Skip to main content
  • Review
  • Published:

Bench-to-bedside review: Genetics and proteomics: deciphering gene association studies in critical illness


There is considerable interest in understanding genetic determinants of critical illness to improve current risk stratification models, provide individualized therapies, and improve our current understanding of disease mechanisms. This review provides a broad overview of genetic nomenclature, different study designs, and problems unique to each of these study designs in critical illnesses. Well designed genetic studies with careful attention to these issues during the planning phase, use of rigorous statistical methods during analysis, and replication of these results in different cohorts will lead to more robust results and improved understanding of genetics of critical care.

The completion of the Human Genome draft in 2000 has been accompanied by an explosion of studies examining genetic determinants of disease [1, 2]. In critical care, current prediction models based on socio-demographic and clinical risk factors fail to explain fully why a particular patient either develops or succumbs to disease. Consequently, physicians have tried to understand if genetic variation affects susceptibility and outcome of critical illnesses. Genetics may also provide insights into biological mechanisms and allow more precise use of interventions. Using targeted therapy based on an individual's genetic makeup, rather than using it on all patients, is an appealing strategy. But conflicting results from early studies in genetics of critical illness have led the scientific community to view these results with skepticism [3]. For example, there has been little consensus regarding genetic markers associated with a tumor necrosis factor (TNF) hypersecretor response. In particular, contradictory reports have been published for the association between the -308 guanine to adenine transition within the promoter region of the TNF gene and its expression and severe sepsis susceptibility [4, 5]. This article will provide a broad outline of study designs to ascertain the role of genetic variation in critical care and focus on gene association studies, the most common study design in critical care. The article also addresses both problems generic to genetic studies and those unique to genetics of critical illness.

Mendelian and complex traits

Mendelian traits or diseases, such as sickle cell disease or cystic fibrosis, are influenced by a single gene. In contrast, most critical illnesses are multifactorial diseases, and called 'complex traits' in genetic parlance. Severe sepsis, an example of a complex trait, results from multiple etiologies, such as Gram-positive and Gram-negative bacteria, or fungal infections. The progression to severe sepsis is often mediated by a common biological pathway, with variations unique to specific infectious agents. Therefore, genetic variations within inflammatory mediators involved in the sepsis pathway have been hypothesized to play a role [4, 6, 7]. However, in addition to genetic factors, host characteristics and pathogen load also influence the phenotype. The relative contribution of host genetic factors in complex traits like severe sepsis would be modest.

Focusing only on the contribution of genetic variation to disease, the exact pattern of genetic variation influencing complex traits is still unclear and several theories have been proposed [8]. One model, termed the common disease-rare variant model, suggests that phenotypic variation in complex traits is due to numerous rare genetic variants at multiple loci, with each variant single handedly causing disease. Although the frequency of each rare variant is low, populations may have several such variants. An example of the common disease-rare variant model includes mutations in the BRCA1 and BRCA2 genes, which have been implicated in the susceptibility to breast and ovarian cancer [9]. The frequency of each of the four mutations within these genes is less than 5%, but more than 80% of subjects with these mutations develop breast cancer.

In contrast, the common disease-common variant model suggests that common variants underlie complex traits. Such variants may be maintained through generations due to some form of balancing selection, where the same genetic variant may be protective for certain diseases and harmful in others. This model may be particularly important in critical illnesses, which often occur due to differences in expression of inflammatory mediators. A robust pro-inflammatory response with TNF and IL-6 release may increase the risk of complications, such as severe sepsis or adult respiratory distress syndrome (ARDS), yet that same response may be critical for an adequate host response to infection. Therefore, genetic variants associated with a pro-inflammatory response could be protective and detrimental under different conditions. An example of balancing selection is the guanine to adenine transition at the +250 site within the lymphotoxin alpha gene, which is associated with increased TNF expression and also with higher risk of severe sepsis but lower risk of prolonged mechanical ventilation after coronary artery bypass graft surgery [4, 10]. Complex traits may also occur due to a combination of rare and common variants. Finally, interactions may occur among genes (epistasis) and with environmental factors (gene-environment interactions) to influence the phenotype (Table 1).

