- Research article
- Open Access
Sequence variation in human succinate dehydrogenase genes: evidence for long-term balancing selection on SDHA
© Baysal et al; licensee BioMed Central Ltd. 2007
- Received: 09 September 2006
- Accepted: 21 March 2007
- Published: 21 March 2007
Balancing selection operating for long evolutionary periods at a locus is characterized by the maintenance of distinct alleles because of a heterozygote or rare-allele advantage. The loci under balancing selection are distinguished by their unusually high polymorphism levels. In this report, we provide statistical and comparative genetic evidence suggesting that the SDHA gene is under long-term balancing selection. SDHA encodes the major catalytical subunit (flavoprotein, Fp) of the succinate dehydrogenase enzyme complex (SDH; mitochondrial complex II). The inhibition of Fp by homozygous SDHA mutations or by 3-nitropropionic acid poisoning causes central nervous system pathologies. In contrast, heterozygous mutations in SDHB, SDHC, and SDHD, the other SDH subunit genes, cause hereditary paraganglioma (PGL) tumors, which show constitutive activation of pathways induced by oxygen deprivation (hypoxia).
We sequenced the four SDH subunit genes (10.8 kb) in 24 African American and 24 European American samples. We also sequenced the SDHA gene (2.8 kb) in 18 chimpanzees. Increased nucleotide diversity distinguished the human SDHA gene from its chimpanzee ortholog and from the PGL genes. Sequence analysis uncovered two common SDHA missense variants and refuted the previous suggestions that these variants originate from different genetic loci. Two highly dissimilar SDHA haplotype clusters were present in intermediate frequencies in both racial groups. The SDHA variation pattern showed statistically significant deviations from neutrality by the Tajima, Fu and Li, Hudson-Kreitman-Aguadé, and Depaulis haplotype number tests. Empirically, the elevated values of the nucleotide diversity (% π = 0.231) and the Tajima statistics (D = 1.954) in the SDHA gene were comparable with the most outstanding cases for balancing selection in the African American population.
The SDHA gene has a strong signature of balancing selection. The SDHA variants that have increased in frequency during human evolution might, by influencing the regulation of cellular oxygen homeostasis, confer protection against certain environmental toxins or pathogens that are prevalent in Africa.
- American Sample
- African American Population
- African American Sample
- High Nucleotide Diversity
- DnaSp Software
Succinate dehydrogenase (SDH; mitochondrial complex II) is an essential enzyme complex that has dual roles in the Krebs cycle and the electron transport chain (ETC) in mitochondria . SDH is composed of four subunits encoded by the nuclear genes SDHA, SDHB, SDHC, and SDHD. SDHA at chromosome band 5p15 and SDHB at chromosome band 1p35 encode the two catalytical hydrophilic subunits flavoprotein (Fp; 70 kDa) and iron-sulfur (Ip; 35 kDa), respectively. SDHC at chromosome band 1q23 and SDHD at chromosome 11q23 encode the two membrane-spanning hydrophobic subunits, cybL (15 kDa) and cybS (12 kDa), respectively. The SDHA, SDHB, SDHC, and SDHD gene products are encoded by 15, 8, 6, and 4 exons, which span genomic distances of ~38 kb, 35 kb, 50 kb. and 10 kb, respectively [2, 3].
The identification of the SDHD subunit gene as the hereditary paraganglioma type 1 locus (PGL1) has uncovered unexpected links between SDH and tumor susceptibility, and highlighted the role of mitochondria in cancer . Since then, mutations in SDHB, SDHC, and SDHD subunit genes (PGL genes) have been established as an important cause of sporadic and familial paragangliomas [5–10]. The paraganglia specificity of PGL tumors  and data from global gene-expression analysis , cell biology , animal-model studies , and gene-environment interaction and population genetics  support the hypothesis that constitutive hypoxic stimulation underlies the pathogenesis of PGL.
