doi: 10.1161/CIRCULATIONAHA.108.809970
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Circulation. 2009;119:459-467.)
© 2009 American Heart Association, Inc.
Basic Science for Clinicians |
Evolutionary Genetics of Coronary Heart Disease
From the Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minn.
Reprint requests to Iftikhar J. Kullo, MD, Division of Cardiovascular Diseases, Mayo Clinic, 200 First St SW, Rochester MN 55905. E-mail kullo.iftikhar{at}mayo.edu
Key Words: evolution • coronary disease • genetics • metabolic syndrome X • selection (genetics)
 |
Introduction |
|---|
Top
Introduction
Natural SelectionNothing in biology makes sense except in the light of evolution.1— —Theodosius Dobzhanksy, 1973
No biological problem is solved until both the proximate and the evolutionary causation has been elucidated. Furthermore, the study of evolutionary causes is as legitimate a part of biology as is the study of the usually physico-chemical proximate causes.2— —Ernst W. Mayr, 1982
Susceptibility to common diseases such as coronary heart disease (CHD) may in part reflect historical or evolutionary legacies,3,4 and interest in studying evolutionary biology to gain novel insights into human health and disease is increasing. The evolutionary history of the human species may provide valuable insights into the origin of common diseases beyond what is possible by investigating only the most immediate or "proximal" causes of disease. The potential role of evolutionary biology in explaining disease causation was highlighted by Williams and Nesse3 and is often referred to as darwinian medicine. Although the relevance of an evolutionary perspective may vary depending on the disease under study, a strong argument could be made for studying the evolutionary genetics of CHD, a leading cause of human morbidity and death.
Within the last decade, several important advances have made it possible to study "modern" diseases from an evolutionary perspective. The Human Genome Project5 provided a reference human genome, and the subsequent International HapMap Project6,7 described genetic variations (mostly single nucleotide polymorphisms [SNPs]) among individuals and the patterns of variation across the genome. Both projects provide the raw material to study natural selection in the human genome, as reviewed in recent reports.8–10 In addition, sequencing of multiple other genomes, including those of primates, provides a framework for generating important insights into the origin and expression of human diseases.4 Such an avenue of investigation may help answer why, compared with their closest relative, the chimpanzee, humans are susceptible to CHD,11–13 why CHD has assumed epidemic proportions,14 and why substantial differences in susceptibility to CHD are present between ethnic groups.15 Genetic factors underlying both "complex" and mendelian diseases may be affected by natural selection16; conversely, regions of the human genome under natural selection17 are likely to harbor functional loci (gene coding and regulatory regions18) that may influence disease susceptibility. Inferences about natural selection may therefore help identify disease susceptibility loci in humans and facilitate disease mapping studies.19
In this review, we discuss the current hypotheses and knowledge about the evolutionary genetics of several risk factors for CHD, including hypertension, dyslipidemia, and the metabolic syndrome. Several candidate genes in pathways of blood pressure, glucose and lipid metabolism, blood coagulation, and inflammation that may be under natural selection are enumerated. An evolutionary perspective might explain why contemporary humans are at high risk for CHD and also helps to better understand variation in disease susceptibility.
|
Natural Selection |
|---|
Natural selection is the inevitable consequence of inherited variation in fitness, which is defined as the relative ability of an organism to survive and transmit its genes to the next generation.20 Under natural selection, individuals with advantageous or "adaptive" traits tend to be more successful in producing offspring, and those with nonadvantageous traits will be selected against. Positive selection can increase the prevalence of adaptive traits with a genetic basis. CHD has not been subject to direct selective pressure because it often manifests in middle age and beyond and is unlikely to have an effect on reproductive success.21 However, evolutionary genetics provides a framework for understanding how susceptibility alleles (see the glossary in the online-only Data Supplement) for CHD may have been affected by natural selection.
Nesse22 proposed several possible explanations why previously protective alleles may be maladaptive in the present environment. Selection will tend to maintain the frequency of genes that increase reproductive success even if these genes have other effects that increase disease susceptibility in older age.23 For example, genetically determined high cytokine response levels may be associated with adverse cardiovascular outcomes in older individuals but may increase reproductive success in young age by conferring resistance to fatal infectious diseases24; a time lag between environmental change and adaptation by natural selection may also increase disease susceptibility (eg, the proatherogenic environment is relatively recent in human history); natural selection may not be able to accomplish other potentially protective changes even given plenty of time; and finally, the process of selection is stochastic, and protective alleles may be lost and disease alleles retained by chance, resulting in increased disease susceptibility.25
|
Methods for Detecting Signals of Natural Selection |
|---|
Natural selection takes places at the levels of transmitted genotypes and occurs on varying time scales. Several approaches have been developed to detect signals of natural selection.26 Broadly, 2 types of selective forces have shaped the evolution of species: purifying selection, which favors conservation of existing phenotypes, and positive selection, which promotes the emergence of new phenotypes. Both purifying selection and positive selection leave distinctive signatures in the form of patterns of genetic variation that can be detected through statistical tests of genotype or sequence data. If a gene has an important role in organismal homeostasis, then changes such as nonsynonymous mutations will be selected against and eliminated both between species and within species and result in an increase in the proportion of low-frequency variants within species. Positive selection typically manifests as rapid divergence of functional sites between species and a reduction in polymorphic variation within species.
In his landmark "neutral" theory of molecular evolution, Kimura27 proposed that most of the DNA sequence substitution observed both within and between species has no effect on the fitness of the individual organism and that most evolutionary change is the result of genetic drift acting on neutral alleles (ie, genes under no selective pressure). The neutral theory is widely used as a "null model" to identify signatures of natural selection in genes under selective pressure by comparison with the background distribution of genetic variation.28 Deviations from this null hypothesis are indicative of selection, and several tests have been designed to detect such deviation.29 Sabeti et al30 have reviewed in detail statistical tests to detect positive natural selection based on distinctive patterns of genetic variation in the genome.
Broadly, these statistical tests can be classified into 4 categories discussed briefly below (more information about these tests can be obtained in several excellent reviews16,17,19,31,32). Comparison of DNA sequence between species, especially for genetic variants that alter protein function, can be used to detect positive selection that occurred several million years ago. More recent selection can be gleaned from human population genetic data as alterations in levels of nucleotide diversity (or the allele frequency spectrum), linkage disequilibrium (LD), and haplotype structure. Most methods applicable to population genetic data make certain assumptions about the demographic history of populations (a constant population size and no population structure),19 whereas methods comparing species are relatively robust to demographic factors.33 To tease out the demographic effects, one needs to carry out tests of neutrality that account for the demographic history of the population (see Demography and Recombination Versus Selection below). Examples of several methods that have been used to detect signatures of natural selection across the human leukocyte antigen locus are provided in Table I of the online-only Data Supplement.
|
Tests Based on Divergence of Species |
|---|
Over a prolonged period, positive selection can increase the fixation rate of beneficial function-altering mutations (eg, substitutions that change amino acids [ie, nonsynonymous substitutions]). A simple way of detecting selection from comparative genomic data is to calculate the ratio between the rate of nonsynonymous substitutions and the rate of synonymous substitutions (ie, dN/dS).34 This ratio provides a means of detecting selective pressure at the protein level: dN/dS=1 for no selection, dN/dS<1 for purifying selection, and dN/dS>1 for positive selection. Two caveats should be noted for dN/dS-based tests. First, purifying or positive selection may selectively influence synonymous or nonsynonymous residues within a single gene based on the functions of individual residues at the protein, RNA, or regulatory level. Second, this method is not easily applied to cis-regulatory regions, many of which are putative and not validated.35 However, several studies have shown that conserved noncoding regions in mammals bear the signature of natural selection.36–38
|
Tests Based on Allele Frequency Spectrum |
|---|
The allele frequency spectrum represents a summary of the allele frequencies of various mutations in a sample. Natural selection affects the distribution of alleles within populations, and several tests (eg, Tajima’s D, Fu and Li’s F, Fu and Li’s D, and Fay and Wu’s H) have been developed to detect selection based on the allele frequency spectrum.16,17,19 Selection on 1 allele changes the frequency of other alleles that are in LD with it (ie, when the frequency of an allele increases in a population, the frequency of the linked variants also increases). This effect, also called hitchhiking or a selective sweep, is the process by which a new advantageous mutation eliminates or reduces variation in linked neutral sites as it increases in frequency in the population (Figure 1).
|
|
Tests Based on Population Differentiation (FST) |
|---|
Natural selection may increase the degree of differentiation among geographically separate populations that are subject to distinct environmental and/or cultural pressures. The signature can arise only when populations are at least partially isolated reproductively (eg, after the major human migrations out of Africa
50 000 to 70 000 years ago). This is best summarized by the statistic FST, which measures the variance in allele frequency among populations relative to total variation in the entire population (Figure 2).39 Local adaptation appears to have featured in recent human evolutionary history,40 and screening the whole genome for local adaptation may be a means of identifying susceptibility genes for diseases for which prevalence varies as a function of ethnicity.41 For example, we calculated FST for 15 559 common SNPs in 416 candidate genes in 6 causal pathways for CHD and found significant population differentiation for 9 genes.42
|
Population differentiation leading to varying frequency of gene variants across populations has made admixture mapping of disease susceptibility genes possible.43 In an admixed group such as blacks in the United States, one can classify the genome into sections that come from their African or European ancestors. Genomic regions in which individuals with a particular complex disease tend to have an unusually high proportion of ancestry from either Europeans or Africans may harbor a disease risk variant that influences disease susceptibility.
|
Tests Based on LD |
|---|
These tests rely on the LD structure of a given locus in the genome. Levels of LD will increase in selected regions if the rise in frequency of the beneficial mutation occurs faster than the recombination rate. Haplotypes containing the beneficial allele are conserved over significantly longer distances than other nearby haplotypes with similar frequencies.44 Thus, positive selection manifests as a haplotype at high frequency with homozygosity that extends over large regions and can be detected with the extended haplotype homozygosity test.44 Figure 3 illustrates extended haplotype homozygosity for the derived allele of the SNP rs2032582 in the ATP-binding cassette, subfamily B, member 1 gene (ABCB1, also known as the multidrug resistance gene) using haplotype bifurcation diagrams.45 This gene is associated with export of chemical substance from within cells to the extracellular space and may have been under selective pressure related to xenobiotic toxin exposure. The derived allele in Europeans and Chinese demonstrated clear long-range LD as a predominance of 1 thick branch.
|
|
Demography and Recombination Versus Selection |
|---|
It should be noted that various demographic scenarios can mimic the effects of natural selection on genetic variation. The challenge for population genetics-based methods is to separate a signature of natural selection from the confounding effects of population demographic history (eg, population bottlenecks, expansions, and subdivision).30 For example, population expansion can mimic the effect of a selective sweep (increased proportion of low-frequency variants), whereas a population bottleneck can mirror the pattern seen under balancing selection (an excess of intermediate-frequency variants). Population isolation can lead to significantly less genetic diversity than humanity as a whole46 and extended haplotypes over several centimorgans.47 Thus, it is necessary to tease out the effect of population demographic history if significant deviations from evolutionary neutrality are observed. To do this, one needs to carry out a test of neutrality that accounts for the demographic history of the population. For example, a locus of interest can be compared with the pattern of genetic diversity across the genome.48 In contrast to demographic factors, the effects of selection will be locus specific. Tests of evolutionary neutrality also need to account for the recombination rate in the region under study because recombination is a determinant of haplotype structure given the negative correlation between recombination rates and extent of LD.49
|
Evolutionary Models of the Genetic Architecture of CHD |
|---|
The genetic architecture of CHD refers to the number of genetic polymorphisms that affect the risk of CHD, the distribution of their allele frequencies and effect sizes, and their genetic mode of action (additive, dominant and/or epistasis, and pleiotropy).50 Any evolutionary change such as mutation, genetic drift, and natural selection may affect genetic architecture. An evolutionary genetics perspective provides a better understanding of the number and frequency of susceptibility alleles for CHD and thereby helps determine optimal strategies to detect such alleles. Theoretical models for the allelic structure of rare mendelian disease and common complex diseases have been described.51,52
Natural selection has contrasting effects on the genetic architecture of rare mendelian diseases and common complex diseases. The causal alleles for many dominant mendelian diseases are generally new mutations (ie, derived alleles) for which selective pressure exists against their propagation (ie, purifying selection). Examples include familial hypercholesterolemia (eg, mutations in the low-density lipoprotein receptor gene [LDLR]), long-QT syndrome (eg, the calcium channel, voltage-dependent, L type, 
1C subunit gene [CACNA1C]), and hypertrophic cardiomyopathy (eg, myosin, heavy chain 7, cardiac muscle, β [MYH7]). However, natural selection may operate in different directions in the heterozygous and homozygous forms of recessive mendelian diseases. Two classic examples, sickle cell disease and cystic fibrosis, illustrate the selective advantage for the heterozygote against malaria and diarrhea illness, respectively, whereas the homozygotes with the disease generally die before they reach reproductive age.
In contrast, the allelic spectrum of common complex diseases includes both common51,53 and rare variants.54 Recent genome-wide association studies55 have discovered many susceptibility variants of modest effect size that are prevalent in the general population, indicating that the variants were either neutral or advantageous in the past. Lifestyle changes such as culture and environment (eg, climate and diet) would have led to specific selective pressures as humans spread out of Africa. Present-day human beings live in a markedly different environment from their ancestors, so the common complex diseases might result from a mismatch between the ancestral alleles and current environments (ie, the ancestral-susceptibility model for complex diseases).56,57 In other words, ancestral alleles reflect adaptations to the lifestyle of ancient human populations but are deleterious in the "modern" environment (Figure 4). The rare variant–common disease hypothesis posits that much of the disease susceptibility is due to many rare variants at different loci, each conferring a moderate increase in risk.54 These alleles are not as deleterious for reproductive fitness as are rare mutations in mendelian disease and therefore are not eliminated by strong purifying selection. Detection of such rare alleles will require sequencing of the genome or candidate genes because conventional genome-wide association studies are not powered to detect such alleles.54
|
|
Candidate Genes for CHD With Evidence for Natural Selection |
|---|
Two major challenges in studying the genetic basis and evolutionary genetics of CHD are its phenotypic complexity and the presence of multiple causal factors. Although considerable overlap exists among various CHD phenotypes, the underlying pathophysiology may be quite different. For example, myocardial infarction often results from rupture of a vulnerable atherosclerotic plaque, and risk factors and their interactions that determine plaque vulnerability may differ from those that influence coronary atherosclerotic burden as measured by the presence and quantity of coronary artery calcium.58 Epidemiological research over the last several decades has led to the discovery of several risk factors for CHD, including elevated lipid levels, diabetes mellitus, smoking, hypertension, inflammation, oxidative stress, and increased coagulability. In this section, we enumerate genes in causal pathways of atherosclerosis, including blood pressure regulation, lipoprotein and glucose metabolism, coagulation, and inflammation, that may be subject to various degrees of selective pressures resulting from climatic and dietary changes and host response to pathogens (the Table59–80). Candidate genes in these pathways may influence susceptibility to cardiovascular disease; we briefly discuss a few examples of such genes. It needs to be emphasized that for the loci identified as being under selection, further work is needed to address potential confounding caused by population history and structure and to establish definitive evidence of selection, mechanism of selection, and functional effects of the allelic variants under selection.
|
Hypertension
Weder81 has summarized hypotheses that have been put forth to explain hypertension in the framework of evolution. Differential susceptibility to hypertension is likely in part due to our history of adaptation to climatic changes.82 For example, the sodium hypothesis83 posits that sodium-conserving mechanisms conferred a survival advantage among our ancestors in the hot and humid climate of Africa but may lead to hypertension in temperate climates. Thus, sodium-conserving genotypes in US blacks may predispose to excessive sodium retention and sodium-sensitive hypertension in their current milieu.84 The ancestral alleles (ie, the sodium-conserving alleles) show strong latitudinal gradients in allele frequency (clines) and are more prevalent in Africans than in populations from northern Europe, in whom signatures of positive selection (eg, high levels of LD and low haplotype diversity) are noted for the derived allele. The genetic patterns observed in candidate genes affecting sodium handling could have implications for the diagnosis and treatment of hypertension in geographically and ethnically defined populations around the world.60
The geographic distribution of the A(-6)G variant in the promoter region of the human angiotensinogen (AGT) gene suggests that the G(-6) variant has been selectively advantageous outside Africa.60 The G(-6) variant, the derived allele, is present at higher frequency in Asians and Europeans than in Africans,60 and evidence exists of a selective sweep in the vicinity of the polymorphism because haplotypes carrying the derived G(-6) allele showed elevated levels of LD in non-African populations. Genetic drift is not a likely explanation because frequencies of other AGT alleles are not similarly affected.60 A population bottleneck followed by population growth can result in relatively high LD. However, the differences in LD are pronounced around the A(-6)G polymorphisms but not in the entire gene.60,85 Other examples of correlation between latitude and "heat-adapted" alleles include variants in CYP3A5,61 GNB3,62 ADRB2,62 and SCNN1A62 (online-only Data Supplement Table II). Among these alleles, the variant 825T in GNB3 may account for a significant portion of worldwide variation in blood pressure.62
Lipid Metabolism
Humans, in whom cholesterol metabolism has diverged in a variety of ways from that of many distant mammals such as rodents and dogs, are susceptible to hypercholesterolemia given the present-day "atherogenic" diet.86 Several selective pressures, including climatic and dietary changes, may have influenced lipoprotein metabolism. Higher serum cholesterol may have been advantageous during the rapid increase in human size during human evolution and for its role in steroid hormone synthesis. For these reasons, the 
4 allele of the apolipoprotein E (APOE) gene may have been advantageous in the past, but it is maladaptive in the setting of nutritional abundance typical of the developed world, leading to increased risk of CHD.87,88 Consequently, the derived 3 allele is adaptive in the modern environment.65,89
Two genes that play a key role in cholesterol homeostasis, LDLR and proprotein convertase subtilisin/kexin type 9 (PCSK9), a newly discovered regulator of LDLR, may have been under selection. LDLR has been shown to be differentially expressed among mammals,47,90 and an anthropoid primate-specific sequence element (a novel sterol regulatory element) that accounts for the strong activity characterizing human and other anthropoid primates has been identified.66 Species-specific regulation of LDLR in primates may have evolved as a result of varying availability of dietary cholesterol in the past.66 A study68 of the evolutionary genetics of PCSK9 revealed that it may have been subject to various forms of selection, both ancient and recent. Thus, phylogenetic analysis of the dN/dS ratio in PCSK9 in hominoids, Old World monkeys, and New World monkeys revealed evidence of ancient purifying selection, although the functional carboxyl-terminal domain appeared to be under positive selection in many branches across the phylogeny. In addition, a signature of recent positive selection was noted in 2 common nonsynonymous variants (rs505151 and rs562556) associated with higher plasma LDL cholesterol levels in the general population.69
Metabolic Syndrome and Diabetes Mellitus
The metabolic syndrome is characterized by clustering of several risk factors, including increased abdominal fat (obesity), atherogenic dyslipidemia, hypertension, and abnormal glucose metabolism.91 All of these risk factors increase the risk of atherosclerosis and CHD.92 In an influential paper published in 1962, Neel93 introduced the "thrifty-gene" hypothesis to explain the predisposition of certain ethnic groups to obesity and diabetes in the framework of evolution. He postulated that certain genetic variants in humans have evolved to maximize metabolic efficiency, lipid storage, and food searching behavior and that, in times of abundance, these variants predispose their carriers to diseases caused by excess nutritional intake. In the case of thrifty genes, the modern environment promotes the development of the metabolic syndrome.94
A recent study suggested that evolutionary pressures resulting from climatic changes may have played an important role in shaping variation in genes in metabolic pathways.82 Calpain-10 (CAPN10) encodes a member of the calpain-like cysteine protease family that is involved in the regulation of blood glucose levels.95 Population genetic analyses of SNP 44 (rs2975760) reveal a significant deficit of variation in the haplotypes carrying the derived allele,72,73 suggesting that it was quickly driven to high frequency by positive selection. The derived allele at SNP 44 is protective against diabetes mellitus, providing support for the ancestral-susceptibility model of common diseases.96,97 Another example of an ancestral allele predisposing to disease is the T allele of the SNP rs7903146 in the gene for transcription factor 7-like 2 (TCF7L2), which is strongly associated with type II diabetes mellitus.74 Positive selection has driven the haplotypes with the derived allele at this locus to near fixation in East Asians.74
Coagulation
Genes in the coagulation pathway may have been under selective pressure given the potential survival advantage from a robust clotting mechanism in the ancestral environment, possibly by enhancing hemostasis in the setting of bleeding from injury (eg, during hunting or after childbirth).35 The factor V (F5) Leiden mutation, which increases the risk of venous thromboembolism, is found at a high frequency (5%) in European Americans. The mutation increases circulating thrombin generation and may predispose to atherosclerotic vascular disease.98 However, no evidence for natural selection has been noted so far in F5, and the relatively high prevalence of the Leiden mutation remains unexplained. Evidence for positive selection has been noted in the regulatory variant of the factor VII gene (F7), and the high-expression variant is common in Singaporean Chinese.75 The high-expression variant increases risk of CHD, and the selective forces responsible for its increased frequency are unknown. Reduced nucleotide diversity at the factor IX gene (F9) suggests recent positive selection or background selection,76 but how such selection might have affected reproductive fitness is not clear.
Inflammation
It is well known that the host response to pathogens has been under selective pressure in the past, exemplified by signatures of selection in the human leukocyte antigen locus (online-only Data Supplement Table I).99 A heightened immune response that developed to fight pathogens may increase the risk of CHD. Recent work has highlighted the role of the inflammatory response in atherosclerosis,58 and plasma levels of cytokines and the corresponding receptors are associated with CHD risk. Several genes in the inflammation pathway (see Table) have been reported to have evidence for positive selection.
|
Future Work |
|---|
Several aspects of evolutionary genetics of CHD merit further investigation. It has yet to be proved whether allelic variants associated with CHD and identified as being under natural selection are associated with functional effects that affect fitness. As a result, mechanistic links between signals of natural selection and CHD have not been fully delineated. How allele frequency shifts caused by natural selection shape physiological and morphological characteristics and thereby CHD risk needs further study. For example, the 5T allele in the promoter region of MMP3 may have increased in frequency in European Americans because of the associated improvement in arterial elasticity and a consequent lowering of the risk of CHD.80 Second, evolutionary properties of genetic variation in regulatory regions need to be investigated because such variants may, in turn, lead to variation in gene expression, providing a substrate for evolution.100 Indeed, many of the genetic differences between humans and primates may be due to changes in the regulation of genes rather than differences in gene function.18 Third, a need exists to develop computational tests that are sensitive to deviation from evolutionary neutrality while being robust to population demographic history. Fourth, the ethical and social implications of inferences about selection, particularly recent natural selection, will have to be addressed as investigation in this area accelerates.
|
Conclusions |
|---|
Genetic variants in pathways underlying complex disease such as inflammation and energy metabolism are a substrate for natural selection. Evolutionary hypotheses and models such as the thrifty-gene hypothesis and the ancestral-allele susceptibility model have been proposed to explain the epidemiology of complex diseases in the context of evolution. Increasing genetic evidence supports these hypotheses, motivating further exploration of the link between natural selection and common diseases. With the availability of comparative genomics and population genetic data, it should be possible to determine which genes, and what proportion of variants, have been affected by natural selection. This will allow further exploration into the molecular nature of adaptation and help predict which variants in humans may be associated with disease. Such investigation could provide novel insights into the genetic epidemiology and pathophysiology of atherosclerotic vascular disease, including CHD, and potentially open new avenues for prevention and treatment.
|
Acknowledgments |
|---|
We would like to thank Pardis Sabeti for helpful comments.
