Differences in mtDNA haplogroup distribution among 3 Jewish populations alter susceptibility to T2DM complications
© Feder et al; licensee BioMed Central Ltd. 2008
Received: 24 September 2007
Accepted: 29 April 2008
Published: 29 April 2008
Recent genome-wide association studies searching for candidate susceptibility loci for common complex diseases such as type 2 diabetes mellitus (T2DM) and its common complications have uncovered novel disease-associated genes. Nevertheless these large-scale population screens often overlook the tremendous variation in the mitochondrial genome (mtDNA) and its involvement in complex disorders.
We have analyzed the mitochondrial DNA (mtDNA) genetic variability in Ashkenazi (Ash), Sephardic (Seph) and North African (NAF) Jewish populations (total n = 1179). Our analysis showed significant differences (p < 0.001) in the distribution of mtDNA genetic backgrounds (haplogroups) among the studied populations. To test whether these differences alter the pattern of disease susceptibility, we have screened our three Jewish populations for an association of mtDNA genetic haplogroups with T2DM complications. Our results identified population-specific susceptibility factors of which the best example is the Ashkenazi Jewish specific haplogroup N1b1, having an apparent protective effect against T2DM complications in Ash (p = 0.006), being absent in the NAF population and under-represented in the Seph population. We have generated and analyzed whole mtDNA sequences from the disease associated haplogroups revealing mutations in highly conserved positions that are good candidates to explain the phenotypic effect of these genetic backgrounds.
Our findings support the possibility that recent bottleneck events leading to over-representation of minor mtDNA alleles in specific genetic isolates, could result in population-specific susceptibility loci to complex disorders.
The quest for susceptibility genes of common complex disorders such as type 2 diabetes mellitus (T2DM) has led to recent successful discoveries of novel disease-related genes through the use of large scale genome-wide association studies including thousands of patients belonging to major ethnic groups . Disease-associated loci often fail to replicate in different populations, because of patterns of population-specific susceptibility . This may occur due to genetic drift and founder effects, turning minor alleles in a certain populations to prevalent ones in another population. One may hypothesize that some of these alleles carry functional effects underlying differences in disease susceptibility between populations. Revealing such an effect requires mining special populations, such as the Jews, that due to bottleneck events have increased incidence of alleles that are less abundant in the general population.
The Jewish people underwent several recent bottleneck events after the 2600 year old Babylonian and 2000 year old Roman deportation from Israel [3, 4]. These resulted in geographically separated Jewish communities that kept their customs and religion over centuries, mostly marrying within the communities with little or no intermarriage with local non-Jews, suggesting several founder events. Thus, Jews represent an excellent model to study possible association of population-specific alleles with common disorders, including T2DM .
T2DM is the most common metabolic disease today, with increasing incidence in the Western world (1). Growing evidence for dysfunction of the mitochondrial energy production machinery (OXPHOS) in many T2DM patients  highlights the role of altered OXPHOS activity in the molecular basis leading to the common forms of T2DM: (a) approximately 1% of diabetic patients have large mitochondrial DNA (mtDNA) deletions or the A3243G point mutation [7, 8]; (b) expression of OXPHOS-related genes is decreased in muscle tissues of diabetic individuals [9, 10]; (c) mitochondrial ATP production is decreased and intra-myocellular fat content is increased in offspring of T2DM patients ; (d) in pancreatic beta-cells of mice, cellular depletion of mtDNA and knock-out of mitochondrial transcription factor A (TFAM) interfere with insulin secretion [12, 13].
Given that T2DM is a common complex disorder with considerable heritability, it is probably influenced by a combination of predisposing common genetic variants, potentially including mtDNA variants. Although mtDNA genetic variants have previously been associated with complex disorders in some populations , its extensive genetic variability  and uniparental inheritance may result in diverse association among specific populations . Indeed, mtDNA genetic association with T2DM exemplifies the differences among populations: significant association of certain mtDNA genetic backgrounds (haplogroups) was found in Asians  but not in Caucasians as documented in a recent large scale analysis . Additionally, association of mtDNA variants with T2DM was limited to specific populations [19–21]. The only example of a mtDNA variant (T16189C) associated with T2DM in both Caucasian and Chinese populations [22, 23] was recently questioned .
Similar to T2DM, diabetic complications are complex phenotypes determined by multiple pathways with a large genetic component. Diabetic complications increase markedly in incidence after 5–10 years of active T2DM, but with extreme variability in onset and progression, i.e. some individuals developing severe complications relatively early in the disease course, while others fail to develop any significant complications despite many years of severe disease. Being responsible for most T2DM-associated mortality, diabetic complications involve pathology in small and large vessels (micro- and macrovascular disease), encompassing malfunction of the mitochondrial OXPHOS . Thus, mtDNA variants could be logical candidates to alter the genetic risk to the major diabetic complications- nephropathy, retinopathy and cardiovascular disease .
To search for possible population-specific association between mtDNA common genetic variants and the common complications of T2DM we examined mtDNA genetic variability in three Jewish populations: Ashkenazi, Sephardic and North African Jews.
