Open Access

Prevalence of common disease-associated variants in Asian Indians

  • Trevor J Pemberton1,
  • Niyati U Mehta1, 3,
  • David Witonsky5,
  • Anna Di Rienzo5,
  • Hooman Allayee1, 2,
  • David V Conti2 and
  • Pragna I Patel1, 3, 4Email author
BMC Genetics20089:13

DOI: 10.1186/1471-2156-9-13

Received: 23 January 2007

Accepted: 04 February 2008

Published: 04 February 2008

Abstract

Background

Asian Indians display a high prevalence of diseases linked to changes in diet and environment that have arisen as their lifestyle has become more westernized. Using 1200 genome-wide polymorphisms in 432 individuals from 15 Indian language groups, we have recently shown that: (i) Indians constitute a distinct population-genetic cluster, and (ii) despite the geographic and linguistic diversity of the groups they exhibit a relatively low level of genetic heterogeneity.

Results

We investigated the prevalence of common polymorphisms that have been associated with diseases, such as atherosclerosis (ALOX5), hypertension (CYP3A5, AGT, GNB3), diabetes (CAPN10, TCF7L2, PTPN22), prostate cancer (DG8S737, rs1447295), Hirschsprung disease (RET), and age-related macular degeneration (CFH, LOC387715). In addition, we examined polymorphisms associated with skin pigmentation (SLC24A5) and with the ability to taste phenylthiocarbamide (TAS2R38). All polymorphisms were studied in a cohort of 576 India-born Asian Indians sampled in the United States. This sample consisted of individuals whose mother tongue is one of 14 of the 22 "official" languages recognized in India as well as individuals whose mother tongue is Parsi, a cultural group that has resided in India for over 1000 years. Analysis of the data revealed that allele frequency differences between the different Indian language groups were small, and interestingly the variant alleles of ALOX5 g.8322G>A and g.50778G>A, and PTPN22 g.36677C>T were present only in a subset of the Indian language groups. Furthermore, a latitudinal cline was identified both for the allele frequencies of the SNPs associated with hypertension (CYP3A5, AGT, GNB3), as well as for those associated with the ability to taste phenylthiocarbamide (TAS2R38).

Conclusion

Although caution is warranted due to the fact that this US-sampled Indian cohort may not represent a random sample from India, our results will hopefully assist in the design of future studies that investigate the genetic causes of these diseases in India. Our results also support the inclusion of the Indian population in disease-related genetic studies, as it exhibits unique genotype as well as phenotype characteristics that may yield new insights into the underlying causes of common diseases that are not available in other populations.

Background

India is currently the second most populous country in the world after China with a population of 1,087 million that is predicted to expand to 1,628 million by the year 2050 [1]. The high prevalence of endogamy and relatively low admixture present in the population distinguishes Asian Indians (from the subcontinental region comprising India, Pakistan, Bangladesh and Sri Lanka) from most other populations presently used in genetic studies [2]. As the country is becoming more westernized both in diet and lifestyle, the prevalence of diseases associated with these lifestyle changes is increasing.

The languages of India belong to four major families: Indo-Aryan (a branch of the Indo-European family), Dravidian, Austroasiatic, and Sino-Tibetan, with the overwhelming majority of the population speaking languages belonging to the first two families [3]. The 1981 census – the last census to tabulate languages – reported 112 mother tongues with more than 10,000 speakers and almost 1 million people speaking other languages. The Indian constitution recognizes 22 official Indian languages. They are Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Santhali, Sindhi, Tamil, Telugu, and Urdu. Of the official languages, about 43 percent of the population speaks Hindi as their mother tongue. Telugu, Bengali, Marathi, and Tamil rank next, each the mother tongue of about 4 to 5 percent; Urdu, Gujarati, Malayalam, Kannada, and Oriya are claimed by between 2 and 3 percent; Bhojpuri, Punjabi, and Assamese by 1 to 2 percent; and all other languages by less than 1 percent each. Pertinent to this study, with the exception of Hindi, which is the primary language spoken in several north Indian states, each of the other languages is spoken by individuals with origins from a distinct Indian state [3] and hence, each corresponds to a geographically defined region within India.

The Parsis are a member of the close-knit Zoroastrian community found primarily in the Indian state of Gujarat. Although the Parsis of India are descendents from Persian Zoroastrians who emigrated to the Indian subcontinent from Persia over 1,000 years ago [46], they no longer have social or familial ties to Persians, and do not share language or recent history with them. Mitochondrial and Y-chromosome analyses suggest that since their arrival in India, the Parsis have integrated themselves into Indian society through a male-mediated migration of their ancestors who admixed with local females [7, 8]. However, they have simultaneously maintained their own ethnic identity, making the Parsi community both Indian in terms of national affiliation, language and history, but not typically Indian in terms of consanguinity or cultural, behavioral and religious practices.

Large inter-ethnic variation is known for a number of common conditions, which may be due, in part, to differences in risk allele frequencies that are the result of either population structure, migration, or the action of natural selection on the risk variants themselves [9]. Because selective pressures may vary with geography and other environmental variables, detectable patterns of geographic variation may sometimes be observed in disease alleles. Recent population structure analysis using 1200 genome-wide polymorphisms in 432 individuals from 15 Indian language groups has shown that Indians constitute a distinct population cluster and that despite the geographic and linguistic diversity of the groups, they exhibit a low level of genetic differentiation [10].

Metabolic disorders have been found to have a disproportionately high prevalence in the Indian population, a phenomenon which is likely associated with the increasing westernization of India. For example, many studies have shown that the prevalence of coronary artery disease (CAD) in individuals of Indian origin is currently much higher than in other ethnic groups and is even increasing [1117]. It is also currently estimated that 18% of the Indian population suffer from hypertension, one of the major risk factors associated with CAD, and that the prevalence of hypertension is also increasing within this population [18]. However, this prevalence is comparable to that in other worldwide populations (15.0–40.0%) [19]. Type-2 diabetes is now reaching epidemic proportions in India, most prominently in urban Indians where there has been a very steep increase in prevalence over the last decade from 8.3% in 1992, 11.6% in 1997, to 15.5% in 2005 [2023]. Other diseases are also found at varying frequencies in the Indian population including Hirschsprung disease [24, 25], age-related macular degeneration (AMD) [26, 27], prostate cancer [28, 29], and type-1 diabetes [30, 31]. However, despite the high prevalence of common disorders and the large population within which to study them, most modern genetic studies have not incorporated Indian populations.

In the present study, we have investigated the prevalence of common polymorphisms that have recently been reported to be risk factors for atherosclerosis, hypertension, diabetes, prostate cancer, Hirschsprung disease, and age-related macular degeneration (Table 1) in a cohort of 576 India-born Asian Indians sampled in the United States. This cohort consists of individuals whose mother tongue is one of 14 of the 22 "official" languages recognized in India, as well as individuals whose mother tongue is Parsi. We have also investigated variation in the TAS2R38 bitter taste receptor, associated with the ability to taste phenylthiocarbamide (PTC), and in the SLC24A5 gene, associated with variation in human skin pigmentation (Table 1). The prevalence of these diseases/traits [3243] and the risk-associated variant alleles of these polymorphisms (see Additional file 1) have been reported to vary between populations/ethnicities, suggesting that their influence may also vary between populations. The etiology of most cases of CAD [44], type-1 [45] and type-2 [46] diabetes, hypertension [47], prostate cancer [48], and AMD [49] are complex and are likely caused by the combined effect of genes and environment. However, the relative contribution of genes versus environment to the diseases/traits and the degree of genetic heterogeneity are likely very different for the different diseases/traits considered in this paper. Whilst we have investigated the prevalence of these minor alleles within this Indian cohort and compared the frequencies to those of other populations, we have not addressed the potential impact of environmental factors that may differ between these populations. This study will hopefully aid in the design of future genetic studies investigating the underlying causes of these diseases in India.
Table 1

Investigated polymorphisms and their associated diseases.

Gene Name

Nucleotide Change

Protein Change

NCBI Acc. #

Phenotype

Reference

AGT

g.802C>T

M235T

rs699

Hypertension

[100]

CYP3A5

g.6980G>A

IVS4

rs776746

Hypertension

[101]

GNB3

g.4423C>T

S275S

rs6489738

Hypertension

[53]

ALOX5

(5'-GGGCGG-3')3–8

promoter

none

Athersclerosis

[57]

ALOX5

g.20C>T

T7T

rs4987105

Athersclerosis

 

ALOX5

g.8322G>A

T90T

rs2228064

Athersclerosis

 

ALOX5

g.50778G>A

E243K

rs2228065

Athersclerosis

 

CAPN10

g.4834T>C

IVS3

rs2975760

Type-2 diabetes

[102]

TCF7L2

g.98386G>T

IVS4

rs12255372

Type-2 diabetes

[103]

PTPN22

g.36677C>T

R620W

rs2476601

Type-1 diabetes

[104]

Intragenic

(5'-AC-3')13–30

none

DG8S737

Prostate Cancer

[58]

Intragenic

C>A

none

rs1447295

Prostate Cancer

[58]

CFH

g.37989T>C

Y402H

rs1061170

Age-related macular degeneration

[105-107]

LOC387715

g.205G>T

A69S

rs10490924

Age-related macular degeneration

[108]

RET

g.9349G>A

IVS1

rs2435357

Hirschsprung disease

[109]

TAS2R38

g.144G>C

A49P

rs713598

ability to taste PTC

[59]

TAS2R38

g.784C>T

A262V

rs1726866

ability to taste PTC

[59]

TAS2R38

g.885A>G

I296V

rs10246939

ability to taste PTC

[59]

SLC24A5

g.13233G>A

A111T

rs1426654

Skin pigmentation

[110]

Results

We have examined the variation of 19 common polymorphisms (Table 1) associated with diseases/traits in a collection of 576 individuals of Indian descent sampled in the United States that represent 14 of the 22 official or scheduled Indian languages as well as one additional cultural group (Parsi; Figure 1). These individuals are part of an expanded cohort that represents 15 groups defined by language and that was used in a previous population-genetic study [10]. The allele frequencies of these 19 common polymorphisms in the Indian cohort can be found in Tables 2, 3, 4; for genotype frequencies see Additional file 2, Tables S1–3.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-13/MediaObjects/12863_2007_Article_579_Fig1_HTML.jpg
Figure 1

Sampled language groups, their sample sizes, and their geographic origins within India.

Table 2

Allele frequencies of the polymorphisms associated with the metabolic disorders of diabetes, hypertension, and atherosclerosis.

   

Diabetes

Hypertension

Atherosclerosis

Gene Name:

PTPN22

CAPN10

TCF7L2

CYP3A5

AGT

GNB3

ALOX5

ALOX5

ALOX5

ALOX5

Nucleotide Change:

g.36677C>T

g.4834T>C

g.98386G>T

g.6980G>A

g.802C>T

g.4423C>T

MS (5'-GGGCGG-3' starting at -145)

g.20C>T

g.8322G>A

g.50778G>A

 

n

°N

T

C

T

A

T

T

3

4

5

6

7

8

T

A

A

Mother Tongue

Assamese

26

26.00

0.000

0.220

0.280

0.220

0.400

0.280

0.000

0.160

0.800

0.040

0.000

0.000

0.140

0.000

0.020

Bengali

27

23.00

0.017

0.224

0.138

0.207

0.414

0.293

0.000

0.276

0.655

0.069

0.000

0.000

0.276

0.017

0.000

Gujarati

181

23.00

0.000

0.167

0.167

0.313

0.229

0.396

0.000

0.167

0.792

0.042

0.000

0.000

0.188

0.000

0.000

Hindi

29

30.30

0.012

0.209

0.244

0.221

0.395

0.279

0.000

0.186

0.767

0.047

0.000

0.000

0.186

0.012

0.000

Kannada

24

15.00

0.000

0.268

0.214

0.339

0.339

0.411

0.000

0.179

0.732

0.089

0.000

0.000

0.179

0.036

0.000

Kashmiri

24

32.44

0.000

0.320

0.480

0.080

0.480

0.420

0.000

0.180

0.800

0.020

0.000

0.000

0.160

0.020

0.000

Konkani

43

15.00

0.000

0.115

0.192

0.346

0.308

0.288

0.000

0.080

0.860

0.060

0.000

0.000

0.096

0.058

0.000

Malayalam

25

10.00

0.000

0.130

0.222

0.333

0.426

0.315

0.000

0.148

0.759

0.093

0.000

0.000

0.148

0.000

0.000

Marathi

26

20.00

0.000

0.278

0.315

0.481

0.333

0.259

0.000

0.111

0.778

0.111

0.000

0.000

0.111

0.000

0.000

Marwari

25

27.00

0.006

0.265

0.243

0.265

0.423

0.296

0.008

0.186

0.758

0.044

0.000

0.003

0.193

0.019

0.003

Oriya

27

20.00

0.019

0.192

0.231

0.250

0.308

0.346

0.000

0.231

0.692

0.077

0.000

0.000

0.231

0.000

0.000

Parsi

25

19.00

0.020

0.280

0.180

0.240

0.320

0.440

0.000

0.240

0.660

0.100

0.000

0.000

0.240

0.020

0.000

Punjabi

28

30.40

0.017

0.155

0.241

0.259

0.379

0.345

0.034

0.259

0.690

0.017

0.000

0.000

0.241

0.000

0.017

Tamil

29

11.00

0.018

0.161

0.321

0.143

0.429

0.393

0.018

0.232

0.714

0.036

0.000

0.000

0.232

0.054

0.018

Telugu

28

16.00

0.000

0.229

0.271

0.167

0.354

0.229

0.000

0.229

0.688

0.063

0.021

0.000

0.229

0.000

0.000

FST

  