Disclosures
None.
|
Footnotes |
|---|
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/cgi/content/full/119/3/459/DC1.
|
References |
|---|
1. Dobzhansky T. Nothing in biology makes sense except in the light of evolution. Am Biol Teacher. 1973; 35: 125–129.
2. Mayr E. The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Boston, Mass: Belknap Press; 1982.
3. Williams GC, Nesse RM. The dawn of Darwinian medicine. Q Rev Biol. 1991; 66: 1–22.[CrossRef][Medline] [Order article via Infotrieve]
4. Nesse RM, Stearns SC, Omenn GS. Medicine needs evolution. Science. 2006; 311: 1071.
5. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001; 409: 860–921.[CrossRef][Medline] [Order article via Infotrieve]
6. International HapMap Consortium. A haplotype map of the human genome. Nature. 2005; 437: 1299–1320.[CrossRef][Medline] [Order article via Infotrieve]
7. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007; 449: 851–861.[CrossRef][Medline] [Order article via Infotrieve]
8. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R, Schaffner SF, Lander ES, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MM, Tsui SK, Xue H, Wong JT, Galver LM, Fan JB, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, Chagnon F, Ferretti V, Leboeuf M, Olivier JF, Phillips MS, Roumy S, Sallee C, Verner A, Hudson TJ, Kwok PY, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller P, Xiao M, Tsui LC, Mak W, Song YQ, Tam PK, Nakamura Y, Kawaguchi T, Kitamoto T, Morizono T, Nagashima A, Ohnishi Y, Sekine A, Tanaka T, Tsunoda T, Deloukas P, Bird CP, Delgado M, Dermitzakis ET, Gwilliam R, Hunt S, Morrison J, Powell D, Stranger BE, Whittaker P, Bentley DR, Daly MJ, de Bakker PI, Barrett J, Chretien YR, Maller J, McCarroll S, Patterson N, Pe'er I, Price A, Purcell S, Richter DJ, Sabeti P, Saxena R, Schaffner SF, Sham PC, Varilly P, Altshuler D, Stein LD, Krishnan L, Smith AV, Tello-Ruiz MK, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Lin S, Abecasis GR, Guan W, Li Y, Munro HM, Qin ZS, Thomas DJ, McVean G, Auton A, Bottolo L, Cardin N, Eyheramendy S, Freeman C, Marchini J, Myers S, Spencer C, Stephens M, Donnelly P, Cardon LR, Clarke G, Evans DM, Morris AP, Weir BS, Tsunoda T, Johnson TA, Mullikin JC, Sherry ST, Feolo M, Skol A, Zhang H, Zeng C, Zhao H, Matsuda I, Fukushima Y, Macer DR, Suda E, Rotimi CN, Adebamowo CA, Ajayi I, Aniagwu T, Marshall PA, Nkwodimmah C, Royal CD, Leppert MF, Dixon M, Peiffer A, Qiu R, Kent A, Kato K, Niikawa N, Adewole IF, Knoppers BM, Foster MW, Clayton EW, Watkin J, Gibbs RA, Belmont JW, Muzny D, Nazareth L, Sodergren E, Weinstock GM, Wheeler DA, Yakub I, Gabriel SB, Onofrio RC, Richter DJ, Ziaugra L, Birren BW, Daly MJ, Altshuler D, Wilson RK, Fulton LL, Rogers J, Burton J, Carter NP, Clee CM, Griffiths M, Jones MC, McLay K, Plumb RW, Ross MT, Sims SK, Willey DL, Chen Z, Han H, Kang L, Godbout M, Wallenburg JC, L'Archeveque P, Bellemare G, Saeki K, Wang H, An D, Fu H, Li Q, Wang Z, Wang R, Holden AL, Brooks LD, McEwen JE, Guyer MS, Wang VO, Peterson JL, Shi M, Spiegel J, Sung LM, Zacharia LF, Collins FS, Kennedy K, Jamieson R, Stewart J. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007; 449: 913–918.[CrossRef][Medline] [Order article via Infotrieve]
9. Zhang C, Bailey DK, Awad T, Liu G, Xing G, Cao M, Valmeekam V, Retief J, Matsuzaki H, Taub M, Seielstad M, Kennedy GC. A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations. Bioinformatics. 2006; 22: 2122–2128.
10. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. PLoS Biol. 2006; 4: e72.[CrossRef][Medline] [Order article via Infotrieve]
11. Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature. 2005; 437: 69–87.[CrossRef][Medline] [Order article via Infotrieve]
12. Olson MV, Varki A. Sequencing the chimpanzee genome: insights into human evolution and disease. Nat Rev Genet. 2003; 4: 20–28.[CrossRef][Medline] [Order article via Infotrieve]
13. Schmidt RE. Systemic pathology of chimpanzees. J Med Primatol. 1975; 7: 274–318.[Medline] [Order article via Infotrieve]
14. Fuster V, Badimon L, Badimon JJ, Chesebro JH. The pathogenesis of coronary artery disease and the acute coronary syndromes (1). N Engl J Med. 1992; 326: 242–250.[Medline] [Order article via Infotrieve]
15. Ioannidis JP, Ntzani EE, Trikalinos TA. "Racial" differences in genetic effects for complex diseases. Nat Genet. 2004; 36: 1312–1318.[CrossRef][Medline] [Order article via Infotrieve]
16. Nielsen R, Hellmann I, Hubisz M, Bustamante C, Clark AG. Recent and ongoing selection in the human genome. Nat Rev Genet. 2007; 8: 857–868.[Medline] [Order article via Infotrieve]
17. Bamshad M, Wooding SP. Signatures of natural selection in the human genome. Nat Rev Genet. 2003; 4: 99–111.[CrossRef][Medline] [Order article via Infotrieve]
18. King MC, Wilson AC. Evolution at two levels in humans and chimpanzees. Science. 1975; 188: 107–116.
19. Nielsen R. Molecular signatures of natural selection. Annu Rev Genet. 2005; 39: 197–218.[CrossRef][Medline] [Order article via Infotrieve]
20. Jenkins J. Human Genetics. 2nd ed. New York, NY: Harper & Row; 1990.
21. Watkins H, Farrall M. Genetic susceptibility to coronary artery disease: from promise to progress. Nat Rev Genet. 2006; 7: 163–173.[CrossRef][Medline] [Order article via Infotrieve]
22. Nesse RM. Maladaptation and natural selection. Q Rev Biol. 2005; 80: 62–70.[CrossRef][Medline] [Order article via Infotrieve]
23. Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution. 1957; 11: 398–411.[CrossRef]
24. Van Den Biggelaar AH, De Craen AJ, Gussekloo J, Huizinga TW, Heijmans BT, Frolich M, Kirkwood TB, Westendorp RG. Inflammation underlying cardiovascular mortality is a late consequence of evolutionary programming. FASEB J. 2004; 18: 1022–1024.
25. Houle D. Comparing evolvability and variability of quantitative traits. Genetics. 1992; 130: 195–204.[Abstract]
26. McVean G, Spencer CC. Scanning the human genome for signals of selection. Curr Opin Genet Dev. 2006; 16: 624–629.[CrossRef][Medline] [Order article via Infotrieve]
27. Kimura M. Evolutionary rate at the molecular level. Nature. 1968; 217: 624–626.[CrossRef][Medline] [Order article via Infotrieve]
28. Hellmann I, Ebersberger I, Ptak SE, Paabo S, Przeworski M. A neutral explanation for the correlation of diversity with recombination rates in humans. Am J Hum Genet. 2003; 72: 1527–1535.[CrossRef][Medline] [Order article via Infotrieve]
29. Barton NH, Briggs DEG, Eisen JA, Goldstein DB, Patel NH. Evolution. Cold Spring Harbor, NY: Colod Spring Harbor Laboratory Press; 2007.
30. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O, Palma A, Mikkelsen TS, Altshuler D, Lander ES. Positive natural selection in the human lineage. Science. 2006; 312: 1614–1620.
31. Vallender EJ, Lahn BT. Positive selection on the human genome. Hum Mol Genet. 2004; 13 (spec No. 2): R245–R254.
32. Kreitman M. Methods to detect selection in populations with applications to the human. Annu Rev Genomics Hum Genet. 2000; 1: 539–559.[CrossRef][Medline] [Order article via Infotrieve]
33. Yang Z, Bielawski JP. Statistical methods for detecting molecular adaptation. Trends Ecol Evol. 2000; 15: 496–503.[CrossRef][Medline] [Order article via Infotrieve]
34. Yang Z, Nielsen R. Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol Biol Evol. 2002; 19: 908–917.
35. Ding K, Kullo IJ. Molecular evolution of 5' flanking regions of 87 candidate genes for atherosclerotic cardiovascular disease. Genet Epidemiol. 2006; 30: 557–569.[CrossRef][Medline] [Order article via Infotrieve]
36. Dermitzakis ET, Reymond A, Scamuffa N, Ucla C, Kirkness E, Rossier C, Antonarakis SE. Evolutionary discrimination of mammalian conserved non-genic sequences (CNGs). Science. 2003; 302: 1033–1035.
37. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, Weinstock GM, Wilson RK, Gibbs RA, Kent WJ, Miller W, Haussler D. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005; 15: 1034–1050.
38. Keightley PD, Kryukov GV, Sunyaev S, Halligan DL, Gaffney DJ. Evolutionary constraints in conserved nongenic sequences of mammals. Genome Res. 2005; 15: 1373–1378.
39. Weir B, Cockerham C. Estimating F-statistics for the analysis of population structure. Evolution. 1984; 38: 1358–1370.[CrossRef]
40. Biswas S, Akey JM. Genomic insights into positive selection. Trends Genet. 2006; 22: 437–446.[CrossRef][Medline] [Order article via Infotrieve]
41. Halder I, Shriver MD. Measuring and using admixture to study the genetics of complex diseases. Hum Genomics. 2003; 1: 52–62.[Medline] [Order article via Infotrieve]
42. Kullo IJ, Ding K. Patterns of population differentiation of candidate genes for cardiovascular disease. BMC Genet. 2007; 8: 48.[CrossRef][Medline] [Order article via Infotrieve]
43. Stephens JC, Briscoe D, O'Brien SJ. Mapping by admixture linkage disequilibrium in human populations: limits and guidelines. Am J Hum Genet. 1994; 55: 809–824.[Medline] [Order article via Infotrieve]
44. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF, Gabriel SB, Platko JV, Patterson NJ, McDonald GJ, Ackerman HC, Campbell SJ, Altshuler D, Cooper R, Kwiatkowski D, Ward R, Lander ES. Detecting recent positive selection in the human genome from haplotype structure. Nature. 2002; 419: 832–837.[CrossRef][Medline] [Order article via Infotrieve]
45. Wang H, Ding K, Zhang Y, Jin L, Kullo IJ, He F. Comparative and evolutionary pharmacogenetics of ABCB1: complex signatures of positive selection on coding and regulatory regions. Pharmacogenet Genomics. 2007; 17: 667–678.[CrossRef][Medline] [Order article via Infotrieve]
46. Peltonen L. Positional cloning of disease genes: advantages of genetic isolates. Hum Hered. 2000; 50: 66–75.[CrossRef][Medline] [Order article via Infotrieve]
47. Peltonen L, Jalanko A, Varilo T. Molecular genetics of the Finnish disease heritage. Hum Mol Genet. 1999; 8: 1913–1923.
48. 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: 1805–1814.
49. Greenwood TA, Rana BK, Schork NJ. Human haplotype block sizes are negatively correlated with recombination rates. Genome Res. 2004; 14: 1358–1361.
50. Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev. 2008; 18: 257–263.[CrossRef][Medline] [Order article via Infotrieve]
51. Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet. 2001; 17: 502–510.[CrossRef][Medline] [Order article via Infotrieve]
52. Pritchard JK. Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet. 2001; 69: 124–137.[CrossRef][Medline] [Order article via Infotrieve]
53. Lander ES. The new genomics: global views of biology. Science. 1996; 274: 536–539.
54. Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008; 40: 695–701.[CrossRef][Medline] [Order article via Infotrieve]
55. Ding K, Kullo IJ. Genome-wide association studies for atherosclerotic vascular disease and its risk factors. Circ Cardiovasc Genet. In press.
56. Di Rienzo A, Hudson RR. An evolutionary framework for common diseases: the ancestral-susceptibility model. Trends Genet. 2005; 21: 596–601.[CrossRef][Medline] [Order article via Infotrieve]
57. Di Rienzo A. Population genetics models of common diseases. Curr Opin Genet Dev. 2006; 16: 630–636.[CrossRef][Medline] [Order article via Infotrieve]
58. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005; 352: 1685–1695.
59. Akey JM, Eberle MA, Rieder MJ, Carlson CS, Shriver MD, Nickerson DA, Kruglyak L. Population history and natural selection shape patterns of genetic variation in 132 genes. PLoS Biol. 2004; 2: e286.[CrossRef][Medline] [Order article via Infotrieve]
60. Nakajima T, Wooding S, Sakagami T, Emi M, Tokunaga K, Tamiya G, Ishigami T, Umemura S, Munkhbat B, Jin F, Guan-Jun J, Hayasaka I, Ishida T, Saitou N, Pavelka K, Lalouel JM, Jorde LB, Inoue I. Natural selection and population history in the human angiotensinogen gene (AGT): 736 complete AGT sequences in chromosomes from around the world. Am J Hum Genet. 2004; 74: 898–916.[CrossRef][Medline] [Order article via Infotrieve]
61. 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: 1059–1069.[CrossRef][Medline] [Order article via Infotrieve]
62. Young JH, Chang YP, Kim JD, 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 Genet. 2005; 1: e82.[CrossRef][Medline] [Order article via Infotrieve]
63. Lohmueller KE, Wong LJ, Mauney MM, Jiang L, Felder RA, Jose PA, Williams SM. Patterns of genetic variation in the hypertension candidate gene GRK4: ethnic variation and haplotype structure. Ann Hum Genet. 2006; 70: 27–41.[CrossRef][Medline] [Order article via Infotrieve]
64. Wen G, Wessel J, Zhou W, Ehret GB, Rao F, Stridsberg M, Mahata SK, Gent PM, Das M, Cooper RS, Chakravarti A, Zhou H, Schork NJ, O'Connor DT, Hamilton BA. An ancestral variant of Secretogranin II confers regulation by PHOX2 transcription factors and association with hypertension. Hum Mol Genet. 2007; 16: 1752–1764.
65. Fullerton SM, Clark AG, Weiss KM, Nickerson DA, Taylor SL, Stengard JH, Salomaa V, Vartiainen E, Perola M, Boerwinkle E, Sing CF. Apolipoprotein E variation at the sequence haplotype level: implications for the origin and maintenance of a major human polymorphism. Am J Hum Genet. 2000; 67: 881–900.[CrossRef][Medline] [Order article via Infotrieve]
66. Wang QF, Prabhakar S, Wang Q, Moses A, Chanan S, Brown M, Eisen M, Cheng JF, Rubin E, Boffelli D. Primate-specific evolution of an LDLR enhancer. Genome Biol. 2006; 7: R68.[CrossRef][Medline] [Order article via Infotrieve]
67. Fagundes NJ, Salzano FM, Batzer MA, Deininger PL, Bonatto SL. Worldwide genetic variation at the 3'-UTR region of the LDLR gene: possible influence of natural selection. Ann Hum Genet. 2005; 69: 389–400.[CrossRef][Medline] [Order article via Infotrieve]
68. Ding K, McDonough SJ, Kullo IJ. Evidence for positive selection in the C-terminal domain of the cholesterol metabolism gene PCSK9 based on phylogenetic analysis in 14 primate species. PLoS ONE. 2007; 2: e1098.[CrossRef][Medline] [Order article via Infotrieve]
69. Ding K, Kullo IJ. Molecular population genetics of PCSK9: a signature of recent positive selection. Pharmacogenet Genomics. 2007; 18: 169–179.
70. Le Jossec M, Wambach T, Labuda D, Sinnett D, Levy E. Genetic diversity patterns in the SR-BI/II locus can be explained by a recent selective sweep. Mol Biol Evol. 2004; 21: 760–769.
71. Romeo S, Pennacchio LA, Fu Y, Boerwinkle E, Tybjaerg-Hansen A, Hobbs HH, Cohen JC. Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet. 2007; 39: 513–516.[CrossRef][Medline] [Order article via Infotrieve]
72. Fullerton SM, Bartoszewicz A, Ybazeta G, Horikawa Y, Bell GI, Kidd KK, Cox NJ, Hudson RR, Di Rienzo A. Geographic and haplotype structure of candidate type 2 diabetes susceptibility variants at the calpain-10 locus. Am J Hum Genet. 2002; 70: 1096–1106.[CrossRef][Medline] [Order article via Infotrieve]
73. Vander Molen J, Frisse LM, Fullerton SM, Qian Y, Del Bosque-Plata L, Hudson RR, Di Rienzo A. Population genetics of CAPN10 and GPR35: implications for the evolution of type 2 diabetes variants. Am J Hum Genet. 2005; 76: 548–560.[CrossRef][Medline] [Order article via Infotrieve]
74. Helgason A, Palsson S, Thorleifsson G, Grant SF, Emilsson V, Gunnarsdottir S, Adeyemo A, Chen Y, Chen G, Reynisdottir I, Benediktsson R, Hinney A, Hansen T, Andersen G, Borch-Johnsen K, Jorgensen T, Schafer H, Faruque M, Doumatey A, Zhou J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Sigurdsson G, Hebebrand J, Pedersen O, Thorsteinsdottir U, Gulcher JR, Kong A, Rotimi C, Stefansson K. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat Genet. 2007; 39: 218–225.[CrossRef][Medline] [Order article via Infotrieve]
75. Hahn MW, Rockman MV, Soranzo N, Goldstein DB, Wray GA. Population genetic and phylogenetic evidence for positive selection on regulatory mutations at the factor VII locus in humans. Genetics. 2004; 167: 867–877.
76. Harris EE, Hey J. Human populations show reduced DNA sequence variation at the factor IX locus. Curr Biol. 2001; 11: 774–778.[CrossRef][Medline] [Order article via Infotrieve]
77. Rockman MV, Hahn MW, Soranzo N, Goldstein DB, Wray GA. Positive selection on a human-specific transcription factor binding site regulating IL4 expression. Curr Biol. 2003; 13: 2118–2123.[CrossRef][Medline] [Order article via Infotrieve]
78. Wu X, Di Rienzo A, Ober C. A population genetics study of single nucleotide polymorphisms in the interleukin 4 receptor alpha (IL4RA) gene. Genes Immun. 2001; 2: 128–134.[CrossRef][Medline] [Order article via Infotrieve]
79. Zhou G, Zhai Y, Dong X, Zhang X, He F, Zhou K, Zhu Y, Wei H, Yao Z, Zhong S, Shen Y, Qiang B, He F. Haplotype structure and evidence for positive selection at the human IL13 locus. Mol Biol Evol. 2004; 21: 29–35.
80. Rockman MV, Hahn MW, Soranzo N, Loisel DA, Goldstein DB, Wray GA. Positive selection on MMP3 regulation has shaped heart disease risk. Curr Biol. 2004; 14: 1531–1539.[CrossRef][Medline] [Order article via Infotrieve]
81. Weder AB. Evolution and hypertension. Hypertension. 2007; 49: 260–265.
82. Hancock AM, Witonsky DB, Gordon AS, Eshel G, Pritchard JK, Coop G, Di Rienzo A. Adaptations to climate in candidate genes for common metabolic disorders. PLoS Genet. 2008; 4: e32.[CrossRef][Medline] [Order article via Infotrieve]
83. Gleiberman L. Blood pressure and dietary salt in human populations. Ecol Food Nutr. 1973; 1: 143–156.
84. Wilson TW, Grim CE. Biohistory of slavery and blood pressure differences in blacks today: a hypothesis. Hypertension. 1991; 17 (suppl): I-122–I-128.
85. Danziger RS. Hypertension in an anthropological and evolutionary paradigm. Hypertension. 2001; 38: 19–22.
86. Chiang JY. Bile acid regulation of gene expression: roles of nuclear hormone receptors. Endocr Rev. 2002; 23: 443–463.
87. Davignon J, Gregg RE, Sing CF. Apolipoprotein E polymorphism and atherosclerosis. Arteriosclerosis. 1988; 8: 1–21.
88. Stengard JH, Zerba KE, Pekkanen J, Ehnholm C, Nissinen A, Sing CF. Apolipoprotein E polymorphism predicts death from coronary heart disease in a longitudinal study of elderly Finnish men. Circulation. 1995; 91: 265–269.
89. Corbo RM, Scacchi R. Apolipoprotein E (APOE) allele distribution in the world: is APOE*4 a "thrifty" allele? Ann Hum Genet. 1999; 63: 301–310.[CrossRef][Medline] [Order article via Infotrieve]
90. Horton JD, Goldstein JL, Brown MS. SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver. J Clin Invest. 2002; 109: 1125–1131.[CrossRef][Medline] [Order article via Infotrieve]
91. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC Jr, Spertus JA, Costa F. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005; 112: 2735–2752.
92. Dandona P, Aljada A, Chaudhuri A, Mohanty P, Garg R. Metabolic syndrome: a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation. 2005; 111: 1448–1454.
93. Neel JV. Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"? Am J Hum Genet. 1962; 14: 353–362.[Medline] [Order article via Infotrieve]
94. Neel JV, Weder AB, Julius S. Type II diabetes, essential hypertension, and obesity as "syndromes of impaired genetic homeostasis": the "thrifty genotype" hypothesis enters the 21st century. Perspect Biol Med. 1999; 42: 44–74.
95. Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner TH, Mashima H, Schwarz PE, del Bosque-Plata L, Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Concannon P, Iwasaki N, Schulze J, Baier LJ, Bogardus C, Groop L, Boerwinkle E, Hanis CL, Bell GI. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet. 2000; 26: 163–175.[CrossRef][Medline] [Order article via Infotrieve]
96. Weedon MN, Schwarz PE, Horikawa Y, Iwasaki N, Illig T, Holle R, Rathmann W, Selisko T, Schulze J, Owen KR, Evans J, Del Bosque-Plata L, Hitman G, Walker M, Levy JC, Sampson M, Bell GI, McCarthy MI, Hattersley AT, Frayling TM. Meta-analysis and a large association study confirm a role for calpain-10 variation in type 2 diabetes susceptibility. Am J Hum Genet. 2003; 73: 1208–1212.[CrossRef][Medline] [Order article via Infotrieve]
97. Song Y, Niu T, Manson JE, Kwiatkowski DJ, Liu S. Are variants in the CAPN10 gene related to risk of type 2 diabetes? A quantitative assessment of population and family-based association studies. Am J Hum Genet. 2004; 74: 208–222.[CrossRef][Medline] [Order article via Infotrieve]
98. Ye Z, Liu EH, Higgins JP, Keavney BD, Lowe GD, Collins R, Danesh J. Seven haemostatic gene polymorphisms in coronary disease: meta-analysis of 66,155 cases and 91,307 controls. Lancet. 2006; 367: 651–658.[CrossRef][Medline] [Order article via Infotrieve]
99. Meyer D, Thomson G. How selection shapes variation of the human major histocompatibility complex: a review. Ann Hum Genet. 2001; 65: 1–26.[CrossRef][Medline] [Order article via Infotrieve]
100. Lopez-Maury L, Marguerat S, Bahler J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat Rev Genet. 2008; 9: 583–593.[Medline] [Order article via Infotrieve]
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||