Haplogroup distribution of Ashkenazi (Ash) T2DM patients who developed complications and those who did not develop any complications for at least 10 years after diagnosis ("No complications"). Patients included in this table may have more than one complication. N – number of patients in each group; (%) – percentage of patients belonging to a certain haplogroup in a specific group.
All Ash N (%)
No complications N (%)
Cardiovascular N (%)
Retino-pathy N (%)
Nephro-pathy N (%)
Haplogroup distribution of European non-Ashkenazi Jewish (Seph) T2DM patients who developed complications and the "No complications" group as in table 1. Patients included in this table may have more than one complication.
All Seph n (%)
No complication n (%)
Cardiovascular n (%)
Retino-pathy n (%)
Nephro-pathy n (%)
Haplogroup distribution of North African Jewish (NAF) T2DM patients who developed complications and the "No complications" group as in table 1. Patients included in this table may have more than one complication.
All NAF n (%)
No complication n (%)
Cardiovascular n (%)
Retino-pathy n (%)
Nephro-pathy n (%)
In an attempt to identify candidate haplogroups for association with T2DM complications a permutation analysis was performed (see Additional file 1 and Additional file 1 – Table 1). In the Ash population haplogroup J1 was detected as a plausible candidate for association with retinopathy and nephropathy (p = 0.035 and p = 0.022, respectively) and haplogroup N1b1 for association with nephropathy (p = 0.003) (Table 1). In the Seph population haplogroup aggregate HV* and haplogroup T were detected as borderline candidates for association with retinopathy (p = 0.054) and nephropathy (p = 0.059), respectively (Table 2). In the NAF population haplogroup aggregate HV* was detected as a candidate for association with nephropathy and cardiovascular disease (p = 0.024 and p = 0.014, respectively) (Table 3).
These results suggest that the differences in haplogroup distribution may result in different disease-associated mtDNA factors in each population. To rigorously investigate the involvement of mtDNA haplogroups in the tendency to develop T2DM complications we focused only on the significant candidate haplogroups (J1 and N1b in the Ash population, and HV* in the NAF population).
mtDNA Haplogroups J1 and N1b associate with T2DM Complications in Ashkenazi Jews
Using a logistic regression model and appropriate Bonferonni correction we compared the candidate haplogroups with each of the other haplogroups while controlling for the possible effects of patient characteristics (disease duration, sex and age). A possible association of a population specific mtDNA haplogroup with T2DM complications could be best tested in our Ashkenazi population (Ash), since N1b1 is an apparently Ashkenazi-specific haplogroup. Our analyses revealed that haplogroup N1b was significantly under-represented in the nephropathy group and in the cardiovascular group as compared with the no-complication group relative to all other haplogroups (p = 0.006, odds ratio (OR) = 0.34 (0.15–0.74), and p = 0.017, OR = 0.39 (0.18–0.84), respectively; also see Additional file 1 – Table 2). In the retinopathy group however, no significant association with N1b was found. These results imply that Ashkenazi T2DM patients pertaining to haplogroup N1b exhibit reduced susceptibility to the tested T2DM complications.
In contrast to haplogroup N1b, haplogroup J1 was over-represented in the Ash population only in the microvascular complications (retinopathy and nephropathy). A significant and specific effect of haplogroup J1 could be masked by including patients who exhibit more than one complication in each of the tested groups. This premise is supported by the view that the risks of developing each of the different complications are not entirely independent. Retinopathy and nephropathy may have some common pathophysiological features, and nephropathy per se may increase the risk of cardiovascular disease. Hence reciprocal interactions among the complications could mask the effect of certain genetic backgrounds. To test for the possibility that patients pertaining to mitochondrial haplogroup J1 exhibit preferentially altered susceptibility to either microvascular or macrovascular complications, we performed the analysis on groups of T2DM patients having nephropathy or retinopathy but no evidence of cardiovascular disease and on T2DM patients with cardiovascular disease who did not develop either nephropathy or retinopathy (for detailed information of haplogroup J1 compared to each of the other haplogroups, see Additional file 1 – Table 3). This demonstrated a significant over-representation of haplogroup J1 in the "isolated" nephropathy group [p = 0.018, OR = 2.3 (1.15–4.7)] and in the "isolated" retinopathy group [p = 0.017, OR = 3.1 (1.2–7.8)]. However, no such trends could be detected in the "isolated" cardiovascular group. These results suggest that mutations defining mtDNA haplogroup J1 increase the propensity of Ashkenazi T2DM patients to develop nephropathy or retinopathy but not cardiovascular complications.
mtDNA Haplogroups Associate with T2DM Complications in Non-Ashkenazi Jews
General clinical data for the patient populations.