The role of SDH in disease pathogenesis has been implicated independently through a series of studies on a widely distributed plant and fungal neurotoxin, 3-nitropropionic acid (3-NPA). Acute food poisoning with 3-NPA, which can lead to central nervous system defects with lifelong disability and to mortality in ~10% of the cases, have been associated with consumption of moldy sugarcanes in China . The neurodegeneration induced by 3-NPA poisoning often involves the basal ganglia, hippocampus, spinal tracts, and peripheral nerves, and the symptoms mimic those of Huntington's disease . 3-NPA irreversibly inhibits SDH, owing to the similarity of the chemical structures of 3-NPA to succinate . It has been suggested that 3-NPA may form a covalent adduct with an arginine residue at amino acid position 345 in the active site of the Fp subunit .
Surprisingly, mutations in the major catalytical subunit SDHA have yet to be associated with PGL. Although homozygous mutations in SDHA have been found in Leigh syndrome , a severe neurodegenerative disorder of childhood, and with neuromusculopathies, no genetic link between SDHA and paraganglioma susceptibility has ever been established. Current biochemical knowledge on SDH provides very few clues for the phenotypic dichotomy arising from the germline subunit gene mutations. SDHA and SDHB subunits encode the two physically-interacting catalytical subunits, so it is surprising that their mutations would have such different phenotypic consequences . Recently, after identifying cDNA sequences encoding a missense Fp variant containing the Y629F and V657I polymorphisms, Tomitsuka et al [22, 23] proposed that distinct genetic loci encode two Fp variants, namely type I and type II. They reached this conclusion after observing tissue-specific and cell line-specific differential expression of the cDNA variants and PCR amplification from genomic DNA of processed SDHA gene fragments that lacked introns (i.e, a functional SDHA retrogene). However, the genomic location of the retrogene that was proposed to encode the second SDHA gene could not be determined. A retrogene for SDHA is not present in the human genome, according to the March 2006 assembly in The UCSC database.  Finally, Briere et al  showed the presence of the missense SDHA variants in several different cell types and assumed that these variants originate from two different genes, although they provided no experimental or bioinformatic evidence for the genomic presence of a second SDHA locus. Briere et al  suggested that the presence of two SDHA genes in paraganglia prevents tumorigenesis. If Fp were encoded by two different loci, this would indeed have provided a simple explanation for why SDHA mutations would not be associated with PGL susceptibility.
An alternative approach to gain insights into gene function involves analysis of sequence variation in the population. To date, no study has systematically addressed the variation patterns in the SDH subunit genes in normal subjects from different racial or ethnic groups. To gain further insights into the multiple roles of SDH in disease predisposition and to help to integrate the seemingly disparate phenotypic consequences of SDH subunit defects, we examined sequence variation in the complete coding and partial flanking intronic sequences of the four SDH subunit genes in 24 samples from an African American population and 24 samples from a white population. These analyses uncovered an unexpected degree of nucleotide diversity in the SDHA gene.
Sequence variants in the SDH subunit genes
Summary of variants in SDH subunit genes
No. of coding base pairs sequenced
No. of synonymous variants
No. of non-synonymous variants
No. of non-coding base pairs sequenced
No. of non-coding variants
Nucleotide diversity in SDH subunit genes
Sequence diversity in succinate dehydrogenase subunit genes
Sample size (n)†
Gene diversity ± SD
Nucleotide diversity, % (θ ± SD)
Nucleotide diversity, % (π ± SD)
0.974 ± 0.010
0.141 ± 0.045
0.231 ± 0.118
0.903 ± 0.030
0.107 ± 0.036
0.147 ± 0.077
0.964 ± 0.009
0.126 ± 0.037
0.199 ± 0.101
0.231 ± 0.078
0.039 ± 0.024
0.016 ± 0.019
0.120 ± 0.045
0.034 ± 0.021
0.008 ± 0.013
0.680 ± 0.060
0.086 ± 0.040
0.103 ± 0.067
0.082 ± 0.053
0.012 ± 0.012
0.013 ± 0.017
0.441 ± 0.061
0.075 ± 0.033
0.065 ± 0.046
0.609 ± 0.063
0.055 ± 0.028
0.077 ± 0.052
0.361 ± 0.059
0.048 ± 0.024
0.044 ± 0.034
Comparison of the human and chimpanzee SDHAgenes for sequence diversity
Sequence diversity in the human and chimpanzee SDHA genes
% (θs ± SDc)
% (π ± SD)
0.153 ± 0.054
0.279 ± 0.147
0.122 ± 0.046
0.168 ± 0.093
0.139 ± 0.045
0.238 ± 0.125
0.077 ± 0.033
0.082 ± 0.051
Tests of neutrality
Tests of Neutrality in PGL genes
D test statistic
Fu and Li
D* test statistic
Fu and Li
F* test statistic
Tests of neutrality in SDHA
Sample size (n)*
Tajima D test statistic
Fu and Li D* test statistic
Fu and Li F* test statistic
Coding and non-coding
Maximum-likelihood HKA analysis of silent polymorphisms in SDHA relative to four other neutrally evolving loci
Likelihood-ratio statistic (d.f.)