Table 1 Nomenclature and explanation of some terms in genetic epidemiology

Nomenclature: polymorphism, mutation, and SNPs

Nucleotides are the building blocks of DNA and contain one of the following four bases: adenine (A), thymine (T), guanine (G), or cytosine (C). A polymorphism is a common variation in the sequence of DNA among individuals (>1% of the population). Substitution of one of the four base pairs by another base pair is called single nucleotide polymorphism or SNP; for example, a SNP may change the DNA sequence from AA TCG to AG TCG. Mutations are also heritable changes in the DNA sequence, but have a frequency of <1%. Polymorphisms occur at a rate higher than can be explained by new mutations, suggesting that they may confer some survival advantage. Variable number of tandem repeats is another type of polymorphism, where a particular repetitive sequence is present in different numbers in different individuals. An example of a tandem repeat is the tetranucleotide (CATT)n repeat within the promoter region of the macrophage inhibitory factor gene, where subjects can have from five to eight repeats [11].

All SNPs are not the same: choosing candidate SNPs

The genes in the human genome account for a very small fraction of the total DNA, and more than 90% of the sequences between genes do not encode any particular product [12]. Variations within DNA are ubiquitous. SNPs occur every 1,000 base pairs in the human genome, and most SNPs do not lead to a change in protein structure or secretion. When SNPs lead to changes in amino acids they are called non-synonymous or missense SNPs. Some of the non-synonymous SNPs in the coding region may affect protein structure and lead to alterations in phenotype. An example is the G to A coding polymorphism at the +1691 site in the factor V gene of the coagulation cascade [13]. This polymorphism leads to the substitution of an arginine by glutamine at amino acid position 506, which is one of the cleavage sites for activated protein C. Factor V inactivation is delayed because the cleavage site is not present, and leads to a hypercoagulable state.

Similar to non-synonymous SNPs, those in the promoter region are also important. Although they do not affect protein structure, they may affect binding of transcription factors and alter expression of the protein in response to an appropriate stimulus. For example, an insertion/deletion polymorphism, termed 4G/5G, is found 675 base pairs upstream of the transcriptional initiation site in the plasminogen activator inhibitor-1 gene [14, 15]. Although both alleles bind a transcriptional activator, the 5G allele reduces transcription by binding a repressor protein, and is associated with lower circulating plasminogen activator inhibitor-1 concentrations [16, 17].

However, most SNPs have no effect on the phenotype because they are either in non-coding regions or they are synonymous SNPs, which are variants that code for the same amino acid. Of the SNPs in the non-coding region, those in the 5' or 3' untranslated region are probably more important than those in introns, which are non-coding sequences of DNA that are initially copied into the RNA but cut out of the final RNA transcript. They may play critical roles in the post-transcriptional regulation of gene expression, including modulation of the transport of mRNAs out of the nucleus and stabilization of protein [18]. It is important to understand these distinctions when choosing SNPs during candidate gene analysis for causal variants. In general, promoter region and non-synonymous SNPs are likely to be more important than those in the non-coding region.

SNPs are not necessarily causal: role of genetic markers, linkage disequilibrium, and haplotype blocks

Knowing the causal SNP may often be difficult. Often, we may discover a SNP 'associated' with a specific phenotype, but it is simply a 'marker' rather than the causal variant. This marker is co-inherited along with the causal variant because it tends to be on the same piece of DNA. This phenomenon where two genetic variants are inherited together through generations is called linkage disequilibrium (LD). Several methods can be used to measure LD. Two most commonly used are Lewontin D' and R2. Both are measures of correlation and expressed on a scale of 0 to 1, with a higher number indicating greater LD or that these SNPs are more likely to be inherited together. These measures of LD are statistical measurements in population genetics and do not necessarily imply distance between the two sites. LD maps for SNPs within a single gene are available publicly and provide important insights into choosing marker SNPs for candidate gene analysis.

LD is a powerful tool in genetics. During meiosis, pieces of maternal and paternal DNA are exchanged via recombination. However, markers in LD remain tightly linked and are transmitted through generations as regions of DNA called haplotype blocks. Once an association is determined between a marker and disease, one could focus on the 'block' of DNA to identify the causal polymorphism. These 'blocks' can be identified, or tagged, by one or more polymorphisms on the block. Once a haplotype of interest has been described, additional work can be done to sequence the haplotype and tease out the specific functional polymorphism within the haplotype that appears to cause the phenotype.