Number of patients (% of total population)
Age (mean ± SD)
65.7 ± 9.9
64.3 ± 10
65.9 ± 9.7
64.6 ± 9.2
68.0 ± 9.2
65 ± 9
64.3 ± 8.4
65.1 ± 8.8
64.6 ± 8.2
66.8 ± 8.6
61.9 ± 9.4
60.7 ± 10.0
62.4 ± 8.9
61.6 ± 10.3
63.1 ± 8.8
Years of diabetes (mean ± SD)
18.9 ± 8.3
16.8 ± 7.3
19.2 ± 8.4
22.9 ± 8.5
20.9 ± 9.0
19.4 ± 8.4
21.4 ± 8.8
20.5 ± 9.2
24 ± 9.1
21.2 ± 8.2
19 ± 7.3
16.8 ± 6.4
19.4 ± 7.3
22.4 ± 7.9
19.7 ± 7.9
BMI1 (kg/m2, mean ± SD)
29.9 ± 5.2
28.8 ± 4.6
30.6 ± 5.5
30.8 ± 6.0
29.7 ± 5.0
30.3 ± 4.9
30.7 ± 5.4
30.4 ± 5.3
30.5 ± 5.7
30.5 ± 5
29.6 ± 5.1
28.8 ± 4.8
30.6 ± 5.1
30.3 ± 5.3
30.9 ± 4.5
HbA1c (mean ± SD)
7.9 ± 1.5
7.8 ± 1.4
8.0 ± 1.5
8.2 ± 1.6
7.9 ± 1.4
8.2 ± 1.6
8.2 ± 1.7
8.2 ± 1.4
8.2 ± 1.6
8.3 ± 1.6
8.7 ± 1.9
8.7 ± 2.0
8.8 ± 2.0
8.6 ± 1.6
8.7 ± 1.9
LDL-c (mg/dl, mean ± SD)
104.0 ± 31.0
108.7 ± 31.1
102.3 ± 29.7
103.3 ± 27.9
98.2 ± 30.5
109.3 ± 36.7
106 ± 36
107.1 ± 39
110.9 ± 38
96.1 ± 29.4
104.7 ± 29.8
104.9 ± 28.3
106.3 ± 27.8
105.6 ± 36.6
102.5 ± 31
Taken together these observations suggest that different mtDNA haplogroups may play a role in the propensity of Jewish T2DM patients to develop complications in the studied populations and that this propensity may be population specific.
Evaluating the Functional Significance of Mutations Defining Haplogroups N1b and J1
To test for the potential of the N1b-haplogroup-defining changes to alter function, we studied their degree of evolutionary conservation by investigating the alignment of mtDNA gene sequences from 42 different vertebrates and invertebrates (see Methods and Additional file 1 – Tables 5 and 6). The degree of conservation was ranked high only if it fell within one standard deviation range from the mean conservation degree of mtDNA disease-causing mutations  (Figure 2, Additional file 1 – Table 5). Strikingly, only the N1b1 node (Ashkenazi Jewish) holds highly conserved changes in addition to three highly conserved changes in the N1b stem thus supporting their potential involvement in the protective effect of haplogroup N1b1.
MtDNA genes, in contrast to nuclear DNA (nDNA)-encoded genes, are in full linkage disequilibrium. The mutation rate of the mtDNA is ~10 times faster than that of the nDNA and thus it is the most variable coding region in the human genome. Since mtDNA is maternally inherited, it is prone to genetic drift, resulting in large differences in patterns of genetic variability among and within populations . Such genetic drift often leads to difficulties in replicating results of mtDNA association studies among populations. Hence, we hypothesized that, due to its high genetic divergence among populations, a subset of mtDNA alleles with functional consequences will differentiate among distinct populations. Here we have shown that recent bottleneck events within the three studied Jewish populations (Ash, Seph and NAF), underlie marked differences in mtDNA diversity in three ethnically-related Jewish populations, resulting in increased frequency of genotypes in some populations, some of which may act as susceptibility factors to T2DM complications. Such was the case in the haplogroup N1b1 that was significantly under-represented in certain complications of the Ashkenazi population and not present in the NAF population.
In contrast to haplogroup N1b1, the haplogroups identified as factors with risk trends to some T2DM complications (haplogroup J1 and haplogroup aggregate HV*, with marginal significant values considering a Bonferonni corrected α<0.017) were present in all three studied populations. Nevertheless haplogroup J1, showing association with increased risk for T2DM nephropathy or retinopathy in the Ashkenazi population does not have enough power to replicate in the Seph and NAF populations, i.e. ~250 subjects in each of the complications groups to replicate the significant results of the Ash population (power of 80%, α<0.05 (two tailed)) (Figure 1). The significant over-representation of haplogroup aggregate HV* in certain complications of the NAF population is harder to interpret, since although there was enough power to detect its effect in the Ashkenazi population, i.e. ~70 subjects in each of the complication groups in order to detect significance with a power of 80% and α<0.05 (two tailed), it did not show the same tendency as in the NAF population. Nevertheless one should take into account that HV* is a haplogroup aggregate; hence the different bottleneck events leading to the establishment of the Ashkenazi and North African Jewish populations could result in different compositions of lineages comprising the HV* haplogroup aggregate in the two populations. Testing for this possibility needs further genotyping of HV*, requiring increased sample sizes of the studied populations.