Selection on SDHA
5.3 × 10-3
Empirical assessment of neutrality in SDH subunit genes
Haplotype structures of the SDH subunit genes
Haplotypes, haplotype-block structures and the tagging SNPs for each block were inferred using the web-based HAP software (see methods). As expected, the haplotypes were more variable in the African American than in the European American samples. The SDHA haplotype variation could be defined by 6 haplotype blocks and 13 tagging SNPs in the African American samples but only by 3 haplotype-blocks and 5 tagging SNPs in the European American samples (Additional file 1). In contrast, haplotype variation in the PGL genes could be defined by single-haplotype blocks. The most common haplotype accounted for ~99% of the haplotypes of the PGL genes in the European American samples (Additional file 4). Similarly, the most common haplotype and its 1-nucleotide neighbors covered ~98%, 79% and 73% of the variation in the SDHB, SDHC, and SDHD genes, respectively, in the African American samples.
Haplotype number test
To test whether the number of predicted SDHA haplotypes in the African American samples is compatible with neutral evolution, we employed the Depaulis and Veuille haplotype number test . In total, 35 variants in 46 African American sequences defined 27 different haplotypes (Figure 3). Using Depaulis and Veuille simulations under assumptions of neutrality showed that when there are 40 variants in 50 sequences, the upper limit of the 95% confidence interval for the expected number of different haplotypes is 24. Thus, the number of SDHA haplotypes is statistically significantly higher than expected under neutrality, and is consistent with an ancient balanced polymorphism in the African American population.
Estimating age of the SDHAhaplogroups
We estimated the age of the two haplogroups by comparing the sequence divergence between them with that between the human and chimpanzee genes, assuming a constant evolutionary rate of nucleotide substitutions. Haplogroups 1 and 2 have eight fixed nucleotide differences, at SNPs 8–12, 17, 21, and 22 (Figure 3), within 5255 bp, whereas human and chimpanzee genes have eight fixed nucleotide differences within 2832 bps. On the basis of these fixed nucleotide substitutions, we estimated haplogroups 1 and 2 to be as old as [(8/5255)/(8/2832)] times the divergence time of human and chimpanzees. Thus, SDHA balanced polymorphisms were estimated to be 2.69–3.23 million years old, assuming a divergence time of 5–6 million years for human and chimpanzees. This is probably a conservative estimate, as the fixed differences between the haplogroups erode in time by recombination and gene conversion.
Our results establish a foundation to understand the selective and demographic forces that have shaped the variation patterns in SDH subunit genes, and have important functional implications. Our findings indicate that the variation pattern in SDHA is characterized by the presence of higher sequence diversity, two common and highly dissimilar haplogroups, and statistical and empirical support for the operation of a balancing selection mechanism. Our data also refute the previous suggestions that the Y629F and V657I variants originate from two distinct genetic loci because these missense variants are encoded by a single, highly polymorphic SDHA gene.
The PGL genes had much lower nucleotide diversity, which was especially evident in SDHB, suggesting that the SDHB gene product might be under functional constraints that preclude the accumulation of variants. If slightly deleterious variants in PGL genes increase the risk of paraganglioma tumor development, such variants would be eliminated before they reach high frequencies in the population. This potential mechanism might apply especially to SDHB because its mutations are associated with malignancy and early-onset pheochromocytomas that could lead to severe hypertensive crises [37, 38]. In contrast, because there is no evidence that heterozygous mutations in SDHA are associated with a pathologic phenotype, negative selection of deleterious SDHA alleles may operate only when they are in the homozygous state, which often leads to a lethal metabolic syndrome in childhood.
A major finding of our study is the unexpectedly high nucleotide diversity in the SDHA gene in the African American samples. It has been suggested that high local recombination rates may increase SNP density . However, this mechanism is unlikely to contribute to SDHA variant density, because a recent high-resolution recombination map indicates a very low recombination rate at the tip of chromosome 5 short arm, where SDHA is located . It is conceivable that the four SDHA pseudogenes, generated by complete or partial gene duplications, may increase the de novo mutation rate in the SDHA gene through illegitimate recombination or gene conversion during meiosis to increase variant density. However, lack of high nucleotide diversity in the chimpanzee SDHA gene does not suggest that the mutation rate in SDHA is inherently high, even though the chimpanzee genome also contains the duplicated SDHA pseudogenes. Rather, our findings suggest that the high nucleotide diversity of the human SDHA gene is a consequence of persistence of two distinct haplogroups for long periods during human evolution, leading to acquisition of a distinct set of polymorphisms by each haplogroup.
The most important finding of our study is the statistical and empirical support for a balancing selection mechanism on SDHA. A classic example of balancing selection is found at the major histocompatibility complex (MHC) loci , where high levels of polymorphisms in the functional MHC genes may confer a selective advantage to the heterozygotes by enabling them to process a wider range of pathogen antigens on T cells. The variation in a few other human genes may also have been shaped by balancing selection. For example, the 5' cis-regulatory region of CCR5, encoding the principal coreceptor for HIV-1 , protocadherin alpha gene cluster promoters  and the bitter-taste receptor gene, PTC , have two major ancient haplotype groups and positive D test statistics, similar to SDHA. However, in contrast to SDHA, these genes did not show significant Tajima D statistics in the African or African American samples. In general, the average Tajima D value is positive in the European American population and negative in the African American population. Positive Tajima D statistics in European Americans are often interpreted to reflect population contraction that occurred during the migration of modern humans out of Africa, whereas negative Tajima D statistics in African Americans may reflect admixture between African and European populations . Thus, evidence of balancing selection on a gene, suggested by statistically significantly positive Tajima D values, is more likely to be confounded by population history in European American samples than in African American samples.
It is conceivable that an environmental factor prevalent in Africa may have contributed to the increased frequency of certain SDHA variants that might have differential roles in the regulation of oxygen homeostasis by the SDH complex. A candidate environmental factor is the neurotoxin 3-NPA and its aliphatic nitrocompounds derivatives. In addition to being a product of certain fungi such as Arthrinium species, 3-NPA and its derivatives are also found in several higher plants. The toxicity of these plants is well established, because their aliphatic nitrocompound contents have been linked to acute and chronic diseases in some domestic animals. Major livestock losses were attributed to plant nitrocompounds in the western United States, Canada and Mexico . Thus, although human toxicity involving moldy sugarcane poisoning have to date been reported only in China, human exposure to 3-NPA and other nitrocompounds might be more common throughout the world than is indicated by the numbe of clinical cases [18, 46]. 3-NPA exposure might be more prevalent in Africa partly because a hot and humid climate promotes the growth of fungi. If certain SDHA variants confer a selective advantage against 3-NPA poisoning by affecting gene expression levels, protein translation efficiency, and/or the binding affinity for 3-NPA, then such variants may provide a survival advantage for their carriers against 3-NPA poisoning. Alternatively, SDH may play a currently unrecognized role against infectious pathogens such as malaria, which are prevalent in Africa. Genetic studies of PGL suggest that inactivation of SDH by subunit mutations inappropriately activates hypoxia-inducible pathways. If the SDHA variants that have increased in frequency during human evolution are hypomorphs or encode Fps that have slight functional deficits, these variants might promote the activation of hypoxia-inducible pathways and help the immune cells to survive better under sustained hypoxic microenvironments of the infected tissues.