Haplotype and haplotype tag SNPs

Haplotyping is a way of describing blocks of DNA with a pattern of alleles. A potential problem in constructing haplotypes from results of genotype alone is that it is often difficult to determine which set of alleles derives from the paternal chromosome and which set derives from the maternal chromosome. In other words, how are adjacent bases aligned on each chromosome? The specific arrangement of markers on each chromosome within a pair is called haplotype phase. Although phase can be determined by molecular genetic techniques, such methods are expensive. Therefore, statistical software is used to estimate the haplotype frequencies in a population based on genotype data and LD.

Commonly used statistical programs either use iterative likelihood (SAS Genetics, EH Plus) or Bayesian methods (PHASE) to estimate haplotype frequencies in the population. Consider an example of estimation of haplotypes and frequencies of each of these haplotypes in the promoter region of the TNF gene with two SNPs at the -308 and -238 sites (Figure 1). Based on arrangement of these alleles on the maternal and paternal chromosomes, an individual with a GA genotype at both sites could potentially have four different haplotypes, G/G, G/A, A/G, and A/A. Assuming that no LD exists between these sites, the probability of each of these haplotypes is 0.25. However, the estimated probabilities based on LD differ significantly. It is important to emphasize that statistical methods can only estimate probabilities of each haplotype.

Figure 1
figure 1

Estimation of haplotype frequencies for two tumor necrosis factor (TNF) single nucleotide polymorphisms (SNPs) at -308 and -238 promoter sites.

The human chromosome is a mosaic of several such haplotype blocks, which are often 11 to 22 kb in size, but may extend longer [19]. Although multiple polymorphisms (SNPs or variable number of tandem repeats) may be present on each haplotype block, only two or three of them are required to identify a particular haplotype. These SNPs are called haplotype tag SNPs, and are often used as genetic markers in gene association studies. Haplotype tag SNPs are an important tool in mapping genetic determinants of disease and, therefore, there is much interest in developing a haplotype map of the entire human genome [20, 21].

Study design

Two broad approaches are used to assess the roles of genetic variants in disease: linkage analysis and association studies (Figure 2). Linkage analysis follows meiotic events through families for co-segregation of disease and genetic variants. In contrast to chronic illnesses like diabetes, obtaining an accurate family history about critical illnesses in the past, such as whether a family member developed ARDS after pneumonia, is difficult. Therefore, this approach is less useful in acute illnesses, and has not been used widely in the critically ill. In contrast to linkage analysis, association studies detect association between genetic variants and disease across individuals in large populations. Most association studies are population based, but family based studies using parents-affected child trios (transmission disequilibrium test) can also be conducted. This design tests for an association between a specific allele and disease in the child by testing whether heterozygous parents transmit this allele to affected children more frequently than expected [22].

Figure 2
figure 2

Overview of genetic studies.

Gene association studies can be cohort or case-control. Cohort studies are time consuming and expensive to conduct, and are impractical for rare diseases, whereas case-control designs can be affected by selection bias or information bias. However, there are study design problems unique to gene-association studies in critical care. A common practice in case control studies is the use of blood bank donors as a control population. For example, consider a case-control design to study genetic variants influencing susceptibility to pneumonia and severe sepsis. The allele frequency in the control population is often driven by subjects who volunteer to participate in the control group. Little information is available on whether individuals in the control group would or would not develop pneumonia when exposed to an adequate pathogen load in the presence of similar non-genetic risk factors for pneumonia susceptibility.

Even assuming that pneumonia does occur uniformly in the controls and cases, it is not known if severe sepsis would then develop among controls. Severe sepsis and other critical illnesses often occur due to differences in innate immune response. Therefore, while a particular innate immune response like higher TNF production can be protective for pneumonia susceptibility, it might increase risk of severe sepsis. Critical illness occurs in the continuum of a healthy host, who develops infection or trauma, progresses to organ dysfunction or severe sepsis, and death. Taking only the cases at the end of this spectrum, those with established severe sepsis, and comparing them to healthy blood donors could be an entirely spurious process. This association could be confounded by the inciting stimulus that led to severe sepsis.

