the Ashkenazim are very similar to the affinities of the Italians, with the Ashkenazim usually being a bit more distant from the other populations, as can be expected from a population that underwent a stronger genetic drift. It is thus unlikely that the Ashkenazim are a mixture of people from different places in the Mediterranean basin, unless current-day Italians themselves not only have absorbed foreign genetic contributions, but actually constitute such a mixture, and this seems unlikely as well. The very high correlation (0.926) between the genetic distances of EEJ and geographic distances, when the latter are calculated from Rome, also supports the origin of EEJ from Italy or its vicinity and not merely from the Mediterranean basin. The similarity to Italians was also evident when several Italian populations from different provinces were included in a comparison based on classical autosomal markers. Most Italian populations were closer to EEJ than all other populations (data not shown).

My comments on the papers by Atzmon et al. [53] and Behar et al. [54] are in the discussion. Studying autosomal haplotypes will indeed contribute to revealing the ancestries of populations, but in order to gain meaningful insights one ought to study at least several loci and ensure that sample sizes are adequate, this may entail more effort than studying single SNPs, and I am not sure that the affinities between the populations are going to be depicted more accurately. I changed the phrasing in Methods to make it clearer that the formula of Reynolds et al. was only used for the calculation of the tree.

Reviewer's report 2

Kateryna Makova, Department of Biology, Penn State University, Pennsylvania USA.

This is an interesting manuscript that presents intriguing results. I have only a few comments:
  1. 1.

    The introduction is very short, while the discussion is lengthy. I suggest moving parts of the Discussion to the Introduction.

     
  2. 2.

    Some of the statements in the Discussion are too strong. I disagree with statements about "erroneous Y chromosomal genetic distances", "both uniparental markers should not be used to trace their origin", "uniparental markers being unreliable". The author should modify them.

     

Author's response

I moved the paragraph on the history of EEJ to the Introduction. The current revised version of the paper includes a new comparison based on mtDNA. I maintain that it adds more weight to my assertion that the uniparental markers should not be used to trace the origin of EEJ. In no way did I mean that the uniparental markers are always unreliable; to clarify it I modified the relevant sentence in the discussion. Indeed from the demographic examples that I give in the Discussion, it seems that the uniparental markers can be used to study the origins of Iraqi Jews and Yemenite Jews.

Reviewer's report 3

Qasim Ayub, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK (nominated by Dan Graur, Department of Biology and Biochemistry, University of Houston, Houston, USA).

The paper by Zoossmann-Diskin entitled 'The origin of Eastern European Jews revealed by autosomal and sex chromosomal polymorphisms' explores autosomal and sex chromosomal polymorphisms in six Jewish populations using previously published and additional unpublished data. The author concludes that the Jewish populations examined do not share a common origin and that Eastern European Jews are closer to the Italian population.

My major concern is the choice of markers and populations used in this study. The author has analyzed 17 autosomal loci, including 9 polymorphic protein electrophoretic variants in which the genotype was assumed. Although phenotypes often do correlate with genotypes assuming that they do can lead to erroneous results. Of the remaining 8 it is unclear whether the same samples were genotyped as the sample numbers for each locus vary widely (Supplementary Tables 2-4).

The author also uses Y hapologroup frequencies and shows a multidimensional scaling plot of Y chromosomal genetic distance matrix. However, the supplementary data (Supplementary Table 5) lists an outdated nomenclature for Y haplogroups as the M78 marker is no longer considered part of haplogroup E3b1. It would be more appropriate to list which markers are used to designate the haplogroups to ensure that they are comparable. In addition, the haplogroups that are selected for these analyses do not provide phylogenetic resolution to reliably detect male genetic sub-structure within the Middle East. The omission of recent mtDNA studies (Behar et al., 2008, PLoS One 3:e2062) is surprising as is the use of a single X chromosomal locus (DYS44) to make broad conclusions about genetic relatedness.

Current evidence, supported more recently by two major studies carried out on Jewish populations (Atzmon et al., Am J H Genetics 86:850-859; Behar et al., Nature doi:10.1038) using a much larger dataset clearly demonstrate a common genetic thread linking the diverse Mizrahi, Sephardic and Ashkenazi Jewish populations with the populations from the Levant and Middle East. The Ashkenazi show a European component but this is shared with many Eastern and Southern Europeans populations. These studies contradict the author's conclusion and demonstrate the power of using unbiased markers and host populations in corresponding geographic regions to address issues such as genetic relatedness among Jewish and non-Jewish populations

Author's response

I am not sure what Dr Ayub means by "assumed", but I suspect that he means something like the relationships between phenotype and genotype in certain blood groups, in which one (or more) allele is dominant over the other and the gene frequencies of the alleles have to be inferred from the phenotypes assuming Hardy-Weinberg equilibrium. In such cases there may indeed be errors in the gene frequencies. Protein electrophoretic markers are completely different. Nothing is inferred! As mentioned in Methods all the protein electrophoretic markers in this study represent a SNP at the DNA level. This SNP causes an amino acid change that can be detected at the protein level. Both alleles are directly viewed on the gel in the same way as both alleles of an RFLP are directly viewed on the gel. Gene frequencies are determined in both cases by simple gene counting and the error rate in protein electrophoresis is no greater than in DNA studies. There is no need to type the same samples for all the polymorphisms, because the unit of study is the population, not the individual. One can use polymorphisms typed by different researchers using different samples and combine them to create a genetic profile of each population. Typing all the polymorphisms on the same sample does not add more credibility to the study. Indeed the renowned works that employed classical autosomal markers to portray the genetic affinities of human populations were based on many different samples typed by many different researchers [56, 57].

The nomenclature in the Y chromosome supplementary table has been updated. Following the publication of the study by Behar et al. [54] it was possible to add more Jewish populations to the Y chromosome analysis and increase the number of chromosomes for the Jewish populations. This increase has come however at the expense of resolution, because Behar et al. [54] used fewer haplogroups in their analysis. Consequently the number of haplogroups was reduced from 15 in the original version to 14 in this revised version. I would have been happier if the available data on the Jewish populations had enabled greater resolution to reliably detect male genetic sub-structure within the Middle East, but since this work deals with the genetic affinities of EEJ, the current level is sufficient. The work of Behar et al. from 2008 was instrumental in creating the mtDNA matrix as can be seen in table 7 in Additional file 1. There was no need to cite it previously, as it did not contain any genetic distance analysis that could further clarify the origin of EEJ. I am surprised at Dr Ayub's surprise at the use of a single X chromosomal locus. It would have been better to use many X chromosomal loci, but even the use of single loci is advantageous, as I am sure even Dr Ayub would agree regarding the two other single loci that I use, the non-recombining Y chromosome (NRY) and mtDNA.

As written in the Discussion the genetic distance matrices of Atzmon et al. [53] and Behar et al. [54] do not contradict my results, but reinforce them. I completely reject Dr Ayub's claim that the markers or populations I used are biased in anyway, and I let the reader judge, where exactly the bias lies.

Declarations

Authors’ Affiliations

(1)
Department of Haematology and Genetic Pathology, School of Medicine, Flinders University
(2)
Department of Human Genetics, Sackler Faculty of Medicine, Tel-Aviv University
(3)
Current Address: Blood Bank, Sheba Medical Center

References

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