Finally, our findings do not support the previous explanations as to why SDHA mutations are not associated with PGL susceptibility because these explanations assume the presence of two SDHA genes in the human genome. Instead, the contrasting patterns of sequence variation between SDHA and the PGL genes suggest the presence of two functionally distinct modules in SDH: one formed by the three closely-associated PGL gene products (PGL module), and the other a loosely-interacting, highly-variable SDHA protein product. This model provides an alternative explanation as to why SDHA mutations do not cause PGL and predicts the following two conditions:
(i) The relative concentration of SDHA protein product is much higher (>two-fold) than the PGL module in the paraganglionic tissues. Thus, even a 50% reduction in SDHA protein levels, as a result of heterozygous mutations, would not compromise the SDH function in paraganglia to initiate tumor formation.
(ii) The physical interaction between the SDHA protein product and the PGL module is loose and kinetically fast during catalysis, thus a mutant SDHA protein product could not irreversibly trap a PGL module to initiate tumor formation.
Our findings demonstrate that the SDHA gene carries a strong signature of balancing selection in the African American population and that PGL and SDHA gene products are subject to distinct selective constraints. Collectively, these data provide new insights into SDH biology and may catalyze further research on the causes and the consequences of the unexpectedly high sequence diversity in the SDHA subunit gene.
DNA was isolated using standard protocols from samples from 24 unrelated African American and 24 unrelated European American women, which are part of an anonymized sample collection in the Department of Human Genetics at The University of Pittsburgh School of Public Health. The samples were collected under research protocols approved by the internal review board review committee. One African American and two European American samples that failed to amplify multiple SDHA exons on repeated attempts were removed from certain analyses, including minor allele frequency calculations, haplotype analysis, and neutrality statistics. We also sequenced the SDHA gene in 18 unrelated common chimpanzees (Pan troglodytes), which are part of the primate DNA collection in the Department of Human Genetics.
PCR and sequencing
PCR amplification for each exon was performed by using oligonucleotide primers that were designed from the flanking intronic or untranslated sequences of the exons. The primer sequences and the amplicon sizes for each SDH subunit gene exon are provided in Additional file 5. The PCR amplification was performed using Taq polymerase under standard conditions. The PCR amplification of SDHA is potentially confounded by the presence of multiple pseudogenes created by genomic duplications. These pseudogenes contain multiple mutations in their coding regions. BLAST analyses of human expressed sequences database in GenBank reveal no evidence for expression of the SDHA pseudogenes (data not shown). The PCR primers for specific amplification of the SDHA gene were designed so that the 3' ends of the primers were placed at nucleotides that showed divergence from the pseudogenes. The human genome March 2006 sequence assembly at UCSC database indicates that SDHA has two complete and one truncated gene duplications within ~3 Mb at chromosome band 3q29 and one truncated duplication ~100 kb centromeric to the functional gene at chromosome band 5p15 . The duplicated SDHA copies have 92.5–98.4% sequence identity with the functional gene within the exons and in the flanking introns. This high degree of sequence identity has erroneously led to the designation of some of the fixed nucleotide differences between the functional SDHA gene and its pseudogenes as real SNPs in the SDHA gene in the dbSNP database. In our experiments, we confirmed the specific amplification of each SDHA exon by analyzing the nucleotide positions of the amplicons where there are fixed differences between the functional and the duplicated gene copies (number of fixed nucleotide differences between SDHA and its duplicated pseudogenes are indicated in Additional file 5). In addition, we confirmed that all SDHA exonic variants, except the rare variants of SNPs 15, 33, and 36, which were observed only once in our whole sample set (i.e. were singletons), are represented by multiple expressed sequence tags (ESTs) in the human EST database at NCBI as determined by BLAST analyses . Taken together, these results confirm that our genomic primers have specifically amplified the exons of the functional SDHA gene while avoiding the duplicated pseudogenes.