0.016

0.008

0.002

0

0.011

0.010

0.006

0.013

0.009

0.013

p-value

  

1

1

1

1

1

1

1

1

1

1

Gender

Female

237

 

0.002

0.241

0.241

0.295

0.390

0.316

0.008

0.191

0.733

0.066

0.002

0

0.192

0.017

0.006

Male

339

 

0.013

0.224

0.254

0.236

0.385

0.320

0.004

0.192

0.753

0.049

0

0.001

0.193

0.016

0.003

FST

  

0

0.041

0.046

0

0.052

0.052

0

0.052

0.052

0.037

p-value

  

1

0.839

0.830

1

0.820

0.820

1

0.819

0.820

0.847

Population

Cohort

576

 

0.009

0.231

0.248

0.260

0.387

0.319

0.006

0.192

0.745

0.056

0.001

0.001

0.193

0.016

0.004

Standard Error

  

0.002

0.016

0.021

0.025

0.017

0.017

0.003

0.014

0.015

0.007

0.001

1.85 × 10-4

0.013

0.005

0.002

HWE Constant

  

4.98 × 10-5

0.003

0.001

0.013

0.004

0.001

0.009

0.016

2.50 × 10-4

1.94 × 10-5

p-value

  

1

0.720

1

0.100

0.658

1

0.213

0.014

1

1

Welch modified t-test:

               

Africa

   

5.57 × 10 -9

 

8.44 × 10 -3

4.55 × 10 -10

        

0.151

0.172

Europe

  

7.16 × 10 -6

0.003

0.790

3.19 × 10 -4

1.57 × 10 -4

0.036

       

0.724

0.067

Middle East

     

1.94 × 10 -9

0.007

          

Central/South Asia

  

0.006

4.65 × 10 -5

1.16 × 10 -6

4.89 × 10 -14

0.432

1.37 × 10 -7

       

0.007

 

East Asia

     

4.64 × 10 -10

1.19 × 10 -9

          

Americas

   

0.029

 

1.18 × 10 -5

1.56 × 10 -5

0.136

         

Oceania

     

1.17 × 10 -8

0.002

          

P-values significant at <0.05 are shown in bold.

°N = degrees of latitude north of the equator.

Table 3

Allele frequencies of the polymorphisms associated with prostate cancer.

  

Prostate Cancer

 

Polymorphism:

DG8S737

rs1447295

Nucleotide Change:

MS

C>A

 

n

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

+1

+2

+3

+4

+5

+6

+7

A

Mother Tongue

Assamese

26

0.000

0.040

0.000

0.000

0.000

0.020

0.120

0.020

0.060

0.180

0.220

0.100

0.060

0.080

0.080

0.020

0.000

0.000

0.120

Bengali

27

0.017

0.000

0.000

0.000

0.000

0.000

0.034

0.121

0.155

0.190

0.103

0.155

0.052

0.121

0.052

0.000

0.000

0.000

0.121

Gujarati

181

0.000

0.042

0.000

0.000

0.042

0.000

0.021

0.042

0.229

0.188

0.188

0.042

0.042

0.125

0.042

0.000

0.000

0.000

0.125

Hindi

29

0.023

0.023

0.000

0.012

0.012

0.012

0.128

0.035

0.151

0.209

0.116

0.093

0.035

0.070

0.023

0.035

0.023

0.000

0.186

Kannada

24

0.000

0.071

0.000

0.000

0.018

0.000

0.089

0.036

0.161

0.268

0.125

0.036

0.000

0.143

0.054

0.000

0.000

0.000

0.125

Kashmiri

24

0.000

0.000

0.000

0.000

0.000

0.000

0.060

0.060

0.120

0.220

0.120

0.200

0.040

0.120

0.060

0.000

0.000

0.000

0.040

Konkani

43

0.000

0.019

0.000

0.000

0.019

0.019

0.077

0.115

0.135

0.173

0.192

0.058

0.135

0.019

0.019

0.019

0.000

0.000

0.192

Malayalam

25

0.000

0.000

0.000

0.000

0.000

0.000

0.093

0.037

0.167

0.204

0.111

0.185

0.019

0.148

0.000

0.019

0.000

0.019

0.074

Marathi

26

0.000

0.000

0.000

0.000

0.037

0.000

0.074

0.037

0.093

0.278

0.111

0.130

0.056

0.093

0.056

0.037

0.000

0.000

0.111

Marwari

25

0.000

0.008

0.003

0.000

0.008

0.022

0.110

0.028

0.116

0.293

0.122

0.077

0.058

0.105

0.041

0.006

0.003

0.000

0.141

Oriya

27

0.000

0.000

0.019

0.019

0.058

0.038

0.058

0.135

0.077

0.173

0.173

0.096

0.038

0.077

0.019

0.019

0.000

0.000

0.308

Parsi

25

0.000

0.000

0.020

0.000

0.040

0.000

0.020

0.020

0.380

0.220

0.100

0.140

0.020

0.020

0.020

0.000

0.000

0.000

0.040

Punjabi

28

0.000

0.000

0.000

0.000

0.017

0.000

0.052

0.069

0.121

0.345

0.121

0.086

0.086

0.069

0.034

0.000

0.000

0.000

0.034

Tamil

29

0.000

0.000

0.000

0.000

0.018

0.018

0.161

0.018

0.125

0.143

0.143

0.143

0.054

0.107

0.054

0.018

0.000

0.000

0.161

Telugu

28

0.000

0.021

0.000

0.000

0.000

0.000

0.042

0.104

0.188

0.375

0.083

0.021

0.042

0.042

0.021

0.042

0.000

0.021

0.104

FST

 

0.007

0.020

p-value

 

1

1

Gender

Female

237

0.002

0.021

0.004

0.002

0.019

0.011

0.078

0.044

0.146

0.251

0.129

0.099

0.057

0.078

0.049

0.011

0.000

0.000

0.120

Male

339

0.003

0.009

0.001

0.001

0.013

0.013

0.091

0.052

0.140

0.242

0.133

0.094

0.049

0.105

0.032

0.013

0.004

0.003

0.136

FST

 

0

0

p-value

 

1

1

Population

Cohort

576

0.003

0.014

0.003

0.002

0.016

0.012

0.085

0.050

0.142

0.245

0.130

0.098

0.052

0.094

0.039

0.012

0.003

0.002

0.129

Standard Error

 

0.002

0.006

0.002

0.001

0.005

0.003

0.011

0.010

0.020

0.017

0.010

0.014

0.008

0.010

0.005

0.004

0.002

0.002

0.018

HWE Constant

 

0.003

0.002

p-value

 

0.433

0.580

Welch modified t-test:

                    

Africa

                    

Europe

                   

0.091

Asia

                   

0.486

Americas

                    

Alleles of DG8S737 were assigned based upon the change in the number of repeats away from that of the reference sequence in the NCBI database (23 AC dinucleotide repeats). Allele -1 is equivalent to the prostate cancer risk-associated -8 allele reported by Amundadottir et al. that had 22 repeats [58]. P-values significant at <0.05 are shown in bold.

Table 4

Allele frequencies of the polymorphisms associated with melanogenesis, Hirschsprung disease, AMD, and the ability to taste PTC.

  

Melanogenesis

Hirschsprung

AMD

Ability to Taste PTC

Gene Name:

SLC24A5

RET

CFH

LOC387715

TAS2R38

TAS2R38

TAS2R38

Nucleotide Change:

g.13242G>A

g.9349G>A

g.37989T>C

g.205G>T

g.144G>C

g.784C>T

g.885A>G

 

n

G

T

C

T

C

C

G

Mother Tongue

Assamese

26

0.260

0.240

0.360

0.200

0.340

0.360

0.360

Bengali

27

0.293

0.207

0.276

0.397

0.310

0.310

0.310

Gujarati

181

0.208

0.271

0.333

0.375

0.313

0.313

0.313

Hindi

29

0.105

0.209

0.267

0.244

0.349

0.360

0.360

Kannada

24

0.268

0.232

0.304

0.464

0.196

0.250

0.250

Kashmiri

24

0.020

0.100

0.260

0.340

0.480

0.500

0.500

Konkani

43

0.058

0.250

0.385

0.269

0.308

0.308

0.308

Malayalam

25

0.259

0.259

0.222

0.500

0.259

0.296

0.296

Marathi

26

0.296

0.259

0.278

0.204

0.407

0.407

0.426

Marwari

25

0.041

0.273

0.265

0.334

0.384

0.398

0.406

Oriya

27

0.154

0.346

0.212

0.250

0.346

0.365

0.365

Parsi

25

0.100

0.300

0.300

0.380

0.280

0.280

0.300

Punjabi

28

0.052

0.293

0.431

0.414

0.328

0.310

0.328

Tamil

29

0.036

0.179

0.250

0.393

0.339

0.321

0.375

Telugu

28

0.000

0.188

0.167

0.354

0.417

0.417

0.417

FST

 

0

0.011

0.009

0

0.009

0.010

0.011

p-value

 

1

1

1

1

1

1

1

Gender

Female

237

0.110

0.232

0.268

0.348

0.331

0.338

0.342

Male

339

0.117

0.261

0.289

0.330

0.363

0.376

0.386

FST

 

0.049

0.020

0.037

0.042

0.020

0.007

0

p-value

 

0.825

0.887

0.847

0.837

0.887

0.933

1

Population

Cohort

576

0.114

0.249

0.280

0.338

0.350

0.360

0.368

Standard Error

 

0.028

0.015

0.018

0.024

0.018

0.017

0.017

HWE Constant

 

0.004

0.003

0.009

0.006

0.002

0.002

0.002

p-value

 

0.304

0.654

0.350

0.513

0.926

0.855

0.857

Welch modified t-test:

       

Africa

 

4.21 × 10 -14

   

3.86 × 10 -4

3.24 × 10 -8

0.005

Europe

 

2.80 × 10 -4

 

4.20 × 10 -3

0.068

2.43 × 10 -6

5.78 × 10 -5

1.37 × 10 -4

Asia

 

7.18 × 10 -14

 

1.55 × 10 -8

0.018

0.055

7.97 × 10 -6

7.60 × 10 -6

Americas

 

0.024

   

0.006

1.92 × 10 -5

1.64 × 10 -5

Oceania

     

0.090

0.009

0.003

P-values significant at <0.05 are shown in bold.

Indian language groups

All of the polymorphisms genotyped in our Indian cohort were found to be in Hardy-Weinberg equilibrium (HWE; Tables 2, 3, &4), except for the ALOX5 g.20C>T polymorphism which gave an HWE constant of 0.016 (p-value 0.014). However, when corrected for multiple testing using a Bonferroni correction, this deviation becomes insignificant (corrected p-value 0.266).

Computations of FST suggested that there were no significant minor allele frequency (MAF) differences between the language groups (Tables 2, 3, &4). This is in agreement with a recent population structure analysis of this Indian cohort that found that the different language groups exhibited a low level of genetic differentiation [10]. Therefore, when comparing the MAF of these polymorphisms in this Indian cohort with their frequency in other populations, we grouped the MAFs of these polymorphisms in the Indian language groups into a single comparative group. Of those polymorphisms with MAF data reported in other populations (see Additional file 1, Tables S4–20), most had significant differences in frequency with our Indian cohort (Tables 2, 3, &4).