An inception cohort design is thus a stronger approach. But such studies are time consuming and it is impractical to follow large population based cohorts for long periods, waiting for infections and critical illness to occur. One must, therefore, identify a population at risk, and a single inception cohort may not be able to provide all the answers. One example would be to follow a cohort of elderly subjects for development of pneumonia, while another inception cohort of subjects who present to physicians' offices or emergency rooms with pneumonia could be followed for development of subsequent complications.

Finally, gene-environment interactions are also important to consider in gene association studies. Many interventions in the intensive care unit alter the cytokine cascade, such as strategies to ventilate patients, medications, or surgical techniques. Since differences in expression of proteins involved in the cytokine cascade are hypothesized as candidate genes, interactions between cytokine gene polymorphisms and these interventions would be important.

Candidate gene approach and genome-wide screen

Regardless of the overall study design, one also needs to decide what methodology to use to examine genetic variation. There are two general approaches: genome-wide association studies and candidate gene association studies. Genome-wide association studies are philosophically similar to whole genome linkage analyses, where the investigator does not have an a priori idea of the susceptibility locus, but is trying to locate a chromosomal region that is associated with the 'disease' of interest [23]. This approach is hypothesis-generating, and it is technologically intensive and expensive. However, as the cost of genotyping continues to decrease, this methodology becomes more viable. The exact number of SNPs and type of SNPs (all versus non-synonymous SNPs only) to be used for a genome-wide screen is still a matter of debate.

The candidate gene approach examines the role of genetic variation in one or more genes most likely to be involved in the biological pathway. This approach requires an understanding of the biological mechanisms to identify candidate genes and is commonly used because it is technologically non-intensive and relatively inexpensive. Alternatively, a hybrid approach can be used: a genome-wide screen is used to identify genetic variation spaced throughout the human genome, followed by a candidate gene approach to examine genes within the region of interest.


Accurate definition of phenotype is critical to genetic studies. False positive or false negative results are often due to differences in definitions of phenotypes across studies. Critical illnesses are heterogeneous conditions or syndromes and occur due to a variety of etiologies, each leading to different outcomes. Although clinical definitions of ARDS or severe sepsis are useful diagnostic criteria for clinicians, they may be too expansive for understanding the role of genetic variation. Different sets of genetic markers may underlie susceptibility to ARDS due to infections and trauma [24]. Similarly, genetic variation underlying severe sepsis susceptibility due to different infections may also vary due to interactions between individual organisms and genetic variants.

Statistical issues in gene association studies


Irrespective of study design, it is critical to have sufficient power to detect association. As described previously, the relative risk for critical illness for individual loci would be small, with relative risk ≤ 2. Sample size estimates for gene association studies are determined by the allele frequency and relative risk of the genetic marker of interest. In general, association studies may be more likely to provide statistical evidence of a disease gene with low relative risks than linkage studies [25]. However, approximately 1,000 cases and 1,000 controls will be required to detect modest relative risks of 1.5 [26]. Larger sample sizes would be necessary for rare alleles (frequency <10%), whereas smaller sample sizes would be required if the relative risks are larger. Numerous statistical tools are available to determine sample sizes required for different levels of significance, for example Quanto [22, 27] and Genetic Power Calculator [28, 29].

Multiple testing

There is no easy statistical solution to the problem of multiple-testing. If thousands of tests are performed, then there will be many false-positive results. One of the current approaches is to use a false-discovery rate (FDR) statistic to decide what proportion of true positives to false positives is acceptable to the investigator, choose a level of significance based on this proportion, and follow-up on all results that achieve this level of significance [30]. Thus, the first stage of analyses in which multiple-testing is performed is usually considered to be hypothesis-generating, and results of these analyses will contain some false positives. However, follow-up analyses in another population, that is, replication, should differentiate between true-positive and false-positive results.

Increasingly, the use of permutation tests is being advocated to estimate p-values. P-values for genotype or haplotypes are calculated by random permutations thousands of times. The resulting empirical distribution is used to estimate the p-value for the test-statistic obtained from the actual data. Permutation analyses will account for some of the relatedness among the markers, which are linked if present on the same chromosome. This will remove the dependence of the test-statistic on an underlying distribution. Several statistical packages like R Statistical Computing Environment [31] and SAS Genetics enable the estimation of permutation statistics and FDR.