Our findings support association of mtDNA common genetic variants with sub-phenotypes of T2DM. Interestingly, the inconsistency of mtDNA genetic association with complications of T2DM as found here was described for other phenotypes as well: While haplogroup J has been associated with successful longevity in northern Italians and the Finnish , it was not associated with successful longevity in southern Italians  and subhaplogroup J1, but not J2, was associated with successful longevity in the Northern Irish . Therefore, both differences in mtDNA sub-groups and their differences in response to environment appear to affect the relationship between mtDNA genotypes and phenotypes. Since all of the functional SNPs in particular mtDNA lineages would have a collective effect on mitochondrial function, many mtDNA haplogroups and sub-haplogroups might interact with environmental variation differently. Furthermore, this difference can be further complicated by the interaction of mtDNA encoded subunits, harboring functional SNPs, with nuclear DNA encoded subunits, harboring their own genetic variation. This interpretation applies to our observation that N1b1 reduces the risk to T2DM complications only in Ashkenazi Jews. In addition, the tendency to develop complications is an interplay of environment and genetics, hence it is not solely dependent on a particular haplogroup, and thus the absence of the N1b1 haplogroup in the NAF cohort is not expected to change significantly the overall risk do develop complications.
Since our study observed disease-association with a population specific haplogroup, the Ashkenazi specific N1b1, it was of importance to assess the functional potential of this haplogroup defining mutations. During our sequence analysis of haplogroup N1b we noticed that the Ash-specific sub-haplogroup N1b1 harbors an amino acid substitution in mtDNA position 4917 (Figure 2), which also defines haplogroup T that was previously associated with reduced sperm motility . Interestingly, our permutation test suggests with borderline significance that haplogroup T might be in association (p = 0.059) with some T2DM complications in the Seph population. Previously [30, 36] we showed that this mutation alters a highly conserved amino acid in the ND2 gene, hence suggesting a functional potential. Since haplogroups N1b1 and T stem from very different branches in the human mtDNA phylogeny it can be concluded that the 4917 mutation was established at least twice during human evolution. All these evidence imply that the change at position 4917 contributes to the protective effect against certain T2DM complications.
Along with haplogroup J1 association with other multi-factorial phenotypes [30, 37], and its effect on the penetrance of mutations causing the eye disorder LHON  our results support the premise that mutations defining this haplogroup affect OXPHOS. Similar to haplogroup N1b1, some mutations defining haplogroup J1 alter amino-acids with high conservation degree: (1) a transition in position 10398 which is a Thr114Ala replacement in the ND3 subunit of complex I, shown to alter mitochondrial matrix pH in cell-culture experiments ; and (2) a transition in position 13708, causing a Ala458Thr replacement in the ND5 complex I subunit. In addition, haplogroup J1 harbors the G3010A substitution located within the 12SrRNA gene. Although this mutation has been found in several haplogroups , it is possible that the combination of this mutation with the mutations that generally define haplogroup J underlies the phenotypic effect of J1 in the Ash population.
Interestingly, the non-synonymous changes in haplogroups J1 and N1b1 altered highly conserved amino-acid positions in OXPHOS complex I, implying that they possibly affect the activity of this complex . Accordingly, anti-diabetic agents (metformin and thiazolidinediones) act specifically through the inhibition of complex I activity , which suggests a role for complex I functional alteration in the etiology of T2DM.
In summary, our results revealed notable differences in mtDNA genetic diversity within Jews. Our association study of mtDNA genetic variants with T2DM complications showed, that the differences in haplogroup distribution in the three studied populations were associated with differences in disease susceptibility factors. These findings supported our working hypothesis that minor alleles overlooked in large scale association studies may reveal their functional potential in genetic isolates.
The Israeli Diabetes Research Group (IDRG) collected Jewish unrelated T2DM patients of Ashkenazi (Ash) origin (n = 762), of European non-Ashkenazi (Seph) origin (n = 191) and North African Jews (NAF) from seven medical centers in Israel. The Ashkenazi Jews belong to a relatively young population that has gone through a recent bottleneck and thus has less genetic heterogeneity than the general Caucasian population . The Seph and NAF Jewish populations are as young as the Ashkenazi population, yet may have gone through different bottleneck events thus the three populations were analyzed separately. The countries of origin of the patients included in this study can be viewed in Additional file 1-methods. To avoid population stratification effects on the genetic variability, samples in the compared groups were matched for the maternal country of origin.
The basic clinical characteristics of the patients are shown in Table 4. Patients with at least 10 years of known diabetes were selected to assure a sufficiently high prevalence of diabetic complications to provide adequate statistical power in populations of this size. Using information from the patients' medical records and from structured interviews; we initially classified the patients into two groups, those who did not develop any complications after at least 10 years of clinical disease, and those who developed at least one complication. The latter group was further divided into three groups according to the complication diagnosed: retinopathy – patients with proliferating diabetic retinopathy, macular edema and/or blindness; cardiovascular disease – patients with a history of percutaneous transluminal coronary angioplasty, coronary artery bypass graft, myocardial infarction, and congestive heart failure; and nephropathy – patients with microalbuminuria (>30 but < 300 mg protein per gram of creatinine) or proteinuria (>300 mg protein per gram of creatinine), with or without decreased renal function.
DNA was extracted from peripheral lymphocytes by standard techniques (Puregene, Gentra Systems, Minneapolis, MN). Written informed consent was obtained from all individuals who participated in this study, which was approved by the Hadassah Medical Organization's Institutional Review Board for Human Studies.
Classification of haplogroups
Genotyping was conducted by a hierarchical approach, starting from the most prevalent haplogroups in this population . For detailed information see Additional file 1 – methods and Additional file 1 – Tables 7,8.
To avoid small sample sizes, some of the haplogroups were grouped following phylogenetic considerations (for details, see Additional file 1- methods). Statistical analyses were performed using Systat 11.0 (Systat Software, Inc., CA, USA). We first used R × C (rows × columns) test of independence to compare haplogroup distribution among the three different Jewish populations. Next we used a permutation test to detect candidate haplogroups with altered representation in the complication groups in each of the three Jewish populations. Permutation tests were performed using a MATLAB (v.6.5) script: Complications (a binary indicator variable, 0 – no complication and 1 – complication present) were randomly assigned without replacement to patients with different mtDNA genetic backgrounds (i.e., haplogroups). The proportion of patients in each haplogroup who developed a specific complication was calculated. Next the absolute difference (two-tailed test) between each of these values and general tendency to develop such a complication in the entire population (i.e., the proportion of patients in the population who developed such a complication irrespective of their genetic background) was recorded. This procedure was repeated 10,000 times. P values were estimated as the proportion cases in which the absolute difference obtained during the simulations was equal to or greater than that of the original data set. Finally, to test whether the susceptibility to develop T2DM complications (represented by a binary indicator variable taking on values 0 and 1) differed among haplogroups, logistic regression was performed to adjust for patient characteristics, i.e., disease duration, sex and age. It is notable that by converting the categorical variable "haplogroup" into a dummy variable, we could compare the candidate haplogroups with each of the other haplogroups using a single test, i.e., to avoid multiple testing. Specifically, since this variable composed of 8–10 classes (depending on the population analyzed), its inclusion in a logistic regression model requires generating 7–9 indicator variables, respectively. The coefficients of these indicator variables indicate whether the propensity to develop complications in each of the respective haplogroups differs from that of the reference (candidate) haplogroup (haplogroup J1 or N1b in the Ash and the HV* lineage in the NAF populations). For simplicity, we have presented in the text results from analyses in which we treated all the haplogroups in the aggregate excluding the reference haplogroup. The complete analyses, in which these candidate haplogroups were compared with each of the other haplogroups, are presented in Additional file 1 – Tables 2-4. To obtain an estimate for the relative risk of carriers of a particular haplogroup to develop T2DM complications, odds ratios (ORs) were calculated. Power analysis was conducted to get an estimate of the sample size required to replicate our results (see discussion). Although it was argued in the past that corrections are not necessary in our case , we have considered the three different complications examined as potential multiple testing and the statistical significance was Bonferonni corrected to α<0.017.
Whole mtDNA sequencing
The mtDNA genome of normal non-T2DM individuals was amplified in 3 overlapping DNA fragments, and was sequenced using the mitochondrial DNA re-sequencing chip, as previously described  ; sequence ambiguities were resolved using specific primers with a conventional ABI 3100 sequencer. For details see Additional file 1 – methods. Changes within the sequences as compared with the Cambridge reference sequence were documented (see Additional file 1 – Table 5).
Sequence alignment, phylogenetic reconstruction and bootstrap analysis were performed with MEGA3 software .
Estimating degrees of conservation of variants in the mtDNA
Alignment of each of the mtDNA encoded proteins and rRNA genes in 40 vertebrates (including man) and two invertebrates (Drosophila and octopus) was performed (see Additional file 1 – Table 5,6). This alignment was used to assess the degree of conservation (CD), i.e., the number of species that shared the same exact amino acid or nucleotide positions in protein-coding or rRNA-coding mtDNA genes, respectively (see Additional file 1 – Table 6). The CD of the naturally occurring variants in the whole mtDNA sequences was compared with the mean CD of 20 mtDNA disease-causing mutations (36 ± 9) as previously described. A position was considered highly conserved if its CD was within one standard deviation from the mean CD of the disease-causing mutations.
We would like to thank all of the members of the Israel Diabetes Research Group (IDRG – Josef Cohen, Ilana Harman-Boehm, Oscar Minuchin, Yair Yerushalmi, Andreas Buchs, Anat Tsur and Clara Norymberg) and their supporting staff for collecting the samples; We also would like to thank Prof Dina Raveh, BGU, for critical reading of this manuscript and Inon Scharf for writing the MATLAB script for the permutation test. This study was funded by grants from the Israel Science Foundation (D.M.), from the chief scientist of the Israel Ministry of Health (B.G. and D.M.), from the Russell Berrie Foundation and D-Cure, Diabetes Care in Israel (B.G.) and from an unrestricted research grant from Novo-Nordisk, Denmark (I.R.). The authors thank Dr. Procaccio, MAMMAG, UCI for providing positive controls for the A3243G mutation screen.
- Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007, 445 (7130): 881-885. 10.1038/nature05616.PubMedView ArticleGoogle Scholar
- Shriner D, Vaughan LK, Padilla MA, Tiwari HK: Problems with genome-wide association studies. Science. 2007, 316 (5833): 1840-1842. 10.1126/science.316.5833.1840c.PubMedView ArticleGoogle Scholar
- Thomas MG, Weale ME, Jones AL, Richards M, Smith A, Redhead N, Torroni A, Scozzari R, Gratrix F, Tarekegn A, Wilson JF, Capelli C, Bradman N, Goldstein DB: Founding mothers of Jewish communities: geographically separated Jewish groups were independently founded by very few female ancestors. Am J Hum Genet. 2002, 70 (6): 1411-1420. 10.1086/340609.PubMedPubMed CentralView ArticleGoogle Scholar
- Behar DM, Metspalu E, Kivisild T, Achilli A, Hadid Y, Tzur S, Pereira L, Amorim A, Quintana-Murci L, Majamaa K, Herrnstadt C, Howell N, Balanovsky O, Kutuev I, Pshenichnov A, Gurwitz D, Bonne-Tamir B, Torroni A, Villems R, Skorecki K: The matrilineal ancestry of Ashkenazi Jewry: portrait of a recent founder event. Am J Hum Genet. 2006, 78 (3): 487-497. 10.1086/500307.PubMedPubMed CentralView ArticleGoogle Scholar
- Stern E, Blau J, Rusecki Y, Rafaelovsky M, Cohen MP: Prevalence of diabetes in Israel. Epidemiologic survey. Diabetes. 1988, 37 (3): 297-302. 10.2337/diabetes.37.3.297.PubMedView ArticleGoogle Scholar
- Maechler P, Wollheim CB: Mitochondrial function in normal and diabetic beta-cells. Nature. 2001, 414 (6865): 807-812. 10.1038/414807a.PubMedView ArticleGoogle Scholar
- Ballinger SW, Shoffner JM, Hedaya EV, Trounce I, Polak MA, Koontz DA, Wallace DC: Maternally transmitted diabetes and deafness associated with a 10.4 kb mitochondrial DNA deletion. Nature Genetics. 1992, 1: 11-15. 10.1038/ng0492-11.PubMedView ArticleGoogle Scholar
- van den Ouweland JM, Bruining GJ, Lindhout D, Wit JM, Veldhuyzen BF, Maassen JA: Mutations in mitochondrial tRNA genes: non-linkage with syndromes of Wolfram and chronic progressive external ophthalmoplegia. Nucleic Acids Research. 1992, 20 (4): 679-682. 10.1093/nar/20.4.679.PubMedPubMed CentralView ArticleGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.PubMedView ArticleGoogle Scholar
- Sreekumar R, Halvatsiotis P, Schimke JC, Nair KS: Gene expression profile in skeletal muscle of type 2 diabetes and the effect of insulin treatment. Diabetes. 2002, 51 (6): 1913-1920. 10.2337/diabetes.51.6.1913.PubMedView ArticleGoogle Scholar
- Petersen KF, Dufour S, Befroy D, Garcia R, Shulman GI: Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes. N Engl J Med. 2004, 350 (7): 664-671. 10.1056/NEJMoa031314.PubMedPubMed CentralView ArticleGoogle Scholar
- Silva JP, Kohler M, Graff C, Oldfors A, Magnuson MA, Berggren PO, Larsson NG: Impaired insulin secretion and beta-cell loss in tissue-specific knockout mice with mitochondrial diabetes. Nature Genetics. 2000, 26 (3): 336-340. 10.1038/81649.PubMedView ArticleGoogle Scholar
- Soejima A, Inoue K, Takai D, Kaneko M, Ishihara H, Oka Y, Hayashi JI: Mitochondrial DNA is required for regulation of glucose-stimulated insulin secretion in a mouse pancreatic beta cell line, MIN6. J Biol Chem. 1996, 271 (42): 26194-26199. 10.1074/jbc.271.42.26194.PubMedView ArticleGoogle Scholar
- Wallace DC: A Mitochondrial Paradigm of Metabolic and Degenerative Diseases, Aging, and Cancer: A Dawn for Evolutionary Medicine. Annu Rev Genet. 2005, 39: 359-407. 10.1146/annurev.genet.39.110304.095751.PubMedPubMed CentralView ArticleGoogle Scholar
- Ingman M, Kaessmann H, Paabo S, Gyllensten U: Mitochondrial genome variation and the origin of modern humans. Nature. 2000, 408 (6813): 708-713. 10.1038/35047064.PubMedView ArticleGoogle Scholar
- Ghezzi D, Marelli C, Achilli A, Goldwurm S, Pezzoli G, Barone P, Pellecchia MT, Stanzione P, Brusa L, Bentivoglio AR, Bonuccelli U, Petrozzi L, Abbruzzese G, Marchese R, Cortelli P, Grimaldi D, Martinelli P, Ferrarese C, Garavaglia B, Sangiorgi S, Carelli V, Torroni A, Albanese A, Zeviani M: Mitochondrial DNA haplogroup K is associated with a lower risk of Parkinson's disease in Italians. Eur J Hum Genet. 2005, 13 (6): 748-752. 10.1038/sj.ejhg.5201425.PubMedView ArticleGoogle Scholar
- Fuku N, Park KS, Yamada Y, Cho YM, Matsuo H, Segawa T, Watanabe S, Kato K, Yokoi K, Nozawa Y, Lee HK, Tanaka M: Mitochondrial Haplogroup N9a Confers Resistance against Type 2 Diabetes in Asians. American Journal of Human Genetics. 2007, 80 (3): 407-415. 10.1086/512202.PubMedPubMed CentralView ArticleGoogle Scholar
- Saxena R, de Bakker PI, Singer K, Mootha V, Burtt N, Hirschhorn JN, Gaudet D, Isomaa B, Daly MJ, Groop L, Ardlie KG, Altshuler D: Comprehensive association testing of common mitochondrial DNA variation in metabolic disease. Am J Hum Genet. 2006, 79 (1): 54-61. 10.1086/504926.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang D, Taniyama M, Suzuki Y, Katagiri T, Ban Y: Association of the mitochondrial DNA 5178A/C polymorphism with maternal inheritance and onset of type 2 diabetes in Japanese patients. Exp Clin Endocrinol Diabetes. 2001, 109 (7): 361-364. 10.1055/s-2001-17407.PubMedView ArticleGoogle Scholar
- Hegele RA, Zinman B, Hanley AJ, Harris S, Connelly PW: A common mtDNA polymorphism associated with variation in plasma triglyceride concentration [letter]. American Journal of Human Genetics. 1997, 60 (6): 1552-1555. 10.1016/S0002-9297(07)64252-9.PubMedPubMed CentralView ArticleGoogle Scholar
- Rai E, Sharma S, Koul A, Bhat AK, Bhanwer AJ, Bamezai RN: Interaction between the UCP2-866G/A, mtDNA 10398G/A and PGC1alpha p.Thr394Thr and p.Gly482Ser polymorphisms in type 2 diabetes susceptibility in North Indian population. Hum Genet. 2007Google Scholar
- Poulton J, Luan J, Macaulay V, Hennings S, Mitchell J, Wareham NJ: Type 2 diabetes is associated with a common mitochondrial variant: evidence from a population-based case-control study. Hum Mol Genet. 2002, 11 (13): 1581-1583. 10.1093/hmg/11.13.1581.PubMedView ArticleGoogle Scholar
- Bhat A, Koul A, Sharma S, Rai E, Bukhari SI, Dhar MK, Bamezai RN: The possible role of 10398A and 16189C mtDNA variants in providing susceptibility to T2DM in two North Indian populations: a replicative study. Hum Genet. 2007, 120 (6): 821-826. 10.1007/s00439-006-0272-4.PubMedView ArticleGoogle Scholar
- Chinnery PF, Elliott HR, Patel S, Lambert C, Keers SM, Durham SE, McCarthy MI, Hitman GA, Hattersley AT, Walker M: Role of the mitochondrial DNA 16184-16193 poly-C tract in type 2 diabetes. Lancet. 2005, 366 (9497): 1650-1651. 10.1016/S0140-6736(05)67492-2.PubMedView ArticleGoogle Scholar
- Brownlee M: Biochemistry and molecular cell biology of diabetic complications. Nature. 2001, 414 (6865): 813-820. 10.1038/414813a.PubMedView ArticleGoogle Scholar
- Mukae S, Aoki S, Itoh S, Sato R, Nishio K, Iwata T, Katagiri T: Mitochondrial 5178A/C Genotype is Associated With Acute Myocardial Infarction. Circ J. 2003, 67 (1): 16-20. 10.1253/circj.67.16.PubMedView ArticleGoogle Scholar
- Sokal RR, Rohlf FJ: Biometry: the principles and practice of statistics in biological research. 1995, W.H. Freeman and Company, New York, 3rdGoogle Scholar
- Picornell A, Gimenez P, Castro JA, Ramon MM: Mitochondrial DNA sequence variation in Jewish populations. Int J Legal Med. 2006, 120 (5): 271-281. 10.1007/s00414-006-0083-0.PubMedView ArticleGoogle Scholar
- Zeviani M, Di Donato S: Mitochondrial disorders. Brain. 2004, 127 (Pt 10): 2153-2172. 10.1093/brain/awh259.PubMedView ArticleGoogle Scholar
- Carelli V, Achilli A, Valentino ML, Rengo C, Semino O, Pala M, Olivieri A, Mattiazzi M, Pallotti F, Carrara F, Zeviani M, Leuzzi V, Carducci C, Valle G, Simionati B, Mendieta L, Salomao S, Belfort R, Sadun AA, Torroni A: Haplogroup effects and recombination of mitochondrial DNA: novel clues from the analysis of Leber hereditary optic neuropathy pedigrees. Am J Hum Genet. 2006, 78 (4): 564-574. 10.1086/501236.PubMedPubMed CentralView ArticleGoogle Scholar
- Ruiz-Pesini E, Mishmar D, Brandon M, Procaccio V, Wallace DC: Effects of Purifying and Adaptive Selection on Regional Variation in Human mtDNA. Science. 2004, 303 (5655): 223-226. 10.1126/science.1088434.PubMedView ArticleGoogle Scholar
- Feder J, Ovadia O, Glaser B, Mishmar D: Ashkenazi Jewish mtDNA haplogroup distribution varies among distinct subpopulations: lessons of population substructure in a closed group. Eur J Hum Genet. 2007, 15 (4): 498-500. 10.1038/sj.ejhg.5201764.PubMedView ArticleGoogle Scholar
- Dato S, Passarino G, Rose G, Altomare K, Bellizzi D, Mari V, Feraco E, Franceschi C, De Benedictis G: Association of the mitochondrial DNA haplogroup J with longevity is population specific. European Journal of Human Genetics. 2004, 12 (12): 1080-1082. 10.1038/sj.ejhg.5201278.PubMedView ArticleGoogle Scholar
- Ross OA, McCormack R, Curran MD, Duguid RA, Barnett YA, Rea IM, Middleton D: Mitochondrial DNA polymorphism: its role in longevity of the Irish population. Experimental Gerontology. 2001, 36 (7): 1161-1178. 10.1016/S0531-5565(01)00094-8.PubMedView ArticleGoogle Scholar
- Ruiz-Pesini E, Lapena AC, Diez-Sanchez C, Perez-Martos A, Montoya J, Alvarez E, Diaz M, Urries A, Montoro L, Lopez-Perez MJ, Enriquez JA: Human mtDNA Haplogroups associated with high or reduced spermatozoa motility. American Journal of Human Genetics. 2000, 67 (3): 682-696. 10.1086/303040.PubMedPubMed CentralView ArticleGoogle Scholar
- Niemi AK, Majamaa K: Mitochondrial DNA and ACTN3 genotypes in Finnish elite endurance and sprint athletes. Eur J Hum Genet. 2005, 13 (8): 965-969. 10.1038/sj.ejhg.5201438.PubMedView ArticleGoogle Scholar
- Hudson G, Keers S, Yu Wai Man P, Griffiths P, Huoponen K, Savontaus ML, Nikoskelainen E, Zeviani M, Carrara F, Horvath R, Karcagi V, Spruijt L, de Coo IF, Smeets HJ, Chinnery PF: Identification of an X-chromosomal locus and haplotype modulating the phenotype of a mitochondrial DNA disorder. Am J Hum Genet. 2005, 77 (6): 1086-1091. 10.1086/498176.PubMedPubMed CentralView ArticleGoogle Scholar
- Kazuno AA, Munakata K, Nagai T, Shimozono S, Tanaka M, Yoneda M, Kato N, Miyawaki A, Kato T: Identification of Mitochondrial DNA Polymorphisms That Alter Mitochondrial Matrix pH and Intracellular Calcium Dynamics. PLoS Genet. 2006, 2 (8): e128-10.1371/journal.pgen.0020128.PubMedPubMed CentralView ArticleGoogle Scholar
- www.mitomap.org: .
- Brunmair B, Staniek K, Gras F, Scharf N, Althaym A, Clara R, Roden M, Gnaiger E, Nohl H, Waldhausl W, Furnsinn C: Thiazolidinediones, like metformin, inhibit respiratory complex I: a common mechanism contributing to their antidiabetic actions?. Diabetes. 2004, 53 (4): 1052-1059. 10.2337/diabetes.53.4.1052.PubMedView ArticleGoogle Scholar
- Behar DM, Hammer MF, Garrigan D, Villems R, Bonne-Tamir B, Richards M, Gurwitz D, Rosengarten D, Kaplan M, Della Pergola S, Quintana-Murci L, Skorecki K: MtDNA evidence for a genetic bottleneck in the early history of the Ashkenazi Jewish population. Eur J Hum Genet. 2004, 12 (5): 355-364. 10.1038/sj.ejhg.5201156.PubMedView ArticleGoogle Scholar
- Maitra A, Cohen Y, Gillespie SE, Mambo E, Fukushima N, Hoque MO, Shah N, Goggins M, Califano J, Sidransky D, Chakravarti A: The Human MitoChip: a high-throughput sequencing microarray for mitochondrial mutation detection. Genome Res. 2004, 14 (5): 812-819. 10.1101/gr.2228504.PubMedPubMed CentralView ArticleGoogle Scholar
- Rothman KJ: No adjustments are needed for multiple comparisons. Epidemiology. 1990, 1 (1): 43-46.PubMedView ArticleGoogle Scholar
- Kumar S, Tamura K, Nei M: MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Brief Bioinform. 2004, 5 (2): 150-163. 10.1093/bib/5.2.150.PubMedView ArticleGoogle Scholar
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