Patterns in the variation of the MAF of several of the 19 polymorphisms were visible between some of the language groups (Tables 2, 3, &4). Of note was the Kashmiri group, which had significantly higher MAFs than the other groups for the CAPN10 g.4834T>C and TCF7L2 g.98386G>T SNPs associated with type-2 diabetes risk (Table 2). Furthermore, considering the hypertension-associated SNPs, whilst the Kashmiri group had a significantly lower MAF for the CYP3A5 g.6980G>A SNP compared to the other groups, this language group also had the highest MAF for the AGT g.802C>T SNP and the second highest MAF for the GNB3g.4423C>T SNP (Table 2). Interestingly, whilst the Kashmiri group also had the highest MAFs for all three TAS2R38 SNPs associated with the ability to taste PTC (Table 4), the Kannada group had the lowest MAF for all three TAS2R38 SNPs. It was also noteworthy that the Oriya group had a significantly higher MAF of the rs1447295 SNP associated with prostate cancer risk than the other groups (Table 3), but this higher prevalence was not reflected in the DG8S737 microsatellite allele frequencies that are also associated with prostate cancer risk.

Hypertension

The frequency of the CYP3A5 g.6980G>A [50, 51], AGT g.802C>T [50], and GNB3 g.825C>T [5254] polymorphisms have been reported to vary significantly between different populations/ethnicities in a cline that correlates with the latitudinal variation of the populations [50, 55, 56]. To investigate how this Indian cohort and its language groups fit into this correlation, the MAFs of the individual groups were plotted against absolute latitude together with the MAFs of the other known populations previously investigated with regard to the latitudinal cline (Figure 2; see Additional file 1, Tables S4–6). The MAFs of the Indian language groups for the CYP3A5 g.6980G>A SNP were found to follow the pattern of the other populations (Figure 2A). Accordingly, the correlation of allele frequency with latitude, as measured by ρ and r2, remained similarly high whether or not the Indian data were included (Table 5). A corresponding correlation was not found for the AGT g.802C>T SNP, for which the language groups were clustered largely below the other populations (Figure 2B). This resulted in an appreciable reduction in ρ and r2 with the inclusion of the Indian data (Table 5), but the correlation remained significant at the 0.01 level. For the GNB3 g.825C>T SNP, the MAFs of the language groups largely cluster slightly below those of the other populations (Figure 2C). However, there was a substantial reduction of both ρ and r2 when the Indian data were included (Table 5), reducing the significance of the correlation to the 0.05 level.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-13/MediaObjects/12863_2007_Article_579_Fig2_HTML.jpg
Figure 2

The correlation between minor allele frequency and latitude of (A) CYP3A5 g.6980G>A, (B) AGT g.802C>T, and (C) GNB3 g.4423C>T. Correlation coefficients for each graph can be found in Table 5.

Table 5

Correlation coefficients of hypertension-associated polymorphism MAFs with latitude.

Polymorphism

Population

Adjusted Spearman Rank Correlation Score

Adjusted r2

CYP3A5 g.6980G>A

CEPH-HGDP

-0.621**

0.319

 

CEPH-HGDP + HapMap

-0.620**

0.349

 

CEPH-HGDP + HapMap + AIP

-0.603**

0.302

AGT g.802C>T

CEPH-HGDP

-0.718**

0.409

 

CEPH-HGDP + Others

-0.693**

0.373

 

CEPH-HGDP + Others + AIP

-0.518**

0.248

GNB3 g.825C>T

Others

-0.620**

0.278

 

Others + AIP

-0.300*

0.101

* significant at 0.05, ** significant at 0.01

CEPH-HGDP = Centre d'Etude du Polymorphisme Humain-Human Genome Diversity Panel.

AIP = Asian Indian population.

Others = data from other populations that were not part of CEPH-HGDP or HapMap.

For sample sizes, please see Additional file 1, Tables S4, S5, S6, for CYP3A5 g.6980G>A, AGT g.802C>T, and GNB3 g.825C>T, respectively.

Atherosclerosis

Whilst deviation away from the common 5-allele of the ALOX5 promoter microsatellite (ALOX5P) has been previously associated with an increased risk of atherosclerosis [57], allelic variation at the three ALOX5 coding SNPs (g.20C>T; g.8322G>A; g.50778G>A) has not. Haplotype analysis of ALOX5P and these three ALOX5 SNPs in this Indian cohort found that they were in strong LD, with a D' close to 1 (data not shown). However, their squared correlation (r2) was found to vary (Table 6). The 4-repeat allele of ALOX5P was found to be strongly correlated with the minor T-allele of the g.20C>T SNP, and the common 5-repeat allele was strongly correlated with the major C-allele of the g.20C>T SNP. The 3-repeat allele of the promoter repeat polymorphism was also found to be in moderate correlation with the major G-allele of the g.50778G>A SNP. In agreement with this correlation and with the allele frequencies, the most common haplotype of the ALOX5P promoter variant and the coding ALOX5 SNPs (g.20C>T; g.8322G>A; g.50778G>A) in the Indian population was 5CGG. The frequency of this haplotype was four times that of the second most common haplotype, which combined the 4-repeat allele of ALOX5P with the minor allele of the g.20C>T SNP and the major allele of the g.8322G>A and g.50778G>A SNPs (Table 7). Over half of the Indian cohort was homozygous for the 5CGG haplotype (Table 8) and a further third were heterozygous, of which 3.3% also possessed a second haplotype containing the 5-repeat allele of ALOX5P (Table 9). Two thirds of this cohort was heterozygous for the 5CGG and 4TGG haplotypes, and just over a fifth were heterozygous for the 5CGG and 6CGG haplotypes (Table 8). Three other haplotypes were found to be homozygous in individuals in this cohort but at a very low frequency; 4TGG, 6CGG, and 3CGG (Table 8).
Table 6

Squared correlation (r2) values between alleles of ALOX5P and the three coding ALOX5 SNPs.

Allele

g.20C>T

g.8322G>A

g.50778G>A

3

8.79 × 10-5

9.87 × 10-5

0.253 [G]

4

0.949 [T]

0.004

1.89 × 10-5

5

0.674 [C]

0.006

0.013

6

0.014

0.001

2.67 × 10-4

7

2.07 × 10-4

1.43 × 10-5

3.94 × 10-6

8

2.07 × 10-4

1.43 × 10-5

3.94 × 10-6

Significant r2 values are shown in bold.

Table 7

Frequencies of the identified ALOX5 polymorphism haplotypes.

Haplotype

Frequency

5CGG

0.726

4TGG

0.179

6CGG

0.046

5CAG

0.017

4CGG

0.012

6TGG

0.010

3CGG

0.003

3CGA

0.003

5CGA

0.002

3TGG

0.001

5TGG

0.001

7CGG

0.001

8CGG

0.001

Polymorphisms are ordered ALOX5P→g.20C>T→g.8322G>A→g.50778G>A. Haplotypes containing common ALOX5P 5-repeat allele are shown in bold.

Table 8

Frequencies of the identified ALOX5 polymorphism diplotypes.

Diplotype

Frequency

5CGG/5CGG

0.531

4TGG/5CGG

0.253

5CGG/6CGG

0.086

4TGG/4TGG

0.051

5CGG/5CAG

0.031

4CGG/6TGG

0.016

3CGA/5CGG

0.005

6CGG/6CGG

0.003

4CGG/5CGG

0.003

4CGG/5CGA

0.003

5CGG/6TGG

0.003

3CGG/3CGG

0.002

3CGG/5CGG

0.002

3TGG/4TGG

0.002

4CGG/4TGG

0.002

4TGG/5CAG

0.002

5CGG/7CGG

0.002

5CGG/8CGG

0.002

5CGG/5TGG

0.002

Polymorphisms are ordered ALOX5P→g.20C>T→g.8322G>A→g.50778G>A.

Haplotype containing the common allele for all polymorphisms is shown in bold.

Haplotypes containing the common ALOX5P 5-repeat allele but not the common allele of all three SNPs are underlined.

Table 9

Frequencies of the ALOX5 diplotypes containing the common 5-repeat allele of ALOX5P.

Diplotype

Frequency

5CGG/5CGG

0.531

5CGG/5*

0.033

5CGG/*

0.356

5*/*

0.005

*/*

0.075

* indicates all other haplotypes.

Prostate Cancer

The DG8S737 -1 allele and the A allele of rs1447295 were reported by Amundadottir et al. to be in strong linkage disequilibrium (LD) within the Icelandic population (D' = 0.85; r2 = 0.52), the HapMap Caucasian American (D' = 0.72; r2 = 0.29) and African (D' = 0.62; r2 = 0.21) populations, and African Americans (D' = 0.48; r2 = 0.12) [58]. Interestingly, haplotype analysis between the DG8S737 microsatellite and the rs1447295 SNP in this Indian cohort found that whilst the -1 microsatellite allele was in complete LD with the A-allele of rs1447295, the alleles were weakly correlated (Table 10). Rather, it was the rarer -4 allele (19 repeats) of DG8S737 that exhibited the greatest correlation with the A-allele of rs1447295 (Table 10).
Table 10

Linkage disequilibrium (D') and the squared correlation (r2) values between alleles of DG8S737 and rs1447295.

Allele

D'

r2

-10

0.551

0.005

-9

1.000

0.002

-8

1.000

3.88 × 10-4

-7

0.326

0.001

-6

0.160

0.002

-5

0.812

0.055

-4

0.802

0.403 [A]

-3

0.345

0.043

-2

0.417

0.004

-1

1.000

0.048

0

1.000

0.022

1

0.972

0.015

2

1.000

0.008

3

0.866

0.012

4

1.000

0.006

5

1.000

0.002

6

0.551

0.005

7

0.326

0.001

Allele -1 is equivalent to the prostate cancer risk-associated -8 allele reported by Amundadottir et al. that had 22 repeats [58].

In this Indian cohort, the haplotype containing the minor A-allele of the rs1447295 SNP and the -4 allele of DG8S737 was found to represent 7.1% of the haplotypes (Table 11). A further 1.4% carried the -4 allele of DG8S737 but the major C-allele of rs1447295, and 5.8% carried the minor A-allele of rs1447295 but a DG8S737 allele other than -4 (Table 11). The remaining 85.7% of haplotypes were found to contain the major C-allele of rs1447295 and a DG8S737 allele other than -4 (Table 11), which resulted in 73.4% of this Indian cohort being homozygous for these haplotypes (Table 12). We also found that 1.2% of this cohort was homozygous for the haplotype containing the -4 allele of DG8S737 and the minor A-allele of rs1448295, and a further 11.8% were heterozygous for this haplotype (Table 12). Of the latter 11.8%, a sixth had a second haplotype that contained the -4 allele of DG8S737 but not the minor A-allele of rs1448295, and just over half had a haplotype that contained the minor A-allele of rs1448295 but not the -4 allele of DG8S737. Interestingly, there were no haplotypes containing the -1 allele of DG8S737 and the minor A-allele of rs1447295, with the -1 allele found always associated with the C-allele of rs1447295. However, 6.6% of the individuals carried a single copy of both the -1 allele and the A-allele (data not shown).
Table 11

Haplotype frequencies for the -4 allele of the DG8S737 microsatellite and the alleles of rs1447295.

Haplotype

Frequency

-4/C

0.014

-4/A

0.071

*/C

0.857

*/A

0.058

* denotes all other DG8S737 alleles.

Table 12

Diplotype frequencies for the -4 allele of the DG8S737 microsatellite and the alleles of rs1447295.

Diplotype

Frequency

(*/C)/(*/C)

0.734

(*/C)/(*/A)

0.109

(*/C)/(-4/C)

0.026

(*/C)/(-4/A)

0.109

(*/A)/(-4/A)

0.007

(-4/C)/(-4/A)

0.002

(-4/A)/(-4/A)

0.012

* denotes all other DG8S737 alleles.