Replication of genetic studies

The strongest evidence that a particular variant or candidate gene is associated with a trait, and thus may be causal, or in strong LD with a causal variant, is to replicate the result [32]. Replication is defined as doing the analyses in a different population, preferably by different investigators, using different methods to avoid introduction of bias. DeMeo and colleagues [33] recently used linkage analysis to narrow down the candidate genes for chronic obstructive pulmonary disease to chromosome 2q. Using microarray technology on murine and human lung tissue, they identified three genes of interest on chromosome 2q. The associations between these three genes and chronic obstructive pulmonary disease were tested using family based design, and the association with one of the genes, a serine protease inhibitor or SERPINE2, was confirmed in another case-control design using different patient populations from the United States.

Studies have attempted to replicate work in populations of different ethnic origin. For example, a recent report showed that the association between polymorphisms within the selenoprotein S gene with TNF and IL-6 expression in a study of Caucasians was replicated among Mexican families [34]. However, failure to replicate results for a genetic marker in populations of different ethnic origin does not suggest that the results are merely due to type I error. Rather, differences in LD between the genetic marker and the causal variant may lead to different results.

Population admixture

Sub-populations within a population may have a different genetic architecture. Differences in frequency of genetic variants within the population may lead to false positive results. False positive associations between genetic markers and disease can occur due to association of disease with a sub-population, rather than the genetic marker. Self-reported race is used commonly to stratify subjects to avoid ethnic stratification. Population admixture is more common among self-identified African-American subjects compared to those identifying themselves as of Caucasian ethnic origin [35]. Although population admixture does occur in most genetic association studies, the extent to which results would be affected is less clear. Techniques have been developed to detect and correct for population stratification by typing unlinked markers [3638]. Whether this approach is adequate is controversial [39].


Genetic association studies will be more valid if study design issues are carefully considered during the planning phase of a study and rigorous statistical methods are used during analysis. There are several challenges to conducting well designed genetic studies in critical care, including recruiting large cohorts to obtain sufficient power, accurately identifying phenotypes, identifying appropriate case and control groups, and choosing a candidate gene or whole genome approach. However, if such considerations are met, one can be cautiously optimistic that genetic association studies may lead to better understanding of biological mechanisms and improve our ability to target therapy in the critically ill.


This article is part of a thematic series on Translational Research edited by John Kellum.

Other articles in the series can be found online at,


ARDS = :

adult respiratory distress syndrome

IL = :


LD = :

linkage disequilibrium

SNP = :

single nucleotide polymorphism

TNF = :

tumor necrosis factor.


  1. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, et al.: Initial sequencing and analysis of the human genome. Nature 2001, 409: 860-921. 10.1038/35057062

    Article  CAS  PubMed  Google Scholar 

  2. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et al.: The sequence of the human genome. Science 2001, 291: 1304-1351. 10.1126/science.1058040

    Article  CAS  PubMed  Google Scholar 

  3. Weiss KM, Terwilliger JD: How many diseases does it take to map a gene with SNPs? Nat Genet 2000, 26: 151-157. 10.1038/79866

    Article  CAS  PubMed  Google Scholar 

  4. Mira JP, Cariou A, Grall F, Delclaux C, Losser MR, Heshmati F, Cheval C, Monchi M, Teboul JL, Riche F, et al.: Association of TNF2, a TNF-a promoter polymorphism, with septic shock susceptibility and mortality. JAMA 1999, 282: 561-568. 10.1001/jama.282.6.561

    Article  CAS  PubMed  Google Scholar 

  5. Stuber F, Udalova IA, Book M, Drutskaya LN, Kuprash DV, Turet-skaya RL, Schade FU, Nedospasov SA: -308 tumor necrosis factor (TNF) polymorphism is not associated with survival in severe sepsis and is unrelated to lipopolysaccharide inducibility of the human TNF promoter. J Inflamm 1995, 46: 42-50.