The sequenced segments of the genes, including the coding, non-coding and flanking intronic sequences, were conjoined in a single gene-sequence file. This file was then used to enter polymorphism data for each sample using Sequencher™ software (Gene Codes Corporation, Ann Arbor, MI, USA). The sequence files for each sample were used to generate input files for data analyses in population genetic software. Nucleotide diversity, population diversification analyses and departures from Hardy-Weinberg expectations were calculated using Arlequin software (version 2.001) . Tests of neutrality were conducted using DnaSp software (version 4.10) . The phylogenic relationship between the inferred haplotypes was established using Network software (version 4.1) . All software programs were operated on a PC platform. Haplotype analyses and the prediction of tagging SNPs were performed using HAP, a free web-based haplotype analysis software.
We used the BLAT function of UCSC genome browser to determine the genomic locations of and sequence similarities between SDHA genomic duplications . The Ensembl genome browser was used to determine the intron-exon junction, transcription initiation sites, and start/stop codons of the SDH subunit genes . Gene variation data in the SeattleSNP database (August 2006) derived from 24 African American individuals and 23 Europeans  were used to compare with our results.
Two measures of nucleotide diversity were derived using unphased genotypic data: π, which measures the mean number of differences per nucleotide between two randomly chosen sequences and θs, which measures the proportion of segregating sites under the assumption of an infinite site-neutral model. Both measures estimate the mutation rate, θs = 4Neμ, where Ne is the effective population size and μ is the neutral mutation rate per generation.
In a sample of n chromosomes, π = Σi<j πi, j/nc, where πi, j is the number of nucleotide differences between ith and jth DNA sequences and nc = n(n - 1)/2 and
θs = S/a, where
Tests of neutrality
θs is strongly affected by the existence of deleterious alleles, because such alleles are usually present in low frequencies, but θs is not affected by the frequency of mutants. Conversely, π is not significantly affected by the presence of rare deleterious alleles because π incorporates the frequency of mutants. If some of the variants in the sample have selective effects, then the estimates of θs and π will be different. Tajima  used the difference between these two estimates to detect selection among the sequences.
Tajima's D statistic is calculated as D = (π - θs)/[Var(π - θs]1/2
The value of D is expected to be zero for selectively neutral variants in a constant population. A non-zero D value is a sign of departure from the neutral model caused by a relative excess (positive D values) or deficiency (negative D values) of substitutions of various frequencies .
Departures from the neutral model of the allelic distributions can also be tested by Fu and Li's D* and F* test statistics . These tests compare the number of mutations between internal and external branches of a sequence genealogy with their expectations under selective neutrality. D* and F* tests compare the number of nucleotide variants observed only once in a sample with the total number of nucleotide variants and with the mean pairwise difference between the sequences, respectively. We assessed the significance of neutrality test statistics by comparing the observed test values to those obtained by 10000 coalescent simulations using sample size and number of segregating sites as variables and assuming a standard neutral model with no recombination. Coalescent simulations were performed by DnaSp software (version 4.10).
We used the HKA test for excesses of variation in SDHA gene. This test compares whether the level of intra-specific polymorphism parallels the level of nucleotide divergence between two species in a given locus relative to neutrally evolving loci. We used the direct HKA mode in the DNAsp software for locus-by-locus comparison. We also used a software testing maximum likelihood ratio of selection on SDHA in a multilocus framework as described previously . Twice the difference of log likelihoods for two competing models is approximately χ2 distributed, with the degree of freedom (d.f.) equal to the number of selected loci. We seeded 100000 and 200000 cycles of the Markov chain to run two independent tests on a PC. Both chains provided similar results.
Genetic structure of populations
The genetic structure of populations was investigated by the analysis of molecular variance (AMOVA) approach, as implemented in Arlequin software . This approach is based on the analyses of variance of gene frequencies. The proportion of total variation among populations is estimated by FST, Wright's fixation index.
We used HAP, a software employing a highly accurate method for common haplotype prediction from genotype data  to calculate minor allele frequencies of all variants. The haplotype resolution employs a phasing method that uses imperfect phylogeny. This method partitions the SNPs into haplotype blocks, and for each block, it predicts the common haplotypes and each individual's haplotype. We used Network (version 4.1), a phylogenetic network analysis software, to generate an evolutionary tree network that links the predicted haplotypes on the basis of their similarity .
We thank Joan W. Willett-Brozick for technical help and three reviewers for helpful suggestions. This research is supported in part by a National Institute of Health grant CA112364 to BEB.
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