Ability to taste PTC

The ability to taste PTC has been previously associated with the haplotypes formed by three SNPs in the TAS2R38 gene; g.144C>G, g.784C>T, and g.885G>A [59]. Haplotype analysis of these three SNPs found that the haplotype associated with an inability to taste PTC [59, 60] was almost twice as prevalent in this Indian cohort (AVI) than the taster haplotype (PAV), with recombinants between these two haplotypes being extremely rare by comparison (Table 13). Almost half of the Indian cohort was found to be heterozygous for the taster/non-taster haplotypes AVI/PAV (Table 14). The most common homozygous haplotype was that of the non-taster AVI/AVI, which occurred almost four times as frequently as homozygosity for the taster haplotype PAV/PAV. Only 4.2% of the cohort possessed one of the taster or non-taster haplotypes and one of the recombinant haplotypes (Table 14). No individuals were found to possess only recombinant haplotypes.
Table 13

TAS2R38 haplotypes, their corresponding prototypes, and frequencies in the Asian Indian cohort.

Haplotype

 

Genotype

Prototype

Frequency

GTA

AVI

0.630

CCG

PAV

0.345

GCG

AAV

0.014

GTG

AVV

0.006

CTG

PVV

0.003

CCA

PAI

0.001

Polymorphisms ordered as g.144C>G, g.784C>T, and g.885G>A

Taster amino acid haplotype (prototype) is shown in bold (PAV), and the non-taster haplotype is shown underlined (AVI).

Table 14

Individual diplotypes of the identified TAS2R38 polymorphisms and their corresponding frequencies in the Asian Indian cohort.

Diplotype

 

Genotype

Prototype

Frequency

GTA/CCG

AVI/PAV

0.437

GTA/GTA

AVI/AVI

0.402

CCG/CCG

PAV/PAV

0.120

GCG/GTA

AAV/AVI

0.017

GTG/CCG

AVV/PAV

0.009

GCG/CCG

AAV/PAV

0.007

CTG/GTA

PVV/AVI

0.005

CCG/GTG

PAV/AVV

0.002

GTA/GCG

AVI/AAV

0.002

Polymorphisms ordered as g.144C>G, g.784C>T, and g.885G>A

Taster haplotype is shown in bold (PAV), and the non-taster haplotype is shown underlined (AVI).

As observed above, the Kashmiri group had the highest MAFs for all three TAS2R38 SNPs and the Kannada group had the lowest (Table 4). Based on their relative frequencies, the Kashmiri would therefore be expected to have twice the number of tasters as the Kannada. The Kashmiri are one of the most northerly groups and the Kannada one of the most southerly (Figure 1), suggesting a possible latitudinal cline for the ability to taste PTC, with the greatest number of tasters in the northern groups and the least in the southern groups. This is supported by the MAF of the three SNPs in the other Indian language groups (Figure 3; Table 15). However, only the TAS2R38 g.144G>C SNP follows the same trend in both the Indian groups and in the other available world populations (Figure 3A; see Additional file 1, Table S18). For both the TAS2R38 g.784C>T and g.885A>G, the trend observed with the other world populations is the opposite of that observed with the Indian language groups (Figures 3B &3C, respectively; see Additional file 1, Tables S19 & S20, respectively). For example, the Indian groups display a decrease in MAF as the distance from the equator decreases, whereas other world populations demonstrate an increase in MAF as the distance from the equator decreases. The correlation between frequency and latitude was found to be the strongest for the TAS2R38 g.144G>C SNP, both when the Indian groups and the other world populations were considered separately and when they were combined (Table 15; Figure 3A &3B). The correlation was weakest for the TAS2R38 g.885A>G SNP, where it was still appreciable for the Indian groups, but less so for the other world populations (Table 15; Figure 3C).
Table 15

Correlation coefficients of the MAFs of the three TAS2R38 SNPs with latitude.

Polymorphism

Population

Adjusted Spearman Rank Correlation Score

Adjusted r2

g.144G>C

AIP

0.535*

0.186

 

Others

0.386*

0.126

 

Others + AIP

0.369**

0.154

g.784C>T

AIP

0.474

0.180

 

Others

-0.670**

0.429

 

Others + AIP

-0.305*

0.085

g.885A>G

AIP

0.433

0.118

 

Others

-0.241

0.055

 

Others + AIP

0.063

-0.017

* significant at 0.05, ** significant at 0.01

AIP = Asian Indian population

Others = data from non-Asian Indian populations

For sample sizes, please see Additional file 1, Tables S18, S19, §20, for g.144G>C, g.784C>T, and g.885A>G, respectively.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-13/MediaObjects/12863_2007_Article_579_Fig3_HTML.jpg
Figure 3

The correlation between latitude and minor allele frequency of the TAS2R38 SNPs (A) g.144G>C, (B) g.784C>T, and (C) g.885A>G. Correlation coefficients for each graph can be found in Table 15.

Discussion

We have reported here the frequencies of common polymorphisms associated with an increased risk of several diseases or phenotypes, within a cohort of 576 Indian-born Asian Indians. It is important to note that because the study participants were born in India, we refer to the individuals and populations as being "Asian Indian" or "Indian"; however, it is important to note that because the Asian Indian individuals were sampled in the United States, and therefore do not represent a random sample from India or all of the different castes, tribes, or language groups, some biases may be introduced when extrapolating the results to India as a whole [10]. It is also important to note that because Hindi is spoken by 43 percent of the Indian population, primarily in the states of Northern India, heterogeneity will be present between its various constituent population groups. Our Hindi language group is a sample of individuals who emigrated from a restricted region of India, and therefore biases may be introduced when extrapolating their results to Hindi speakers as a whole.

All polymorphisms, with the exception of ALOX5 g.20C>T, were found to be in HWE. The deviation away from HWE for the ALOX5 g.20C>T SNP is most likely due to chance, but given its low frequency we cannot exclude the possibility of genetic drift, differing selection between the groups, or the small sample size of all but one (Gujarati) of our language groups. Genotyping error could be another possible explanation, but the extremely high accuracy of the TaqMan genotyping assay [61] and the 100% concordance of replicated genotypes makes this explanation unlikely.

No significant population subdivision was identified by FST between the different Indian language groups (Tables 2, 3, &4). However, variation in the MAF of some of the polymorphisms between the different language groups was observed. Furthermore, the ALOX5 g.8322G>A and g.50778G>A SNPs, as well as the PTPN22 g.36677C>T SNP, were found to be present in some but not all of the Indian language groups investigated here. There was no discernible pattern to presence/absence of the minor allele of these SNPs with regard to the location of these groups within India (Figure 1), and it is possible that their presence or absence is the result of either the different origins of the language groups during the peopling of India and strengthened by their practice of endogamy [62, 63], or admixture introduced by the differential migration of non-Indian populations into regions of India during the course of its history [64, 65]. With the exception of the Gujaratis, the small sample size of our individual language groups could also be responsible for this observation as we may be under powered to detect the minor allele in these groups. However, the absence of the ALOX5 g.8322G>A, ALOX5 g.50778G>A, and PTPN22 g.36677C>T SNPs in the Gujarati language group, for which we have around seven times the number of individuals of the other groups, would suggest that their absence in the other groups may not necessarily be due to a lack of a sufficient sample size for their detection. If this observed variation in the MAF of some of the polymorphisms between the different language groups is confirmed, then this could impact the design of future case-control studies in India as it would suggest that the inclusion of a disproportionate number of individuals from a particular language group in either the case or control groups could lead to missed or spurious associations.

Interestingly, the MAF of the atherosclerosis (ALOX5P, ALOX5 g.20C>T, g.8322G>A, and g.50778G>A), type-1 and type-2 diabetes (CAPN10 g.4834T>C, TCF7L2 g.98386G>T, and PTPN22 g.36677C>T), and Hirschsprung disease (RET g.9349G>A) associated polymorphisms were found to be similar to those of European populations. Similarly, the frequencies of variants associated with AMD (CFH g.37989T>C and LOC388715 g.205G>T) and prostate cancer (DG8S737 and rs1447295 were similar to those of African populations. However, none showed a high similarity to the populations of Eastern Asia. This could reflect a greater degree of non-Asian compared to Asian gene flow into India during the course of its history; however, if higher caste is correlated with a European or western Asian component of ancestry, this could be a reflection of our sampling of Asian Indian immigrants in the United States that are likely biased towards the higher castes.

The frequency of the CYP3A5 g.6980G>A, AGT g.802C>T, and GNB3 g.825C>T polymorphisms have been reported to vary significantly between different populations/ethnicities in a cline that correlates with variation in the latitudinal position of the populations [50, 55, 56]. We have shown that whilst the MAF of the CYP3A5 g.6980G>A SNP in the Indian groups agrees with the trend identified in other world populations, the inclusion of the Indian data reduces the previously reported correlation between the frequency of the AGT g.802C>T and GNB3 g.4423C>T SNPs and latitude. The association of the MAF of these three SNPs with changes in latitude, purportedly due to their effects on sodium homeostasis, suggests that the relevant selective pressures within India are not themselves strongly correlated with latitude.

An association between changes away from the common allele (5 repeats) in a repeat-polymorphism (ALOX5P) comprised of Sp1 binding sites in the promoter region of ALOX5 and atherosclerosis has been previously reported [57]. Analysis of ALOX5P in the Indian cohort identified six alleles; 56.4% of individuals were homozygous for the common allele of five Sp1-binding site repeats, and 92.5% of individuals had at least one copy of the common allele. The remaining 7.5% of individuals had two variant alleles, which is much lower than the incidence of CAD in the Indian population [16, 66]. This is higher than the 5.96% reported for a cohort of individuals of mixed-ethnicity, who reported 94.0% of individuals with at least one copy of the common allele [57]. The same study also reported that variant alleles were more common in blacks (24.0%) and Asians or Pacific Islanders (19.4%) than in Hispanic (3.6%) and non-Hispanic whites (3.1%). The frequency of variants in this Indian cohort would suggest that their frequency is closer to Caucasians than Asians.

Three coding SNPs have also been identified in the ALOX5 gene (g.20C>T, g.8322G>A, and g.50778G>A) and these appear to be in partial or complete LD with the alleles of ALOX5P (HA; unpublished data). Haplotype analysis of the four ALOX5 polymorphisms in this Indian cohort found that there is LD between three alleles of ALOX5P – the major and minor alleles of the g.20C>T SNP (5 and 4 repeats, respectively), and the major allele of g.50778G>A SNP (3 repeats). Haplotypes containing variant alleles of ALOX5P are present in almost half of the cohort, with 8.1% of the cohort homozygous for haplotypes carrying a variant allele.

Analysis of the type-2 diabetes-associated SNPs CAPN10 g.4834T>C and TCF7L2 g.98386G>T, found their MAFs (23.1% and 24.8%, respectively) to be almost twice as common as type-2 diabetes in the Indian population (11.6%) [22]. Two recent studies have reported that the TCF7L2 g.98386G>T variant is strongly associated with type-2 diabetes risk in a Asian Indians [67, 68], and our frequency of this variant (24.8%) in this Indian cohort is close to that reported for control subjects (22%) [68]. The PTPN22 g.36677C>T SNP has a low MAF (0.009) in our Indian cohort, comparable to that previously reported (0.012) [69] and almost one hundred fold higher than the reported prevalence of type-1 diabetes in the Indian population (0.011–0.026%) [30, 31]. The minor allele of PTPN22 g.36677C>T SNP is absent in other Asian populations, but present in those of European origin (see Additional file 1, Table S9), suggesting that its presence in Asian Indians is through the introduction of admixture by migrating individuals of European origin into India during the course of its history.

Interestingly, the most common allele of the DG8S737 microsatellite in this Indian cohort has been associated with an increased risk of prostate cancer in other populations (-1 allele) [58]. However, whilst this allele has been reported to be in strong LD with the minor A-allele of SNP rs1447295 in other populations, we have found that a different allele (-4 allele) is in strong LD with the minor A-allele of SNP rs1447295 in this Indian cohort. The frequency of these polymorphisms is also still far higher than the frequency of prostate cancer in the Asian Indian population (10 per 100,000) [28, 29]. Further studies on Asian Indian prostate cancer cases may allow for the true causative variant to be identified.

The moderate frequency of the Hirschsprung disease-associated minor allele of RET g.9349G>A in the Indian cohort is in contrast with the relatively low frequency of the disease in India (2.8 per 10,000 births) [25]. However, the MAF of the CFH g.37989T>C and LOC387715 g.205G>T SNPs in this Indian cohort is in agreement with the low frequency of AMD in the Indian population [27].

Previous investigation suggests that the PAV "taster" haplotype is dominant over the other recessive haplotypes including the "non-taster" AVI haplotype as PAV/AVI individuals were largely able to taste PTC [59, 70]. We would therefore expect, based upon the frequencies of the different haplotypes in this Indian cohort, that 57.4% of the population would be able to taste PTC (Table 16). This value is very close to the percentage of the population identified as able to taste PTC in previously reported studies: Gujarat (57.1%), Kerala (51.6%), Maharashtra (54.9%, 57.6% and 63.7%), Tamil Nadu (59.2%), and Uttar Pradesh (69.1%) [7175]. These data would suggest that the lower than expected number of Indians able to taste PTC when compared with the average of 75% in worldwide populations [76, 77] is due to an increased presence of the non-taster haplotype in this population. In agreement with this notion, we observed that the frequency of the haplotype of the three SNPs in the TAS2R38 gene associated with the ability to taste PTC in this Indian cohort is almost 20% lower than other world populations.
Table 16

Frequencies of taster and non-taster associated diplotypes in the Asian Indian cohort.

Diplotype

 

Genotype

Prototype

Frequency

CCG/CCG

PAV/PAV

0.120

CCG/*

PAV/*

0.017

GTA/CCG

AVI/PAV

0.437

GTA/*

AVI/*

0.024

GTA/GTA

AVI/AVI

0.402

Polymorphisms ordered as g.144C>G, g.784C>T, and g.885G>A

Taster haplotype is shown in bold (PAV), and the non-taster haplotype is shown underlined (AVI).

Interestingly, the MAFs of the three TAS2R38 SNPs were found to vary in the Indian groups in a cline that correlated with the latitudinal variation of the groups. The MAFs of all three SNPs were higher in the more northerly Indian groups than in the more southerly groups, suggesting a greater frequency of tasters in the former than the latter. However, this trend was only observed in the other world populations for the TAS2R38 g.144G>C SNP, with the trend reversed for both the TAS2R38 g.784C>T and g.885A>G SNPs.

One possible explanation for the differences between the trends in the Indian groups and other world populations could be the differential migration of non-Indian individuals into different parts of India during the course of its history. This could result in variation in the degree and source of the admixture introduced into the different language groups such that their frequencies deviate from the trends of other populations. There could also be selective pressures that are unique to, more important in, or different in action in, India than the other world populations, and that also share a strong correlation with latitude.

It has been previously reported that balancing selection is acting on the TAS2R38 receptor gene, possibly due to the different taster and non-taster alleles encoding receptors that recognize different, but equally important, ligands [60, 77]. The latitudinal cline we have identified in our Indian language groups with regards to the taster/non-taster allele frequencies could therefore be the result of the strength of the selective pressures exerted by these ligands affecting individuals in the north and south of India differently, but for reasons that are distinct from other world populations. Another possible selective pressure relates to bitter taste perception and the acceptance of foods containing increasing amounts of spice [78]. This is of particular interest given that patterns of spice use have been reported to both vary between populations and regions, and also to have a significant latitude correlation, where more spices tend to be used in warmer climates purportedly due to their powerful antimicrobial action [79]. The climate differences between the more arid south of India and the more mountainous north, and the incorporation of a lot of spices in Indian cooking, makes this a possible explanation for our latitudinal correlation. It has also been proposed that the local adaptation in human bitter taste receptor genes is common and has been driven by the fitness advantages of avoiding toxic, bitter compounds found in plants [80]. If the composition and abundance of ligands to which the different taster and non-taster alleles of the TAS2R38 gene vary geographically in India in a cline associated with latitude, this could also explain our observed latitudinal correlation.

The MAF for the SLC24A5 g.13242G>A polymorphism (0.114) in the Indian population is closer to that of Caucasian European populations (0.000–0.020) than to that of East Asians (0.979–0.989) and Africans (0.730–0.980) (see Additional file 1, Table S14). This is surprising given that, as a whole, people of Indian origin have darker skin tone compared to Europeans. The East Asian and African populations share a similarly high frequency of the minor allele of the SLC24A5 g.13242G>A SNP, which in itself is surprising as East Asians are typically of much lighter skin than Africans. It is also interesting that the t-test suggested a greater similarity in MAF between Indians and those of populations in the Americas (Puerto Rican, Mexican, and Amerindian), who are of a similar skin tone (Table 4). The presence of the minor allele of the SLC24A5 g.13242G>A SNP cannot therefore, completely explain Indian skin color, making the investigation of polymorphisms in additional genes implicated in skin pigmentation [81] essential to understanding the intricacies of their skin color.

We have found that, with the exception of the TAS2R38 haplotypes associated with the ability to taste PTC, the frequency of the disease/trait-associated minor alleles of the polymorphisms investigated in this Indian cohort did not correlate with the reported disease/trait prevalence in the Indian population. This is unsurprising given that, with the exception of the ability to taste PTC, all are known to be complex diseases/traits that are the result of the combined effect of variants at multiple genetic loci and environmental factors. Variation in genetic backgrounds and environmental heterogeneity between populations will likely have a modifying effect on the correlation of the allele frequency of polymorphisms with disease/trait prevalence as their contribution to the presentation of the disease/trait varies. The degree of complexity between these disease/traits will also vary, as some, such as CAD, are known to be highly complex whereas others, such as AMD, may be less complex. One might therefore not expect the correlation of factors associated with CAD to be as strong as those associated with AMD, and therefore the lack of a strong correlation between many of these polymorphisms and their respective complex disease/trait prevalence most likely indicates that the environmental and/or other genetic factors make a stronger contribution to the presentation of the disease/trait in Indians than do these variants alone. Interestingly, the two polymorphisms associated with AMD (CFH g.37989T>C &LOC387715 g.205G>T), whilst not showing strong correlation between their disease-associated MAF and reported disease prevalence in the Indian population, did show a frequency that correlated with the MAF of these SNPs in other populations and the prevalence of AMD in those populations, suggesting that they may share similar risk factors.

Conclusion

The Asian Indian population represents a large population within which many complex-trait disorders are found at a high frequency. The high prevalence of endogamy and relatively low admixture present in this population distinguishes it from most other populations presently used in genetic studies as it represents a distinct genetic background that is largely unaffected by outside admixture. In some cases, such as coronary artery disease, Asian Indians exhibit unique phenotypic characteristics that distinguish them from the other populations, suggesting that unique causative factors underlie this and possibly other related diseases. Our results suggest that caution should be used when treating the Asian Indian population as a single population in genetic studies using SNPs. The overall difference in minor allele frequency of the polymorphisms investigated was small between the different language groups, but some inter-group variation was observed. However, caution is warranted in interpreting our results as whilst our Indian cohort is likely to be reasonably representative of first-generation individuals of Asian Indian descent currently residing in the United States, individuals from relatively mobile populations and those of higher caste and socioeconomic status are likely overrepresented within this cohort. Such individuals likely do not provide a random sample of the source populations in India and therefore if variables such as caste and socioeconomic status do have a significant effect on genotype frequency in India, this will not be reflected in these results. Additionally, if higher caste is correlated with a European or western Asian component of ancestry, a sample in the United States may be biased towards finding a greater similarity of populations from India to those of the Europe/Middle East rather than to those of East Asia [10], as we have discovered for many of the polymorphisms reported here. We must also use caution when interpreting the variation at the PTPN22 g.1825C>T and ALOX5 g.50778G>A loci between the 15 language groups, as the small sample size for each group, with the exception of the Gujarati, is a limiting factor in our ability to detect polymorphisms with a low minor allele frequency. However, our results do support the inclusion of the Indian population in modern genetic association studies as these data suggest that Asian Indians likely exhibit unique genotype as well as phenotype characteristics that may yield new insight into the underlying causes of diseases that are not available in other populations.

Methods

Study population

The Asian Indian cohort used in this study was as previously described [10]. Our sample consisted of an expanded population of 576 individuals of Indian-born Asian Indians living in the United States who have largely emigrated from Indian cities. This population was designed so that when subdividing study participants by their self-reported primary spoken Indian language, 15 languages (Assamese, Bengali, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Marathi, Marwari, Oriya, Parsi, Punjabi, Tamil, and Telugu), each having a relatively localized distribution within India (Figure 1), would be well-represented. The number of Gujarati individuals sampled (181) was larger than the other language groups (ca. 25–30) to examine rare minor allele frequencies. Three other language groups (Dogri, Sindhi, and Tulu) were present in the population but not at a significant number to be analyzed independently, but these were retained for whole cohort analysis. A sample size of at least 24 per population has been found to be reasonably suitable for population structure analysis [82, 83], as well as for microsatellite studies of linkage disequilibrium [84], suggesting that it is suitable for this study.

Marker genotyping

All single nucleotide polymorphisms (SNPs) were interrogated using TaqMan allelic discrimination assays (Applied Biosystems, Foster City, CA) following the manufacturer's recommended protocol for a 5 μl reaction volume and 2ng of dried DNA using an Applied Biosystems (Foster City, CA) 7900HT fast real-time PCR system. ABgene (Rochester, New York) ABsolute™ QPCR ROX (500 nM) mix was used for all assays. Pre-designed assays were available for all SNPs except CFH g.37989T>C (Probe: 5'-TTTCTTCCAT [G/A]ATTTTG-3'; Forward primer: 5'-CTTTATTTATTTATCATTGTTATGGTCCTTAGGAAAATGTTATTT-3'; Reverse primer: 5'-GGCAGGCAACGTCTATAGATTTACC-3'), ALOX5 g.20C>T (Probe: 5'-TGGCCAC [G/A]GTGACC-3'; Forward primer: 5'-CGCCATGCCCTCCTACAC-3'; Reverse primer: 5'-AGTGCCGGCGAACCA-3'), ALOX5 g.8322G>A (Probe: 5'-CTGAAGAC[G/A]CCCCACG-3'; Forward primer: 5'-TGAATGACGACTGGTACCTGAAGTA-3'; Reverse primer: 5'-GGTGATCCAGCGGTAGCA-3'), and ALOX5 g.50778G>A (Probe: 5'-CTGCCC [G/A]AGAAGC-3'; Forward primer: 5'-AGACCTGATGTTTGGCTACCAGTT-3'; Reverse primer: 5'-CGCTCCAGGCTGCACTCTA-3'), for which custom assays were designed using the Applied Biosystems (Foster City, CA) assay-by-design service. All genotypes were determined using the AutoCall clustering algorithm of the Sequence Detection Systems (SDS) software version 2.1 (Applied Biosystems, Foster City, CA) with a quality value threshold of 95. Genotypes that could not be called using this algorithm and threshold were repeated along with a small number of successfully genotyped samples to validate their genotypes and confirm assay performance in the repeated data. No switches in genotypes were observed with these replicated samples.

Microsatellite markers were genotyped using fluorescent primers on an ABI3100 genetic analyzer (Applied Biosystems, Foster City, CA) and analyzed using Genotyper version 3.7 (Applied Biosystems, Foster City, CA). Primers for DG8S737 were as previously described [58] and PCR was performed using a Biometra (Goettingen, Germany) T-Gradient thermal cycler and the following cycle parameters: 95°C 5 mins followed by 35 cycles of 95°C for 45 sec, 60°C for 45 sec, 72°C for 45 sec, and a final hold at 72°C 10 mins before holding at 4°C. New England Biolabs (Ipswich, MA) Taq DNA polymerase (1U) and manufacturer supplied 10× buffer (including 1.5 mM MgCl2) was used along with 4 pmol of each primer, 30 μmol of each dNTP (Invitrogen, Carlsbad, CA), and 30 ng DNA in a 15 μl reaction volume. Alleles of DG8S737 were assigned based upon the change in the number of repeats away from that of the reference sequence in the NCBI database (23 AC dinucleotide repeats; Acc. # NT_008046.15: 148848–149016).

Primers for the ALOX5 promoter microsatellite (ALOX5P) were as previously described [85] but with the inclusion of the GTGTCTT pig-tail [86] and PCR was performed in an Applied Biosystems (Foster City, CA) GeneAmp 9700 thermal cycler using the following cycle parameters: 95°C 12 mins followed by 10 cycles of 94°C for 1 min, 68°C for 2 min, followed by 25 cycles of 94°C for 30 sec, 60°C for 30 sec, 72°C for 45 sec, and a final hold at 72°C 5 mins before holding at 4°C. Applied Biosystems (Foster City, CA) AmpliTaq Gold DNA polymerase (1.2U) and manufacturer supplied 10× buffer was used along with 1.5 mM MgCl2, 2% DMSO, 400 nmol of each primer, 30 μmol of dATP, dUTP, and dCTP (Invitrogen, Carlsbad, CA), 62.5 μmol 7-deaza-dGTP (New England Biolabs, Ipswich, MA), and 30 ng DNA in a 25 μl reaction volume. Labels for the alleles of ALOX5P were assigned based upon the number of hexanucleotide Sp1-binding sites that were present.

Haplotype analysis

Haplotype phase was estimated for the TAS2R38 g.144G>C, g.784C>T, and g.885A>G SNPs using fastPHASE version 1.1.4 [87] using the following settings; 50 random starts of the EM algorithm, 50 iterations of the EM algorithm, 200 haplotypes sampled from the "posterior" distribution obtained from a particular random start of the EM algorithm, and haplotype frequencies estimated by Monte Carlo methods by sampling from the observed genotypes 10,000 times per diploid individual. As fastPHASE is unable to analyze microsatellite data, PHASE version 2.1 [88, 89] was used to estimate haplotype phase for the ALOX5P and the three coding SNPs (g.20C>T, g.8322G>A, and g.50778G>A) and also for DG8S737 and rs1447295 using the following settings; a burn-in of 25,000 iterations followed by 10,000 main iterations with a thinning interval of 100 iterations. The final run used 10 times this number of burn-in and main iterations. Haplotypes were constructed using the hybrid haplotype reconstruction model that uses a model that ignores the decay of LD with distance [88] for preliminary computations, and a different model that makes explicit allowance for the decay of LD with distance [90] for the final computations.

Statistical analysis

All statistics were calculated in R version 2.3.1 [91] unless otherwise stated. To test that all SNPs are in equilibrium within the Asian Indian cohort, a Hardy-Weinberg equilibrium (HWE) constant was calculated for each polymorphism using the HWE.test function in the genetics R-package [92]. A Pearson's χ2 test with simulated p-value (based on 10000 iterations) was calculated for all HWE statistics as this avoids the reliance on the assumptions of the underlying Chi-square approximation, which is particularly important when some allele pairs have small counts [92]. An HWE score of 0 equates to the polymorphism being in complete Hardy-Weinberg equilibrium, and a score of 1 equates to the polymorphism being in complete Hardy-Weinberg disequilibrium. A standard error (SE) for the estimated allele frequency for each SNP was calculated by using the formula: SE = s/√n, where s is the estimated standard deviation and n is the size of the sample.

Weir and Cockerham FST test statistics [93] were calculated for all SNP data as previously described [94] using R to measure the effect of population subdivision and the overall genetic divergence among these groups, where an FST of 0 means no genetic divergence, and 1 means extreme genetic divergence, between the groups, and an FST of up to 0.05 represents a relatively small level of genetic differentiation. Weir and Cockerham FST test statistics for the microsatellite data were calculated using FSTAT version 2.9.3.2 [95]. To estimate the significance of these FST values, a probability value (p) was calculated for each FST value from a χ2 distribution using the pchisq function in the Stats R-package using 14 degrees of freedom (number of Indian language groups minus 1) [96]. As it is possible for the unbiased estimate of FST used here to assume negative values, which does not have a biological interpretation, we set negative values of FST to 0 [94].

As it was unrealistic to assume that the variance within the Asian Indian cohort is equal to that of other populations, a Welch modified two-sample t-test with unequal variances was calculated between the minor allele frequency in the Indian cohort and those previously reported for other populations. This was performed using the t.test function in the Stats R-package with a 95% confidence interval to test whether or not the minor allele frequency in the Asian Indian cohort were significantly different from those of other populations. To give as accurate a representation of the MAF in the Asian Indian cohort, the MAF of each group was included in this analysis. Data on other populations was obtained from prior publications and the National Center for Biotechnology Information's (NCBI) dbSNP database [97, 98]. These populations were grouped into the following geographical regions for comparison: Africa, Asia (including Central, Southern, Eastern, and South-East Asian populations), Americas (native populations of South and Central America, and native populations of North America), Europe (including non-native European American populations, and Russia), Oceania (Australia, New Zealand, and the Pacific islands), except for the analysis of Cyp3A5 g.6980G>A and AGT g.802C>T where there were sufficient Asian populations to allow Asia to be split into Central/South Asia and East Asia (East and South-Eastern Asia), and there were also sufficient populations to include a Middle East.

To test whether the polymorphisms associated with atherosclerosis (ALOX5P and the three ALOX5 coding-SNPs g.20C>T, g.8322G>A, and g.50778G>A), ability to taste PTC (the three TAS2R38 coding-SNPs g.144G>C, g.784C>T, and g.885A>G), and prostate cancer (the DG8S737 microsatellite and rs1447295 SNP), were in linkage disequilibrium, the linkage disequilibrium measure (D') and the squared correlation measure of linkage disequilibrium (r2) were calculated between the respective polymorphisms using the java based linkage disequilibrium plotter JLIN [99]. For the ALOX5P and DG8S737 microsatellite markers, each allele was tested individually with all other alleles collapsed into a single "super-allele".

Adjusted Spearman's rank correlation coefficients and squared correlation coefficients (r2) for the association of the CYP3A5 g.6980G>A, AGT g.802C>T, and GNB3 g.825C>T SNPs, and the TAS2R38 g.144G>C, g.784C>T, and g.885A>G SNPs, with absolute latitude were calculated using SPSS version 15 (SPSS Inc.; Chicago, IL). Graphs of their MAF against absolute latitude (Figures 2 &3) were plotted using SigmaPlot version 9 (Systat Software, Inc.; San Jose, CA).

Abbreviations

(AMD): 

Age-related macular degeneration

(CAD): 

Coronary artery disease

(HWE): 

Hardy-Weinberg equilibrium

(LD): 

Linkage disequilibrium

(MAF): 

Minor allele frequency

(PTC): 

Phenylthiocarbamide.

Declarations

Acknowledgements

The authors would like to Dr Noah Rosenberg (University of Michigan) for his helpful comments in the preparation of this manuscript. This investigation was supported by a pilot grant award from the Center of Excellence in Genomic Science, University of Southern California (to T.J.P.) and was conducted in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 (RR10600-01, CA62528-01, RR14514-01) from the National Center for Research Resources, National Institutes of Health. A.D. and D.W. were partially supported by National Institute of Health grant GM61393 and H.A. was supported by National Institute of Health grant R01 HL079353.

Authors’ Affiliations

(1)
Institute for Genetic Medicine, Keck School of Medicine, University of Southern California
(2)
Department: of Preventive Medicine, Keck School of Medicine, University of Southern California
(3)
Department of Biochemistry and Molecular Biology, University of Southern California
(4)
Center for Craniofacial Molecular Biology, Keck School of Medicine, University of Southern California
(5)
Department of Human Genetics, University of Chicago

References

  1. Population Reference Bureau. [http://www.prb.org]
  2. Bittles AH: Endogamy, consanguinity and community disease profiles. Community Genet. 2005, 8 (1): 17-20. 10.1159/000083332.PubMedGoogle Scholar
  3. Mosely C, Asher RE: Atlas of the World's Languages. 2007, London , Routledge, 2ndGoogle Scholar
  4. Thapar R: A history of India: Volume I. 1990, London, UK , Penguin Books.Google Scholar
  5. Taraporevala S: Zoroastrians of India. Parsis: A Photographic Journey. 2000, Mumbai, India , Good BooksGoogle Scholar
  6. Taraporevala S: Parsis: The Zoroastrians of India: A Photographic Journey. 2004, Mumbai, India , Good BooksGoogle Scholar
  7. Qamar R, Ayub Q, Mohyuddin A, Helgason A, Mazhar K, Mansoor A, Zerjal T, Tyler-Smith C, Mehdi SQ: Y-chromosomal DNA variation in Pakistan. Am J Hum Genet. 2002, 70 (5): 1107-1124. 10.1086/339929.PubMed CentralPubMedGoogle Scholar
  8. Quintana-Murci L, Chaix R, Wells RS, Behar DM, Sayar H, Scozzari R, Rengo C, Al-Zahery N, Semino O, Santachiara-Benerecetti AS, Coppa A, Ayub Q, Mohyuddin A, Tyler-Smith C, Qasim Mehdi S, Torroni A, McElreavey K: Where west meets east: the complex mtDNA landscape of the southwest and Central Asian corridor. Am J Hum Genet. 2004, 74 (5): 827-845. 10.1086/383236.PubMed CentralPubMedGoogle Scholar
  9. Di Rienzo A, Hudson RR: An evolutionary framework for common diseases: the ancestral-susceptibility model. Trends Genet. 2005, 21 (11): 596-601. 10.1016/j.tig.2005.08.007.PubMedGoogle Scholar
  10. Rosenberg NA, Mahajan S, Gonzales-Quevedo C, Nino-Rosales L, Ninis V, Das P, Hedge M, Molinari L, Zapata G, Weber JL, Belmont JW, Patel PI: Low levels of genetic divergence across populations of India. PLoS Genet. 2006, 2 (12): e215-10.1371/journal.pgen.0020215.PubMed CentralPubMedGoogle Scholar
  11. Wilson PW, Christiansen JC, Anderson KM, Kannel WB: Impact of national guidelines for cholesterol risk factor screening. The Framingham Offspring Study. J Am Medical Assoc. 1989, 262 (1): 41-44. 10.1001/jama.262.1.41.Google Scholar
  12. Enas EA: Prevalence of coronary heart disease and its risk factors in Asian Indian migrants to the United States. Proc Int Symp Atherosclerosis. 1991, 6-11.Google Scholar
  13. Enas EA, Mehta J: Malignant coronary artery disease in young Asian Indians: thoughts on pathogenesis, prevention, and therapy. Coronary Artery Disease in Asian Indians (CADI) study. Clin Cardiol. 1995, 18 (3): 131-135.PubMedGoogle Scholar
  14. Enas EA, Garg A, Davidson MA, Nair VM, Huet BA, Yusuf S: Coronary heart disease and its risk factors in first-generation immigrant Asian Indians to the United States of America. Indian Heart J. 1996, 48 (4): 343-353.PubMedGoogle Scholar
  15. Enas EA, Senthilkumar A: Coronary Artery Disease in Asian Indians: An Update and Review. The Internet Journal of Cardiology. 2001, 1 (2):
  16. Gupta R, Prakash H, Majumdar S, Sharma S, Gupta VP: Prevalence of coronary heart disease and coronary risk factors in an urban population of Rajasthan. Indian Heart J. 1995, 47 (4): 331-338.PubMedGoogle Scholar
  17. Deepa M, Farooq S, Datta M, Deepa R, Mohan V: Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: the Chennai Urban Rural Epidemiology Study (CURES-34). Diabetes Metab Res Rev. 2007, 23 (2): 127-134. 10.1002/dmrr.658.PubMedGoogle Scholar
  18. Gupta R: Meta-analysis of prevalence of hypertension in India. Indian Heart J. 1997, 49 (1): 43-48.PubMedGoogle Scholar
  19. Kearney PM, Whelton M, Reynolds K, Whelton PK, He J: Worldwide prevalence of hypertension: a systematic review. J Hypertens. 2004, 22 (1): 11-19. 10.1097/00004872-200401000-00003.PubMedGoogle Scholar
  20. Gupta R, Misra A: Type 2 Diabetes in India: Regional Disparities. Br J Diabetes Vasc Dis. 2007, 7 (1): 12-16.Google Scholar
  21. Mohan V, Deepa M, Deepa R, Shanthirani CS, Farooq S, Ganesan A, Datta M: Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India--the Chennai Urban Rural Epidemiology Study (CURES-17). Diabetologia. 2006, 49 (6): 1175-1178. 10.1007/s00125-006-0219-2.PubMedGoogle Scholar
  22. Ramachandran A, Snehalatha C, Latha E, Vijay V, Viswanathan M: Rising prevalence of NIDDM in an urban population in India. Diabetologia. 1997, 40 (2): 232-237. 10.1007/s001250050668.PubMedGoogle Scholar
  23. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, Rao PV, Yajnik CS, Prasanna Kumar KM, Nair JD: High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia. 2001, 44 (9): 1094-1101. 10.1007/s001250100627.PubMedGoogle Scholar
  24. Hirschsprung H: Stuhltragheit Neugeborener in Folge von Dilatation und Hypertrophie des Colons. Jahrb Kinderheilk. 1888, 27: 1-7.Google Scholar
  25. Watanatittan S, Suwatanaviroj A, Niramis R, Limprutithum T: Hirschsprung's disease : Diagnostic experience. Bulletin of the Department of Medical Services. 1991, 16 (1): 9-20.Google Scholar
  26. Streiff EB, Babel J: La senescence de la retine. Prog Ophthal. 1963, 13: 1-75.Google Scholar
  27. Krishnaiah S, Das T, Nirmalan PK, Nutheti R, Shamanna BR, Rao GN, Thomas R: Risk Factors for Age-Related Macular Degeneration: Findings from the Andhra Pradesh Eye Disease Study in South India. Invest Ophthalmol Vis Sci. 2005, 46 (12): 4442-4449. 10.1167/iovs.05-0853.PubMedGoogle Scholar
  28. Hsing AW, Tsao L, Devesa SS: International trends and patterns of prostate cancer incidence and mortality. Int J Cancer. 2000, 85 (1): 60-67. 10.1002/(SICI)1097-0215(20000101)85:1<60::AID-IJC11>3.0.CO;2-B.PubMedGoogle Scholar
  29. Quinn M, Babb P: Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part I: international comparisons. BJU International. 2002, 90 (2): 162-173. 10.1046/j.1464-410X.2002.2822.x.PubMedGoogle Scholar
  30. Ramachandran A, Snehalatha C, Abdul Khader OM, Joseph TA, Viswanathan M: Prevalence of childhood diabetes in an urban population in south India. Diabetes Res Clin Pract. 1992, 17 (3): 227-231. 10.1016/0168-8227(92)90098-C.PubMedGoogle Scholar
  31. Ramachandran A, Snehalatha C, Krishnaswamy CV: Incidence of IDDM in children in urban population in Southern India. Diabetes Res Clin Pract. 1996, 34 (2): 79-82. 10.1016/S0168-8227(96)01338-1.PubMedGoogle Scholar
  32. Amos AF, McCarty DJ, Zimmet P: The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabet Med. 1997, 14 Suppl 5: S1-85.PubMedGoogle Scholar
  33. Garcia-Barcelo MM, Sham MH, Lui VC, Chen BL, Song YQ, Lee WS, Yung SK, Romeo G, Tam PK: Chinese patients with sporadic Hirschsprung's disease are predominantly represented by a single RET haplotype. J Med Genet. 2003, 40 (11): e122-10.1136/jmg.40.11.e122.PubMed CentralPubMedGoogle Scholar
  34. Gronberg H: Prostate cancer epidemiology. Lancet. 2003, 361 (9360): 859-864. 10.1016/S0140-6736(03)12713-4.PubMedGoogle Scholar
  35. Kim UK, Breslin PAS, Reed D, Drayna D: Genetics of Human Taste Perception. J Dent Res. 2004, 83 (6): 448-453.PubMedGoogle Scholar
  36. Meza-Valencia BE, de Lorimier AJ, Person DA: Hirschsprung disease in the U.S. associated Pacific Islands: more common than expected. Hawaii Med J. 2005, 64 (4): 96-98.PubMedGoogle Scholar
  37. Vinereanu D: Risk factors for atherosclerotic disease: present and future. Herz. 2006, 31 Suppl 3: 5-24.PubMedGoogle Scholar
  38. Young JH: Evolution of blood pressure regulation in humans. Curr Hypertens Rep. 2007, 9 (1): 13-18. 10.1007/s11906-007-0004-8.PubMedGoogle Scholar
  39. Klein R: Overview of progress in the epidemiology of age-related macular degeneration. Ophthalmic Epidemiol. 2007, 14 (4): 184-187. 10.1080/09286580701344381.PubMedGoogle Scholar
  40. Asleh SA, Chowers I: Ethnic background as a risk factor for advanced age-related macular degeneration in Israel. Isr Med Assoc J. 2007, 9 (9): 656-658.PubMedGoogle Scholar
  41. World Health Organization. [http://www.who.int/]
  42. Procopiou M, Philippe J: The metabolic syndrome and type 2 diabetes: epidemiological figures and country specificities. Cerebrovasc Dis. 2005, 20 (Suppl 1): 2-8. 10.1159/000088231.PubMedGoogle Scholar
  43. Wild S, Sicree R, Roglic G, Green A, King H: Global Prevalence of Diabetes - Estimates for the year 2000 and projections for 2030. Diabetes Care. 2004, 27 (5): 1047-1053. 10.2337/diacare.27.5.1047.PubMedGoogle Scholar
  44. Motulsky AG, Brunzell JD: Genetics of coronary atherosclerosis. The genetic basis of common diseases. Edited by: King RA, Rotter JI, Motulsky AG. 2002, New York, NY , Oxford University Press, 105 -1126. Second editionGoogle Scholar
  45. Gillespie KM: Type 1 diabetes: pathogenesis and prevention. Canadian Med Assoc J. 2006, 175 (2): 165-170. 10.1503/cmaj.060244.Google Scholar
  46. Wolford JK, Vozarova de Courten B: Genetic basis of type 2 diabetes mellitus: implications for therapy. Treat Endocrinol. 2004, 3 (4): 257-267. 10.2165/00024677-200403040-00007.PubMedGoogle Scholar
  47. Staessen JA, Wang J, Bianchi G, Birkenhager WH: Essential hypertension. The Lancet. 2003, 361 (9369): 1629-1641. 10.1016/S0140-6736(03)13302-8.Google Scholar
  48. Bostwick DG, Burke HB, Djakiew D, Euling S, Ho S, Landolph J, Morrison H, Sonawane B, Shifflett T, Waters DJ, Timms B: Human prostate cancer risk factors. Cancer. 2004, 101 (S10): 2371-2490. 10.1002/cncr.20408.PubMedGoogle Scholar
  49. Chamberlain M, Baird P, Dirani M, Guymer R: Unraveling A Complex Genetic Disease: Age-related Macular Degeneration. Surv Ophthalmol. 2006, 51 (6): 576-586. 10.1016/j.survophthal.2006.08.003.PubMedGoogle Scholar
  50. Thompson EE, Kuttab-Boulos H, Witonsky D, Yang L, Roe BA, Di Rienzo A: CYP3A variation and the evolution of salt-sensitivity variants. Am J Hum Genet. 2004, 75 (6): 1059-1069. 10.1086/426406.PubMed CentralPubMedGoogle Scholar
  51. Roy JN, Lajoie J, Zijenah LS, Barama A, Poirier C, Ward BJ, Roger M: CYP3A5 genetic polymorphims in different ethnic populations. Drug Metab Dispos. 2005, 33 (7): 884-887. 10.1124/dmd.105.003822.PubMedGoogle Scholar
  52. Siffert W, Forster P, Jockel KH, Mvere DA, Brinkmann B, Naber C, Crookes R, Heyns ADP, Epplen JT, Fridey JOY, Freedman BI, Muller N, Stolke D, Sharma AM, Moutaery KA, Grosse-Wilde H, Buerbaum B, Ehrlich T, Ahmad HR, Horsthemke B, Du Toit ED, Tiilikainen A, Ge J, Wang Y, Yang D, Husing J, Rosskopf D: Worldwide Ethnic Distribution of the G Protein {beta}3 Subunit 825T Allele and Its Association with Obesity in Caucasian, Chinese, and Black African Individuals. J Am Soc Nephrol. 1999, 10 (9): 1921-1930.PubMedGoogle Scholar
  53. Siffert W: G protein {beta}3 subunit 825T allele, hypertension, obesity, and diabetic nephropathy. Nephrol Dial Transplant. 2000, 15 (9): 1298-1306. 10.1093/ndt/15.9.1298.PubMedGoogle Scholar
  54. Rosskopf D, Manthey I, Siffert W: Identification and ethnic distribution of major haplotypes in the gene GNB3 encoding the G-protein beta3 subunit. Pharmacogenetics. 2002, 12 (3): 209-220. 10.1097/00008571-200204000-00005.PubMedGoogle Scholar
  55. Beeks E, Kessels AG, Kroon AA, van der Klauw MM, de Leeuw PW: Genetic predisposition to salt-sensitivity: a systematic review. J Hypertens. 2004, 22 (7): 1243-1249. 10.1097/01.hjh.0000125443.28861.0d.PubMedGoogle Scholar
  56. Young JH, Chang YPC, Kim JDO, Chretien JP, Klag MJ, Levine MA, Ruff CB, Wang NY, Chakravarti A: Differential Susceptibility to Hypertension Is Due to Selection during the Out-of-Africa Expansion. PLoS Genetics. 2005, 1 (6): e82-10.1371/journal.pgen.0010082.PubMed CentralPubMedGoogle Scholar
  57. Dwyer JH, Allayee H, Dwyer KM, Fan J, Wu H, Mar R, Lusis AJ, Mehrabian M: Arachidonate 5-lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. New Engl J Med. 2004, 350 (1): 29-37. 10.1056/NEJMoa025079.PubMedGoogle Scholar
  58. Amundadottir LT, Sulem P, Gudmundsson J, Helgason A, Baker A, Agnarsson BA, Sigurdsson A, Benediktsdottir KR, Cazier JB, Sainz J, Jakobsdottir M, Kostic J, Magnusdottir DN, Ghosh S, Agnarsson K, Birgisdottir B, Le Roux L, Olafsdottir A, Blondal T, Andresdottir M, Gretarsdottir OS, Bergthorsson JT, Gudbjartsson D, Gylfason A, Thorleifsson G, Manolescu A, Kristjansson K, Geirsson G, Isaksson H, Douglas J, Johansson JE, Balter K, Wiklund F, Montie JE, Yu X, Suarez BK, Ober C, Cooney KA, Gronberg H, Catalona WJ, Einarsson GV, Barkardottir RB, Gulcher JR, Kong A, Thorsteinsdottir U, Stefansson K: A common variant associated with prostate cancer in European and African populations. Nat Genet. 2006, 38 (6): 652-658. 10.1038/ng1808.PubMedGoogle Scholar
  59. Kim U, Jorgenson E, Coon H, Leppert M, Risch N, Drayna D: Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide. Science. 2003, 299 (5610): 1221-1225. 10.1126/science.1080190.PubMedGoogle Scholar
  60. Wooding S, Kim UK, Bamshad MJ, Larsen J, Jorde LB, Drayna D: Natural selection and molecular evolution in PTC, a bitter-taste receptor gene. Am J Hum Genet. 2004, 74 (4): 637-646. 10.1086/383092.PubMed CentralPubMedGoogle Scholar
  61. Ranade K, Chang MS, Ting CT, Pei D, Hsiao CF, Olivier M, Pesich R, Hebert J, Chen YD, Dzau VJ, Curb D, Olshen R, Risch N, Cox DR, Botstein D: High-throughput genotyping with single nucleotide polymorphisms. Genome Res. 2001, 11 (7): 1262-1268.PubMed CentralPubMedGoogle Scholar
  62. Bamshad M, Kivisild T, Watkins WS, Dixon ME, Ricker CE, Rao BB, Naidu JM, Prasad BVR, Reddy PG, Rasanayagam A, Papiha SS, Villems R, Redd AJ, Hammer MF, Nguyen SV, Carroll ML, Batzer MA, Jorde LB: Genetic Evidence on the Origins of Indian Caste Populations. Genome Res. 2001, 11 (6): 994-1004. 10.1101/gr.GR-1733RR.PubMed CentralPubMedGoogle Scholar
  63. Basu A, Mukherjee N, Roy S, Sengupta S, Banerjee S, Chakraborty M, Dey B, Roy M, Roy B, Bhattacharyya NP, Roychoudhury S, Majumder PP: Ethnic India: A Genomic View, With Special Reference to Peopling and Structure. Genome Res. 2003, 13 (10): 2277-2290. 10.1101/gr.1413403.PubMed CentralPubMedGoogle Scholar
  64. Partha PM: People of India: Biological diversity and affinities. Evolutionary Anthropology: Issues, News, and Reviews. 1998, 6 (3): 100-110. 10.1002/(SICI)1520-6505(1998)6:3<100::AID-EVAN4>3.0.CO;2-I.Google Scholar
  65. Cann RL: Genetic Clues to Dispersal in Human Populations: Retracing the Past from the Present. Science. 2001, 291 (5509): 1742-1748. 10.1126/science.1058948.PubMedGoogle Scholar
  66. Gupta R, Gupta VP, Sarna M, Bhatnagar S, Thanvi J, Sharma V, Singh AK, Gupta JB, Kaul V: Prevalence of coronary heart disease and risk factors in an urban Indian population: Jaipur Heart Watch-2. Indian Heart J. 2002, 54: 59-66.PubMedGoogle Scholar
  67. Bodhini D, Radha V, Dhar M, Narayani N, Mohan V: The rs12255372(G/T) and rs7903146(C/T) polymorphisms of the TCF7L2 gene are associated with type 2 diabetes mellitus in Asian Indians. Metabolism. 2007, 56 (9): 1174-1178. 10.1016/j.metabol.2007.04.012.PubMedGoogle Scholar
  68. Chandak GR, Janipalli CS, Bhaskar S, Kulkarni SR, Mohankrishna P, Hattersley AT, Frayling TM, Yajnik CS: Common variants in the TCF7L2 gene are strongly associated with type 2 diabetes mellitus in the Indian population. Diabetologia. 2007, 50 (1): 63-67. 10.1007/s00125-006-0502-2.PubMedGoogle Scholar
  69. Ray D, Tomar N, Gupta N, Goswami R: Protein tyrosine phosphatase non-receptor type 22 (PTPN22) gene R620W variant and sporadic idiopathic hypoparathyroidism in Asian Indians. Int J Immunogenet. 2006, 33 (4): 237-240. 10.1111/j.1744-313X.2006.00604.x.PubMedGoogle Scholar
  70. Prodi DA, Drayna D, Forabosco P, Palmas MA, Maestrale GB, Piras D, Pirastu M, Angius A: Bitter Taste Study in a Sardinian Genetic Isolate Supports the Association of Phenylthiocarbamide Sensitivity to the TAS2R38 Bitter Receptor Gene. Chem Senses. 2004, 29 (8): 697-702. 10.1093/chemse/bjh074.PubMedGoogle Scholar
  71. Ghoshi AK: ABO blood groups and PTC taste sensitivity among the Kota of Nilgiri Hills. Hum Hered. 1973, 23 (1): 78-82.PubMedGoogle Scholar
  72. Hakim SM, Baxi AJ, Balakrishnan V, Kulkarni KV, Rao SS, Jhala HI: Blood groups, secretor status and ability to taste phenylthiocarbamide in some Muslim groups. Hum Hered. 1973, 23 (1): 72-77.PubMedGoogle Scholar
  73. Kapoor AK, Seth PK: Taste sensitivity to PTC in the Rajputs of Narendra Nagar (Uttar Pradesh, India). Hum Hered. 1981, 31 (2): 124-125.PubMedGoogle Scholar
  74. Mathur VD, Mathur S, Bahadur B: Taste deficiency for phenylthiocarbamide in Mathur Kayasths community of Hyderabad, A.P. Indian J Physiol Pharmacol. 1983, 27 (2): 92-100.PubMedGoogle Scholar
  75. Bhatkar RS, Nallulwar SC, Katti VA: The study of tasters and non-tasters of phenyl-thio-carbamide (PTC) and its relation to blood groups. Indian J Physiol Pharmacol. 1989, 33 (3): 168-170.PubMedGoogle Scholar
  76. Kim UK, Drayna D: Genetics of individual differences in bitter taste perception: lessons from the PTC gene. Clin Genet. 2005, 67 (4): 275-280. 10.1111/j.1399-0004.2004.00361.x.PubMedGoogle Scholar
  77. Wooding S, Bufe B, Grassi C, Howard MT, Stone AC, Vazquez M, Dunn DM, Meyerhof W, Weiss RB, Bamshad MJ: Independent evolution of bitter-taste sensitivity in humans and chimpanzees. Nature. 2006, 440 (7086): 930-934. 10.1038/nature04655.PubMedGoogle Scholar
  78. Tepper BJ: 6-n-Propylthiouracil: a genetic marker for taste, with implications for food preference and dietary habits. Am J Hum Genet. 1998, 63 (5): 1271-1276. 10.1086/302124.PubMed CentralPubMedGoogle Scholar
  79. Billing J, Sherman PW: Antimicrobial functions of spices: why some like it hot. Q Rev Biol. 1998, 73 (1): 3-49. 10.1086/420058.PubMedGoogle Scholar
  80. Kim U, Wooding S, Ricci D, Jorde LB, Drayna D: Worldwide haplotype diversity and coding sequence variation at human bitter taste receptor loci. Human Mutation. 2005, 26 (3): 199-204. 10.1002/humu.20203.PubMedGoogle Scholar
  81. Sturm RA: A golden age of human pigmentation genetics. Trends in Genetics. 2006, 22 (9): 464-468. 10.1016/j.tig.2006.06.010.PubMedGoogle Scholar
  82. Rosenberg NA, Burke T, Elo K, Feldman MW, Freidlin PJ, Groenen MAM, Hillel J, Maki-Tanila A, Tixier-Boichard M, Vignal A, Wimmers K, Weigend S: Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds. Genetics. 2001, 159 (2): 699-713.PubMed CentralPubMedGoogle Scholar
  83. Ramachandran S, Rosenberg NA, Zhivotovsky LA, Feldman MW: Robustness of the inference of human population structure: a comparison of X-chromosomal and autosomal microsatellites. Hum Genomics. 2004, 1 (2): 87-97.PubMed CentralPubMedGoogle Scholar
  84. Rosenberg NA, Calabrese PP: Polyploid and multilocus extensions of the Wahlund inequality. Theo Pop Biol. 2004, 66 (4): 381-391. 10.1016/j.tpb.2004.07.001.Google Scholar
  85. In KH, Asano K, Beier D, Grobholz J, Finn PW, Silverman EK, Silverman ES, Collins T, Fischer AR, Keith TP, Serino K, Kim SW, De Sanctis GT, Yandava C, Pillari A, Rubin P, Kemp J, Israel E, Busse W, Ledford D, Murray JJ, Segal A, Tinkleman D, Drazen JM: Naturally Occurring Mutations in the Human 5-Lipoxygenase Gene Promoter That Modify Transcription Factor Binding and Reporter Gene Transcription. J Clin Invest. 1997, 99 (5): 1130-1137. 10.1172/JCI119241.PubMed CentralPubMedGoogle Scholar
  86. Brownstein MJ, Carpten JD, Smith JR: Modulation of Non-Templated Nucleotide Addition by Taq DNA Polymerase: Primer Modifications that Facilitate Genotyping. BioTechniques. 1996, 20 (6): 1004-1010.PubMedGoogle Scholar
  87. Scheet P, Stephens M: A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase. Am J Hum Genet. 2006, 78 (4): 629-644. 10.1086/502802.PubMed CentralPubMedGoogle Scholar
  88. Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001, 68 (4): 978-989. 10.1086/319501.PubMed CentralPubMedGoogle Scholar
  89. Stephens M, Scheet P: Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation. Am J Hum Genet. 2005, 76 (3): 449-462. 10.1086/428594.PubMed CentralPubMedGoogle Scholar
  90. Stephens M, Donnelly P: A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003, 73 (5): 1162-1169. 10.1086/379378.PubMed CentralPubMedGoogle Scholar
  91. R Development Core Team: R: A language and environment for statistical computing. 2006, Vienna, Austria , R Foundation for Statistical Computing, [http://www.R-project.org]Google Scholar
  92. Warnes G, Leisch F: genetics: Population Genetics. R package version 1.2.0. 2005Google Scholar
  93. Weir BS, Cockerham CC: Estimating F-statitsics for the analysis of population-structure. Evolution. 1984, 38 (6): 1358-1370. 10.2307/2408641.Google Scholar
  94. Akey JM, Zhang G, Zhang K, Jin L, Shriver MD: Interrogating a High-Density SNP Map for Signatures of Natural Selection. Genome Res. 2002, 12 (12): 1805-1814. 10.1101/gr.631202.PubMed CentralPubMedGoogle Scholar
  95. Goudet J: Fstat version 1.2: a computer program to calculate Fstatistics. J Hered. 1995, 86 (6): 485-486.Google Scholar
  96. Weir BS: Genetic Data Analysis II. 1996, Sunderland, MA , Sinauer Associates, 2ndGoogle Scholar
  97. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucl Acids Res. 2001, 29 (1): 308-311. 10.1093/nar/29.1.308.PubMed CentralPubMedGoogle Scholar
  98. Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. (dbSNP Build ID: 126). [http://www.ncbi.nlm.nih.gov/SNP/]
  99. Carter K, McCaskie P, Palmer L: JLIN: A java based linkage disequilibrium plotter. BMC Bioinformatics. 2006, 7 (1): 60-10.1186/1471-2105-7-60.PubMed CentralPubMedGoogle Scholar
  100. Jeunemaitre X, Charru A, Chatellier G, Dumont C, Sassano P, Soubrier F, Menard J, Corvol P: M235T variant of the human angiotensinogen gene in unselected hypertensive patients. J Hypertens Suppl. 1993, 11 (5): S80-S81. 10.1097/00004872-199312050-00019.PubMedGoogle Scholar
  101. Givens RC, Lin YS, Dowling ALS, Thummel KE, Lamba JK, Schuetz EG, Stewart PW, Watkins PB: CYP3A5 genotype predicts renal CYP3A activity and blood pressure in healthy adults. J Appl Physiol. 2003, 95 (3): 1297-1300.PubMedGoogle Scholar
  102. Evans JC, Frayling TM, Cassell PG, Saker PJ, Hitman GA, Walker M, Levy JC, O'Rahilly S, Rao PV, Bennett AJ, Jones EC, Menzel S, Prestwich P, Simecek N, Wishart M, Dhillon R, Fletcher C, Millward A, Demaine A, Wilkin T, Horikawa Y, Cox NJ, Bell GI, Ellard S, McCarthy MI, Hattersley AT: Studies of association between the gene for calpain-10 and type 2 diabetes mellitus in the United Kingdom. Am J Hum Genet. 2001, 69 (3): 544-552. 10.1086/323315.PubMed CentralPubMedGoogle Scholar
  103. Grant SFA, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006, 38 (3): 320-323. 10.1038/ng1732.PubMedGoogle Scholar
  104. Bottini N, Musumeci L, Alonso A, Rahmouni S, Nika K, Rostamkhani M, MacMurray J, Meloni GF, Lucarelli P, Pellecchia M, Eisenbarth GS, Comings D, Mustelin T: A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat Genet. 2004, 36 (4): 337-338. 10.1038/ng1323.PubMedGoogle Scholar
  105. Haines JL, Hauser MA, Schmidt S, Scott WK, Olson LM, Gallins P, Spencer KL, Kwan SY, Noureddine M, Gilbert JR, Schnetz-Boutaud N, Agarwal A, Postel EA, Pericak-Vance MA: Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration. Science. 2005, 308 (5720): 419-421. 10.1126/science.1110359.PubMedGoogle Scholar
  106. Edwards AO, Ritter R, Abel KJ, Manning A, Panhuysen C, Farrer LA: Complement Factor H Polymorphism and Age-Related Macular Degeneration. Science. 2005, 308 (5720): 421-424. 10.1126/science.1110189.PubMedGoogle Scholar
  107. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement Factor H Polymorphism in Age-Related Macular Degeneration. Science. 2005, 308 (5720): 385-389. 10.1126/science.1109557.PubMed CentralPubMedGoogle Scholar
  108. Rivera A, Fisher SA, Fritsche LG, Keilhauer CN, Lichtner P, Meitinger T, Weber BHF: Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum Mol Genet. 2005, 14 (21): 3227-3236. 10.1093/hmg/ddi353.PubMedGoogle Scholar
  109. Emison ES, McCallion AS, Kashuk CS, Bush RT, Grice E, Lin S, Portnoy ME, Cutler DJ, Green ED, Chakravarti A: A common sex-dependent mutation in a RET enhancer underlies Hirschsprung disease risk. Nature. 2005, 434 (7035): 857-863. 10.1038/nature03467.PubMedGoogle Scholar
  110. Lamason RL, Mohideen MAPK, Mest JR, Wong AC, Norton HL, Aros MC, Jurynec MJ, Mao X, Humphreville VR, Humbert JE, Sinha S, Moore JL, Jagadeeswaran P, Zhao W, Ning G, Makalowska I, McKeigue PM, O'Donnell D, Kittles R, Parra EJ, Mangini NJ, Grunwald DJ, Shriver MD, Canfield VA, Cheng KC: SLC24A5, a Putative Cation Exchanger, Affects Pigmentation in Zebrafish and Humans. Science. 2005, 310 (5755): 1782-1786. 10.1126/science.1116238.PubMedGoogle Scholar

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