    PubMed  Google Scholar 

  6. Gallagher PM, Lowe G, Fitzgerald T, Bella A, Greene CM, McEl-vaney NG, O'Neill SJ: Association of IL-10 polymorphism with severity of illness in community acquired pneumonia. Thorax 2003, 58: 154-156. 10.1136/thorax.58.2.154

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Waterer GW, Quasney MW, Cantor RM, Wunderink RG: Septic shock and respiratory failure in community-acquired pneumonia have different TNF polymorphism associations. Am J Respir Crit Care Med 2001, 163: 1599-1604.

    Article  CAS  PubMed  Google Scholar 

  8. Zwick ME, Cutler DJ, Chakravarti A: Patterns of genetic variation in Mendelian and complex traits. Annu Rev Genomics Hum Genet 2000, 1: 387-407. 10.1146/annurev.genom.1.1.387

    Article  CAS  PubMed  Google Scholar 

  9. King MC, Marks JH, Mandell JB: Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003, 302: 643-646. 10.1126/science.1088759

    Article  CAS  PubMed  Google Scholar 

  10. Yende S, Quasney MW, Tolley E, Zhang Q, Wunderink RG: Association of tumor necrosis factor gene polymorphisms and prolonged mechanical ventilation after coronary artery bypass surgery. Crit Care Med 2003, 31: 133-140. 10.1097/00003246-200301000-00021

    Article  CAS  PubMed  Google Scholar 

  11. Donn RP, Shelley E, Ollier WE, Thomson W: A novel 5'-flanking region polymorphism of macrophage migration inhibitory factor is associated with systemic-onset juvenile idiopathic arthritis. Arthritis Rheum 2001, 44: 1782-1785.;2-# 10.1002/1529-0131(200108)44:8<1782::AID-ART314>3.0.CO;2-#

    Article  CAS  PubMed  Google Scholar 

  12. Stein LD: Human genome End of the beginning. Nature 2004, 431: 915-916. 10.1038/431915a

    Article  CAS  PubMed  Google Scholar 

  13. Bertina RM, Koeleman BPC, Koster T, Rosendaal FR, Dirven RJ, de Ronde H, van der Velden PA, Reitsma PH: Mutation in blood coagulation factor V associated with resistance to activated protein C. Nature 1994, 369: 64-67. 10.1038/369064a0

    Article  CAS  PubMed  Google Scholar 

  14. Dawson SJ, Wiman B, Hamsten A, Green F, Humphries S, Henney AM: The two allele sequences of a common polymorphism in the promoter of the plasminogen activator inhibitor-1 (PAI-1) gene respond differently to interleukin-1 in HepG2 cells. J Biol Chem 1993, 268: 10739-10745.

    CAS  PubMed  Google Scholar 

  15. Eriksson P, Kallin B, 't Hooft FM, Bavenholm P, Hamsten A: Allele-specific increase in basal transcription of the plasmino-gen-activator inhibitor 1 gene is associated with myocardial infarction. Proc Natl Acad Sci USA 1995, 92: 1851-1855. 10.1073/pnas.92.6.1851

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Westendorp RG, Hottenga JJ, Slagboom PE: Variation in plas-minogen-activator-inhibitor-1 gene and risk of meningococcal septic shock. Lancet 1999, 354: 561-563. 10.1016/S0140-6736(98)09376-3

    Article  CAS  PubMed  Google Scholar 

  17. Hermans PW, Hibberd ML, Booy R, Daramola O, Hazelzet JA, de GrootR, Levin M: 4G/5G promoter polymorphism in the plas-minogen-activator-inhibitor-1 gene and outcome of meningo-coccal disease. Meningococcal Research Group. Lancet 1999, 354: 556-560. 10.1016/S0140-6736(99)02220-5

    Article  CAS  PubMed  Google Scholar 

  18. Mignone F, Gissi C, Liuni S, Pesole G: Untranslated regions of mRNAs. Genome Biol 2002, 3: reviews0004. 10.1186/gb-2002-3-3-reviews0004

    Article  PubMed Central  PubMed  Google Scholar 

  19. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumen-stiel B, Higgins J, DeFelice M, Lochner A, Faggart M, et al.: The structure of haplotype blocks in the human genome. Science 2002, 296: 2225-2229. 10.1126/science.1069424

    Article  CAS  PubMed  Google Scholar 

  20. The International HapMap Consortium: A haplotype map of the human genome. Nature 2005, 437: 1299-1320. 10.1038/nature04226

    Article  PubMed Central  Google Scholar 

  21. International HapMap Project[]

  22. Gauderman WJ: Candidate gene association analysis for a quantitative trait, using parent-offspring trios. Genet Epidemiol 2003, 25: 327-338. 10.1002/gepi.10262

    Article  PubMed  Google Scholar 

  23. Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005, 6: 95-108. 10.1038/nrg1521

    Article  CAS  PubMed  Google Scholar 

  24. Lin Z, Pearson C, Chinchilli V, Pietschmann SM, Luo J, Pison U, Floros J: Polymorphisms of human SP-A, SP-B, and SP-D genes: association of SP-B Thr131Ile with ARDS. Clin Genet 2000, 58: 181-191. 10.1034/j.1399-0004.2000.580305.x

    Article  CAS  PubMed  Google Scholar 

  25. Risch NJ: Searching for genetic determinants in the new millennium. Nature 2000, 405: 847-856. 10.1038/35015718

    Article  CAS  PubMed  Google Scholar 

  26. Reich D, Patterson N, Jager PLD, McDonald GJ, Waliszewska A, Tandon A, Lincoln RR, DeLoa C, Fruhan SA, Cabre P, et al.: A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nat Genet 2005, 37: 1113-1118. 10.1038/ng1646

    Article  CAS  PubMed  Google Scholar 

  27. Quanto[]

  28. Purcell S, Cherny SS, Sham PC: Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 2003, 19: 149-150. 10.1093/bioinformatics/19.1.149

    Article  CAS  PubMed  Google Scholar 

  29. Genetic Power Calculator[]

  30. Hochberg Y, Benjamini Y: More powerful procedures for multiple significance testing. Stat Med 1990, 9: 811-818.

    Article  CAS  PubMed  Google Scholar 

  31. R Statistical Computing Environment[]

  32. de Bakker PIW, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D: Efficiency and power in genetic association studies. Nat Genet 2005, 37: 1217-1223. 10.1038/ng1669

    Article  CAS  PubMed  Google Scholar 

  33. DeMeo DL, Mariani TJ, Lange C, Srisuma S, Litonjua AA, Celedon JC, Lake SL, Reilly JJ, Chapman HA, Mecham BH, et al.: The SERPINE2 gene is associated with chronic obstructive pulmonary disease. Am J Hum Genet 2006, 78: 253-264. 10.1086/499828

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Curran JE, Jowett JBM, Elliott KS, Gao Y, Gluschenko K, Wang J, Azim DMA, Cai G, Mahaney MC, Comuzzie AG, et al.: Genetic variation in selenoprotein S influences inflammatory response. Nat Genet 2005, 37: 1234-1241. 10.1038/ng1655

    Article  CAS  PubMed  Google Scholar 

  35. Sinha M, Larkin EK, Elston RC, Redline S: Self-reported race and genetic admixture. New Engl J Med 2006, 354: 421-422. 10.1056/NEJMc052515

    Article  CAS  PubMed  Google Scholar 

  36. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P: Association mapping in structured populations. Am J Hum Genet 2000, 67: 170-181. 10.1086/302959

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Ardlie KG, Lunetta KL, Seielstad M: Testing for population subdivision and association in four case-control studies. Am J Hum Genet 2002, 71: 304-311. 10.1086/341719

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Freedman ML, Reich D, Penney KL, McDonald GJ, Mignault AA, Patterson N, Gabriel SB, Topol EJ, Smoller JW, Pato CN, et al.: Assessing the impact of population stratification on genetic association studies. Nat Genet 2004, 36: 388-393. 10.1038/ng1333

    Article  CAS  PubMed  Google Scholar 

  39. Cardon LR, Palmer LJ: Population stratification and spurious allelic association. Lancet 2003, 361: 598-604. 10.1016/S0140-6736(03)12520-2

    Article  PubMed  Google Scholar 

Download references


Drs Yende and Angus are supported by NIGMS R01 GM61992.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sachin Yende.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yende, S., Kammerer, C.M. & Angus, D.C. Bench-to-bedside review: Genetics and proteomics: deciphering gene association studies in critical illness. Crit Care 10, 227 (2006).

Download citation

  • Published:

  • DOI: