Review
Open Access

Functional single nucleotide polymorphism-based association studies

  • Victoria EH Carlton1,
  • James S Ireland1,
  • Francisco Useche1 and
  • Malek Faham1Email author
Human Genomics20062:391

https://doi.org/10.1186/1479-7364-2-6-391

©  Henry Stewart Publications 2006

Received: 23 March 2006

Accepted: 23 March 2006

Published: 1 June 2006

Abstract

Association studies hold great promise for the elucidation of the genetic basis of diseases. Studies based on functional single nucleotide polymorphisms (SNPs) or on linkage disequilibrium (LD) represent two main types of designs. LD-based association studies can be comprehensive for common causative variants, but they perform poorly for rare alleles. Conversely, functional SNP-based studies are efficient because they focus on the SNPs with the highest a priori chance of being associated. Our poor ability to predict the functional effect of SNPs, however, hampers attempts to make these studies comprehensive. Recent progress in comparative genomics, and evidence that functional elements tend to lie in conserved regions, promises to change the landscape, permitting functional SNP association studies to be carried out that comprehensively assess common and rare alleles. SNP genotyping technologies are already sufficient for such studies, but studies will require continued genomic sequencing of multiple species, research on the functional role of conserved sequences and additional SNP discovery and validation efforts (including targeted SNP discovery to identify the rare alleles in functional regions). With these resources, we expect that comprehensive functional SNP association studies will soon be possible.

Keywords

functional SNPs association studies human disease

Introduction

Association studies of common, complexly inherited human diseases have the potential to provide us with insights into causes of enormous human suffering [1]. While thousands of such studies have been published (typically using single nucleotide polymorphisms [SNPs]), only a handful of these finding have been clearly and consistently replicated. While some findings are doubtless real, [2] debate continues over most. There are only a small number of genetic variants that have been clearly and consistently associated with a common disease, many of which are listed in Table 1.
Table 1

Some clear, consistent common disease associations.

Gene

Disease

Presumed

causative

variant

Functional effect

Approximate frequency

(in ethnic population of

first positive study)

Frequency information

For other populationsa

PTPN22

Rheumatoid

arthritis [3–13]

R620W

nsSNP

9-10% (Caucasian)[3–13]

0% in n = 1,600 Japanese;

0% in n = 60 Africans [3, 14]

-

Type 1

diabetes [7, 9, 15–20]

-

-

-

-

CFH

(factorH)

Macular

degeneration [21–26]

Y402H

nsSNP

30 40%

(Caucasians)[21–26]

Unknown

FV

(factor5)

Deep venous

thrombosis [27–34]

R506W

nsSNP

3 7% (Caucasians)[35]

0% in n = 800 from Africa,

South-East Asia, Australasia and the

Americas (Native) [35, 36]

F2

(prothrombin)

Deep venous

thrombosis [34, 37–40]

G20210A

3' utr mRNA

cleavage site [41]

1 3% (Caucasians)[42]

0% in Asians;0% in Africans [36, 40, 42]

CARD15

(NOD2)

Crohn's

disease [43–46]

1007fs

Frame shift causing

truncated protein

~2% (Caucasians)

0% in [Q3] n = 888 Asians; 0% in n = 640 Gambians [47–49]

-

-

R702W

nsSNP

~4% (Caucasians)

< 0.1% in 888 Asians; 0% in 640 Gambians [47–49]

-

-

G908R

nsSNP

~1% (Caucasians)

< 0.1% in n = 888 Asians; 0% in n = 640 Gambians [47–49]

-

-

Several very

rare variants

nsSNPs

< 1% (Caucasians)

Unknown

CHEK2

Breast cancer [50–56]

1100delC

Frame shift causing

truncated protein

0.5-1.5% (Caucasians)[50–56]

Unknown

APOE

Alzheimer's

disease [57–60]

C112R

nsSNP

~15% (Caucasians)[57–61]

25 40% in Africans; 8% in Asians [62–65]

KCNJ11

Type 2 diabetes [66, 67]

E23K

nsSNP

~40% (Caucasians)[68]

Unknown

CCR5

HIV infection [69–73]

Delta32

Frame shift causing truncated protein

8 10% (Caucasians)[69–73]

Absent in Africans and Asians; 2 5% in the Middle East, India, Europe [74]

HLA-

various genes

Many autoimmune

diseases

Varied

Largely

nsSNPs/haplotypes

Varied

Varied; many show striking population frequency differences

Abbreviations: nsSNP = non-synonymous single nucleotide polymorphisms; utr = untranslated regions.

a With the exception of HLA and APOE, none of the presumed causative variants have been shown to be present above 1 per cent in multiple major ethnic populations. While the 112R allele of APOE (which defines APOE*4 from the major allele, APOE*3) is seen in African, Asians and Caucasians, this variant is not associated with Alzheimer's disease in African populations [63].

Types of association studies

Researchers, typically, carefully weigh comprehensiveness and efficiency in designing an association study. A highly comprehensive study would assess every variant in the region(s) under study, regardless of type, location and allele frequency. A highly efficient study would be designed to reduce costs, including genotyping and/or multiple testing costs. Genotyping costs can be saved by determining which SNPs are in linkage disequilibrium (LD). For example, if you knew that two SNPs were in complete LD in the specific population of interest, you would only need to genotype one to assess them both. Multiple testing costs can be reduced by only looking at SNPs with a high a priori chance of being associated. Note that as multiple testing correction should account for the effective number of independent tests performed, genotyping only one of two SNPs in complete LD does not reduce multiple testing costs; if the SNPs are in complete LD, only one effective independent test is being performed, regardless of whether one or two SNPs are genotyped (Bonferroni correction is overly conservative). As 'per SNP' genotyping costs continue to fall, it seems likely that multiple testing costs will become the predominant concern in efficiency. Therefore, we discuss efficiency in terms of the a priori likelihood for an SNP to be associated with the phenotype studied.

Different types of large-scale association studies and the balance they strike are shown in Figure 1, although, obviously, many studies are hybrids of these types. These approaches, which have been applied to candidate genes, regions and recently to the whole genome, [21, 76] are discussed in detail below, along with another technique (re-sequencing), which can currently only be applied on a small scale. Additional techniques that may be useful in 'special' populations, such as isolated founder and admixed populations, are discussed elsewhere [77–79].
Figure 1

Association study approaches: Efficiency versus comprehensiveness. Studies vary in their efficiency (the a priori likelihood of a tested single nucleotide polymorphism [SNP] being associated with a disease), which has an impact on genotyping and multiple testing costs. Highly efficient designs (as defined by multiple testing costs) are shown on the right, with less efficient designs on the left. Studies also vary in comprehensiveness, both in terms of the allele frequency spectrum assessed (A) and the extent the region under study is assessed (B). Highly comprehensive studies extend from top to bottom. The efficiency (or comprehensiveness) for a specific study type relative to another in this figure is certainly not meant to be quantitative but merely indicative of the direction (bigger or smaller). This figure is applicable to large-scale studies of candidate genes, regions or the whole genome. Different functional SNP approaches are represented in blue, while non-functional approaches are represented in green. Re-sequencing is currently only feasible for examining one or a few candidate genes and is therefore not depicted. (A) Using linkage disequilibrium (LD) approaches, rare alleles are less likely to be tagged and hence the rare allele region is not covered. Since non-synonymous SNPs (nsSNPs) are assessed directly, association with rare alleles can be readily detected; however, this is limited by the availability of these SNPs. The light colour in the rare allele region is to indicate that coverage is dependent on SNP discovery. In this figure, we consider the most obvious functional SNPs, the nsSNPs. We presume the efficiency of the other functional categories may be significantly lower. (B) Typically, there is a trade-off between efficiency and comprehensiveness. One may limit the study to nsSNPs in order to have high efficiency at the cost of comprehensiveness. Further increase in the efficiency (and decrease in comprehensiveness) can be achieved by focusing only on nsSNPs predicted or known to have a functional consequence. Similarly, it has been proposed that a study utilising SNPs that tag the highest number of other SNPs (ie SNPs in high LD regions) would be more efficient (but less comprehensive) than a study aiming at LD coverage of the full genome [75].

Re-sequencing

When there is strong a priori evidence that a gene may be involved in a disease, it is possible to sequence that gene in cases and controls [43, 80, 81]. This requires no prior knowledge of variants in the region and allows researchers comprehensively to evaluate all variants in a gene, regardless of their allele frequency. Usually, it is necessary to group the very rare variants (< 1 per cent) for power considerations [43, 80, 81]. While this approach is now possible for one or a few candidate genes, it is by no means comprehensive across the genome and dramatic reductions in sequencing costs are necessary for its implementation on a large scale [82–84].

LD

Given the high rate of LD in the genome, many variants do not need to be directly genotyped in order to be assessed. They may instead be assessed by genotyping another SNP in high LD. The goal of LD-based ('tagging') approaches is to test a sufficient number of common SNPs so that SNPs that are not directly tested are assessed through their high correlation with the genotyped SNPs. This can create efficiency in genotyping but does not reduce multiple testing costs (as discussed previously, multiple testing corrections should account for the effective number of independent tests, rather than the number of SNPs genotyped). Additionally, the efficiency of the approach is modest, since there is a low a priori chance that a specific assessed SNP is associated with disease. By focusing only on regions with high LD (in which a single SNP is likely to tag several other SNPs), one improves the efficiency because there is an increased likelihood for any assessed SNP (ie for one test) to be tagging a functional SNP that is associated with the phenotype of interest [75].

Tagging allows most common SNPs to be comprehensively assessed in linkage regions, [85] candidate genes [86] or the whole genome [87]. Tagging, however, is not comprehensive in terms of allele frequencies because it tends to work poorly on rare polymorphisms [88–92]. Given the clear importance of rare polymorphisms (Box 1), this presents a substantial drawback. While some analytical work suggests that long haplotypes may be used to achieve a degree of 'tagging' of the rare allele, this comes with a dramatic multiple testing cost [106]. The adequate assessment of rare alleles requires direct interrogation.

Functional SNPs

Functional variants are the most likely to be associated with diseases (in fact, non-functional variants should only be associated secondary to LD); therefore, genotyping studies using only functional SNPs are relatively efficient. Since these variants are directly assessed, these studies are comprehensive in terms of allele frequency, covering rare and common variants present in the databases or discovered during focused SNP discovery. Our poor ability to predict functional SNPs, however, means that this approach is generally far from comprehensive in terms of coverage of the region under study. Nevertheless, by focusing on the most obvious classes of potentially functional SNPs, such as those causing non-synonymous changes in proteins, researchers have had notable successes with association studies in candidate genes [107] or linkage regions [3, 22]. It is now possible to apply this method on a genome-wide scale, [75, 108] which increases comprehensiveness with some reduction in efficiency.

Extending the (potentially) functional SNP approach

There are many attractive features of the functional SNP approach, including its efficiency and ability to assess rare and common alleles. Additionally, a positive association automatically provides a candidate causative polymorphism.

A major criticism of the functional approach is its lack of comprehensiveness, [96] and extending the coverage has been difficult, given our poor ability to predict functional SNPs. We can, however, broadly define functional SNPs as SNPs in any class predicted to have an above-average chance of having a functional effect. Recent progress in comparative genomics is likely to dramatically increase the comprehensiveness of this approach.

Below, we address some traditional functional elements (non-synonymous, splicing and promoter SNPs), as well as functional sequences emerging from the study of genome conservation.

Non-synonymous

The most obvious class of potentially functional SNPs is those causing non-synonymous changes in proteins (nsSNPs). Over 60 per cent of known Mendelian disease mutations and almost all the consistent, common disease mutations in Table 1 involve nsSNPs [109]. While there is a clear ascertainment bias for studying and confirming associations with nsSNPs, they are inarguably important in disease.

Additional evidence that many nsSNPs are functional and subject to selection comes from candidate gene sequencing studies, which find that 60 per cent of the expected number of nsSNPs are missing [110, 111]. Furthermore, nsSNPs have lower minor allele frequencies than do synonymous SNPs [110, 111]. When we examined all coding SNPs currently in the SNP database (dbSNP), we also found a dearth of nsSNPs; these are expected to comprise two-thirds of coding SNPs [111] but instead comprised less than one-half (20,463 nsSNP out of 42,387 coding SNPs). The deficiency of nsSNPs was even more notable when the analysis was limited to conserved coding regions in which only one-third of SNPs were non-synonymous (8,828 of 23,397). (SNP definitions were derived from the Ensembl database, and conserved regions were as defined previously [112].)

Large-scale studies of nsSNPs maintain high efficiency while allowing reasonable coverage [75]. One could choose to further increase efficiency (and decrease comprehensiveness) by limiting a study only to nsSNPs with a high predicted likelihood of being damaging. A substantial proportion of such SNPs have already been implicated in human disease [103, 113].

Splicing

Perhaps the next most obvious class of potentially functional variants is SNPs around splice junctions. Mutations that affect splicing underlie 15 per cent of mutations in Mendelian diseases and hence are likely to play some role in common diseases [114].

Splicing is catalysed by weakly conserved 5' and 3' splice sites and a branch site, as well as exonic and intronic enhancers and silencers. Sites far from splice junctions can affect splicing, and a few mutations in these distant sites have been shown to cause human disease [115–120]. It appears, however, that most control of splicing lies in the 20 base pairs (bp) flanking each side of exon - intron boundaries [120]. These regions contain a high density of splicing enhancers (SEs), [120] have fewer SNPs than sequences further from splice junctions [120] and contain most of the known splicing mutations [114]. We find that these sequences are significantly conserved and have a relative dearth of SNPs (Table 2).
Table 2

Conservation and relative single nucleotide polymorphism (SNP) density in different types of functional regions.

 

Odds ratio ± standard error

Fold conservation

Transcriptsa

0.895 ± 0.003

12.0×

Transcripts: coding regions

0.762 ± 0.004

16.4×

Transcripts: non-coding

1.072 ± 0.004

6.2×

Conserved elementsb

0.748 ± 0.002

23.5×

Promoterc

0.995 ± 0.005

3.5×

Splice junctionsd

0.780 ± 0.007

10.3×

For each functional region, we report the odds ratio that a nucleotide in that region will be a variant by comparison with the rest of the genome (essentially, the relative SNP density) and standard error. The expected number is obtained using the validated SNP in the genome (4.9 M) and the total number of base pairs of the genome within a particular class of functional elements. A number less than 1 indicates a deficiency in SNP number. We also report the fold conservation (as defined previously [112]) compared with the genome average.

a Includes coding regions and untranslated regions (including RNA genes). All SNPs and the definitions of gene elements were obtained from the Ensembl database http://www.ensembl.org/.

b Defined previously [112] and obtained from the University of California, Santa Cruz website http://genome.ucsc.edu/.

c Within 500 base pairs (bp) upstream of the transcription start site.

d Within 20 bp of splice junctions.

Rather than testing all SNPs within the vicinity of a splice junction, one could increase efficiency by limiting the analysis to SNPs specifically predicted by computational models to affect splicing [121, 122]. Conversely, one can increase comprehensiveness by assessing SEs beyond 20 bp of splice junctions. SEs are most prevalent in exons [123, 124]. Some synonymous SNPs have also been shown to alter splicing [122]. Several programs are now available to predict SEs [125, 126]. In addition to SNPs within 20 bp of the junction, the interrogation of synonymous SNPs predicted to disrupt SE activity [126] increases study comprehensiveness.

Promoters

Promoters are cis-elements that lie upstream of transcription start sites and are responsible for transcription initiation [127]. The existence of regulatory variants affecting transcription has long been established [128, 129] and that have been shown to play a role in human disease [130, 131].

Even though the exact promoter sequence may not be easily discerned, recent work has shown that the 500 bp upstream from the transcription start site is almost always able to function as a promoter [132]. Defining the promoter, however, requires determining the 5' end of transcripts, which is typically done experimentally and hence is laborious [133–135]. As shown in Table 2, conservation in the promoter sequences is threefold higher than expected.

In addition to promoters, numerous other cis-acting elements (for example enhancers) contribute to gene regulation. These elements have been more difficult to identify because they can lie within coding sequences, introns or as far as 1 megabase away [120, 136, 137]. Defining these elements is a main goal of the ENCODE project [138]. Genomic work aimed at identifying transcription factor binding sites and other regulatory sequences experimentally and informatically is ongoing, [87, 139, 140] and study of conserved sequences holds promise for the identification of these regions.

Conserved sequences

Computational efforts have consistently found that approximately 5 per cent of the human genome shows conservation with other species [112, 141–148]. Although some regions may be conserved due to low mutation rates, clearly many, and perhaps most, of these regions are functionally important [149]. Indeed, most coding exons and many untranslated regions show interspecies conservation, although these only account for a minority of conserved regions. Conserved elements have been show to affect gene transcription levels, [150–156] RNA editing [112] and genome stability [157]. Additionally, conserved regions are enriched in intronic stretches surrounding alternatively spliced exons and have an excess of predicted secondary structure [112, 143, 158] and matrix-scaffold attachment regions [159]. Furthermore, they are enriched in stable gene deserts, which have been postulated to contain long range cis-regulatory regions [112]. Two lines of evidence suggest that many SNPs in conserved regions are subject to selection and, hence, are presumably functional: these regions contain a relative dearth of SNPs (Table 2), and the SNPs present there show a shift in allele frequency distribution towards rarer alleles [160, 161].

The identification of conserved non-coding elements has generated a paradigm shift for the definition of functional elements. Without knowing the exact function of each element, sequences conserved across species define a map of likely functional regions in the genome and SNPs in the regions are candidates for functional SNP association studies.

The study of conserved regions is a vibrant field, with diverse methods of defining conservation and views on the correct number and types of species to compare. Some groups have focused on very large regions while others have examined conservation of regions as small as 4 bp [112, 143, 144]. Analyses can be performed using very closely related species (such as primates) or very distant species (such as a range of eukaryotes) [112, 143, 144]. The study of species that are moderately distant (< 75 million years) has yielded many of the conserved elements, [162] while study of primates has provided insight on primate-specific regulatory elements [146]. In addition to identifying conserved elements subject to purifying selection, comparative genomics has identified genes with evidence of positive selection [163, 164]. Similar analyses may eventually be able to identify non-coding elements subjected to positive selection.

The proportion of functional elements that can be identified by comparative genomics is not yet clear. In a study using sequences from multiple yeast species, essentially all the known non-coding regulatory regions were identified as conserved [157]. Another study in yeast could identify conserved elements at the resolution of 6 bp transcription factor binding sites [165]. In mammals, using the currently available genomic sequences, most of the coding sequences and known regulatory sequences are conserved [166]. The analysis of more mammalian genome sequences will undoubtedly refine the current picture of conserved elements, although it is not clear that it will reach the same resolution achieved in yeast [162]. Nevertheless, it is likely that some functional sequences may not be identified through comparative genomics. If these SNPs do not fall into another obvious class of functional elements (like promoter regions), they may be missed by function-based association studies.

Generating a whole genome set of functional SNPs

The current feasibility of genome-wide function association studies depends upon the total number of functional SNPs and the extent to which such SNPs are represented in the databases. In the following discussion, we define functional SNPs as SNPs that fall into any of the above classes (ie non-synonymous, splicing, promoter, conserved [112]). Ongoing improvements in the definition of conserved regions may slightly change these estimates.

To estimate the total number of functional SNPs, we have utilised publicly available data from ENCODE regions. Ten regions (500 kilobases each) were re-sequenced in 48 unrelated individuals (16 Yoruba, 16 Centre D'Etude Du Polymorphisme Humain [CEPH], eight Han Chinese and eight Japanese). The SNPs in these regions, including those already present in the dbSNP and those newly discovered in sequencing, were then genotyped in the full 270 HapMap samples.

We first determined the total number of functional SNPs currently in dbSNP (using the above definitions). We then used the ENCODE regions to determine the allele frequency distribution (ie percentage rare and common) of conserved-region SNPs already in the dbSNP (ignoring those newly discovered by the ENCODE re-sequencing effort). We subsequently used information on the newly discovered ENCODE SNPs and our internal SNP discovery efforts to infer the percentage of SNPs missing from the dbSNP. This allowed us finally to estimate the total number of such SNPs. Implicit in this estimation is that the distribution of the allele frequency of functional SNPs is the same as the distribution of the subset of these SNPs that are in conserved elements (which account for over 75 per cent of the functional SNPs).

There are approximately 380,000 functional SNPs in dbSNP build 124. We infer from the ENCODE data that approximately 190,000 of these are common and 85,000 are rare (the remaining SNPs are very rare or database errors). Results were similar using data from both the CEPH and Yoruban samples. These results differ markedly from the expectations under the standard neutral model that there should be similar numbers of rare and common SNPs, suggesting that rare SNPs are missing in the dbSNP database [167]. Of the conserved region SNPs detected in the ENCODE Yoruban samples, the dbSNP database contained 23 per cent of the rare and 55 per cent of the common SNPs. Coverage was higher for conserved-region SNPs detected in the ENCODE CEPH samples, as the dbSNP database contained 35 per cent of the rare as well as 71 per cent of the common SNPs. Given that limited numbers of chromosomes typically are used for SNP discovery, both the dbSNP database and ENCODE are biased to miss rare SNPsa. The extent of this bias estimated using our internal SNP discovery efforts suggests that dbSNP coverage of rare SNPs is between approximately 25 per cent (in Caucasian) and approximately 15 per cent (in African).

From the above data, we estimate that there are approximately 350,000 common and 570,000 rare functional SNPs in the Yoruban samples and 270,000 common and 340,000 rare functional SNPs in the CEPH samples. Hence, a study that assayed only common functional SNPs would require a similar number of SNPs as an LD tagging study [161, 168]. Even greater genotyping efficiency could be found by combining the approaches. Additionally, the number of rare functional SNPs is within the ability of new genotyping technologies [98, 99, 169].

Discussion

Association studies based on functional SNPs are highly efficient as they study the set of SNPs most likely to cause disease. In the past, these studies have been criticised as not being comprehensive due to our incomplete knowledge of the functional elements of the human genome. Research into conserved sequences and the continuing influx of genomic sequences into the public domain promises to delineate many of these elements and increase the comprehensiveness of functional SNP association studies. The use of functionalbased association studies can, in principle, adequately assess rare alleles, poor coverage of which is a major drawback for LD-based association studies.

It may be possible to improve the balance between the comprehensiveness and efficiency (defined in terms of multiple testing costs) of a functional SNP-based study by incorporating the a priori probability that an SNP is functional into the statistical tests used for analysis. For instance, one might set a less stringent p-value threshold for a nonsense SNP than for one in a putative promoter. Additionally, one might set a lower p-value threshold for an SNP that was in two functional categories rather than in a single functional category. For example, Table 3 indicates that SNP density (which over the whole genome probably reflects selection and, hence, functionality) is particularly low in coding regions that are also conserved or flank splice junctions.
Table 3

SNP density per kilobase (kb) and counts in different types of functional regions.

 

Transcriptsa

Coding regions

Conserved elementsb

Promoterc

Splice junctiond

Transcripts

1.46 ± 0.005e (87065)

    

Coding regions

1.24 ± 0.006 (42387)

1.24 ± 0.006 (42387)

   

Conserved elements

1.03 ± 0.006 (31339)

0.98 ± 0.006 (23397)

1.22 ± 0.003 (170256)

  

Promoter

1.65 ± 0.038 (1854)

1.38 ± 0.06 (533)

1.03 ± 0.02 (2732)

1.62 ± 0.01 (28463)

 

Splice junctions

1.11 ± 0.012 (8728)

1.06 ± 0.012 (7519)

1.07 ± 0.013 (7149)

1.46 ± 0.086 (292)

1.27 ± 0.009 (19225)

The diagonal provides single nucleotide polymorphism (SNP) density for each region type and the off-diagonal provides density for regions of two types, either because one type is a subtype (coding is a subtype of transcript) or because of overlapping transcript definitions (a region may be in the promoter of one transcript, yet coding in another).

a Includes coding regions and untranslated regions (including RNA genes). All SNPs and the definitions of gene elements were obtained from the Ensembl database http://www.ensembl.org/.

b Defined previously [112] and obtained from the University of California, Santa Cruz website http://genome.ucsc.edu/.

c Within 500 base pairs (bp) upstream of the transcription start site.

d Within 20 bp of splice junctions.

e SNPs per kb ± standard error of the mean (total number of SNPs).

For comprehensive functional-based association studies to become practical, several goals need to be accomplished. First, the definition of functional elements needs to be refined through the availability of more genomic sequences. Secondly, SNP discovery efforts must be continued and expanded. Targeted re-sequencing in the functional regions may be necessary in order to compensate for bias against rare alleles in the databases, especially those that are population-specific and hence more likely to be functional [105]. The availability of extra sequencing capacity and efficient SNP discovery technologies can help to achieve this goal [170]. Thirdly, SNPs must be genotyped in the major ethnic populations to determine allele frequencies. HapMap now includes millions of SNPs, although these are biased to common SNPs [161]. Given the high-throughput genotyping technologies available, testing additional candidate functional SNPs to identify the common and rare SNPs can be readily performed. Indeed, we have recently undertaken the task of genotyping approximately 30,000 nsSNPs from the public databases to identify a set of approximately 20,000 that are polymorphic in at least one population [105].

With the availability of the functional elements and the SNPs, only approximately 270,000 - 350,000 SNPs must be genotyped to assess common functional SNPs in the genome. Furthermore, the genotyping of 300,000 - 500,000 additional SNPs will allow assessment of rare functional SNPs which have been implicated in many common diseases and are inadequately assessed by other approaches.

Box 1. Common variant/common disease versus rare variant/common disease

For the purposes of this review, we use the standard definition of a polymorphism as a variant whose minor allele frequency (MAF) is above 1 per cent, and define common alleles/polymorphisms as those with MAF > 10 per cent, rare alleles/polymorphisms as those with MAF 1 - 10 per cent and very rare alleles/variants as those with MAF < 1 per cent. In the past decade, there has been substantial debate over the importance of common alleles versus rare alleles (or even very rare variants) in common, complex human diseases. Theoretical work has been used to argue all points of view: that causative common disease alleles are most likely common alleles, or rare alleles, or very rare alleles [93–95].

One key argument for common alleles relies on the perceived greater practical difficulties in studying rare alleles rather than common alleles. First, analysis methods are particularly sensitive to genotyping errors of rare alleles and rare alleles have been particularly prone to genotyping errors [96, 97]. Recent improvements in genotyping technologies, however, dramatically lessen these concerns [98, 99]. Secondly, rare alleles are more likely to be population specific and therefore are more likely to generate spurious associations due to population substructure. Again, improvements, this time to analytical methods, allow us to detect and adjust for these artifacts [100, 101]. Thirdly, it has been argued that the power to detect associations with rare alleles appears low when compared with that to detect common alleles. While this is certainly true if one assumes the same genotypic relative risk, this assumption is arbitrary, and if one instead uses another arbitrary assumption of equal population attributable risk, then the power to detect rare alleles would be significantly better than that for common alleles. Probably, a more reasonable approach is to consider a specific genetic effect size (eg defined by likelihood of the odds (LOD) score in sibling-pair analysis) of a locus and assume that causative alleles generate this specific effect size [102]. Given this assumption, the power to detect common and rare alleles is fairly similar (data not shown). Finally, rare alleles are difficult to 'tag' and therefore need to be assessed directly, creating two problems: alleles must be in databases in order to be assessed and genotyping all of the rare alleles in the genome would be at least an order of magnitude larger than contemplated for the linkage disequilibrium (LD)-based approach for common alleles. These concerns, while substantial, may be addressed by single nucleotide polymorphism (SNP) discovery and focusing genotyping efforts on rare SNPs that are also potentially functional.

One theoretical argument for rare alleles is that purifying selection should keep the frequency of deleterious functional alleles low. Indeed, in a study of approximately 30,000 non-synonymous SNPs, we confirmed previous observations that SNPs predicted by PolyPhen [103, 104] to be damaging have significantly lower allele frequencies than SNPs predicted to be benign. This effect is largely due to an enrichment of damaging SNPs in the MAF < 10 per cent category [105].

Perhaps the strongest argument comes from an examination of Table 1, which indicates that both common and rare alleles are important. In light of these data, it is clearly essential for common disease association studies to investigate rare, as well as common, alleles.

End notes

a SNP discovery efforts interrogate a limited number of individuals and hence are more likely to find a common minor allele than a rare minor allele. For example, a study using only one individual (two chromosomes) has a 50 per cent chance of including both alleles of a 50 per cent allele frequency SNP, but only a 2 per cent chance of finding both alleles of a 1 per cent frequency SNP. Hence 1 per cent alleles are more likely to be missed in both dbSNP and the targeted re-sequencing than 10 per cent alleles. In addition, SNPs in dbSNP and those identified in this targeted re-sequencing effort are more biased to be more common in a different ethnic population where they may have been discovered. Indeed when studying alleles that are rare in the Caucasian population, we found the frequency in other populations to be higher for SNPs already in dbSNP than for SNPs identified through SNP discovery in the Caucasian population (MF unpublished results).

Authors’ Affiliations

(1)
ParAllele BioScience (Now Affymetrix, Inc)

References

  1. Risch N, Merikangas K: The future of genetic studies of complex human diseases. Science. 1996, 273: 1516-1517. 10.1126/science.273.5281.1516.PubMedView ArticleGoogle Scholar
  2. Lohmueller KE, Pearce CL, Pike M, et al: Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet. 2003, 33: 177-182. 10.1038/ng1071.PubMedView ArticleGoogle Scholar
  3. Begovich AB, Carlton VEH, Honigberg LA, et al: A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am J Hum Genet. 2004, 75: 330-337. 10.1086/422827.PubMed CentralPubMedView ArticleGoogle Scholar
  4. van Oene M, Wintle RF, Liu X, et al: Association of the lymphoid tyrosine phosphatase R620W variant with rheumatoid arthritis, but not Crohn's disease, in Canadian populations. Arthritis Rheum. 2005, 52: 1993-1998. 10.1002/art.21123.PubMedView ArticleGoogle Scholar
  5. Simkins HM, Merriman ME, Highton J, et al: Association of the PTPN22 locus with rheumatoid arthritis in a New Zealand Caucasian cohort. Arthritis Rheum. 2005, 52: 2222-2225. 10.1002/art.21126.PubMedView ArticleGoogle Scholar
  6. Hinks A, Barton A, John S, et al: Association between the PTPN22 gene and rheumatoid arthritis and juvenile idiopathic arthritis in a UK population: Further support that PTPN22 is an autoimmunity gene. Arthritis Rheum. 2005, 52: 1694-1699. 10.1002/art.21049.PubMedView ArticleGoogle Scholar
  7. Zhernakova A, Eerligh P, Wijmenga C, et al: Differential association of the PTPN22 coding variant with autoimmune diseases in a Dutch population. Genes Immun. 2005, 6: 459-461. 10.1038/sj.gene.6364220.PubMedView ArticleGoogle Scholar
  8. Viken MK, Amundsen SS, Kvien TK, et al: Association analysis of the 1858C > T polymorphism in the PTPN22 gene in juvenile idiopathic arthritis and other autoimmune diseases. Genes Immun. 2005, 6: 271-273. 10.1038/sj.gene.6364178.PubMedView ArticleGoogle Scholar
  9. Criswell LA, Pfeiffer KA, Lum RF, et al: Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: The PTPN22 620W allele associates with multiple autoimmune phenotypes. Am J Hum Genet. 2005, 76: 561-571. 10.1086/429096.PubMed CentralPubMedView ArticleGoogle Scholar
  10. Lee AT, Li W, Liew A, et al: The PTPN22 R620W polymorphism associates with RF positive rheumatoid arthritis in a dose-dependent manner but not with HLA-SE status. Genes Immun. 2005, 6: 129-133. 10.1038/sj.gene.6364159.PubMedView ArticleGoogle Scholar
  11. Orozco G, Sanchez E, Gonzalez-Gay MA, et al: Association of a functional single-nucleotide polymorphism of PTPN22, encoding lymphoid protein phosphatase, with rheumatoid arthritis and systemic lupus erythematosus. Arthritis Rheum. 2005, 52: 219-224. 10.1002/art.20771.PubMedView ArticleGoogle Scholar
  12. Steer S, Lad B, Grumley JA, et al: Association of R602W in a protein tyrosine phosphatase gene with a high risk of rheumatoid arthritis in a British population: Evidence for an early onset/disease severity effect. Arthritis Rheum. 2005, 52: 358-360. 10.1002/art.20737.PubMedView ArticleGoogle Scholar
  13. Seldin MF, Shigeta R, Laiho K, et al: Finnish case-control and family studies support PTPN22 R620W polymorphism as a risk factor in rheumatoid arthritis, but suggest only minimal or no effect in juvenile idiopathic arthritis. Genes Immun. 2005, 6: 720-722.PubMedGoogle Scholar
  14. Mori M, Yamada R, Kobayashi K, et al: Ethnic differences in allele frequency of autoimmune-disease-associated SNPs. J Hum Genet. 2005, 50: 264-266. 10.1007/s10038-005-0246-8.PubMedView ArticleGoogle Scholar
  15. Qu H, Tessier MC, Hudson TJ, Polychronakos C: Confirmation of the association of the R620W polymorphism in the protein tyrosine phosphatase PTPN22 with type 1 diabetes in a family based study. J Med Genet. 2005, 42: 266-270. 10.1136/jmg.2004.026971.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Zheng W, She JX: Genetic association between a lymphoid tyrosine phosphatase (PTPN22) and type 1 diabetes. Diabetes. 2005, 54: 906-908. 10.2337/diabetes.54.3.906.PubMedView ArticleGoogle Scholar
  17. Ladner MB, Bottini N, Valdes AM, Noble JA: Association of the single nucleotide polymorphism C1858T of the PTPN22 gene with type 1 diabetes. Hum Immunol. 2005, 66: 60-64.PubMedView ArticleGoogle Scholar
  18. Onengut-Gumuscu S, Ewens KG, Spielman RS, Concannon P: A functional polymorphism (1858C/T) in the PTPN22 gene is linked and associated with type I diabetes in multiplex families. Genes Immun. 2004, 5: 678-680. 10.1038/sj.gene.6364138.PubMedView ArticleGoogle Scholar
  19. Smyth D, Cooper JD, Collins JE, et al: Replication of an association between the lymphoid tyrosine phosphatase locus (LYP/PTPN22) with type 1 diabetes, and evidence for its role as a general autoimmunity locus. Diabetes. 2004, 53: 3020-3023. 10.2337/diabetes.53.11.3020.PubMedView ArticleGoogle Scholar
  20. Bottini N, Musumeci L, Alonso A, et al: A functional variant of lymphoid tyrosine phosphatase is associated with type 1 diabetes. Nat Genet. 2004, 36: 337-338. 10.1038/ng1323.PubMedView ArticleGoogle Scholar
  21. Klein RJ, Zeiss C, Chew EY, et al: Complement factor H polymorphism in age-related macular degeneration. Science. 2005, 308: 385-389. 10.1126/science.1109557.PubMed CentralPubMedView ArticleGoogle Scholar
  22. Edwards AO, Ritter III, Abel KJ, et al: Complement factor H polymorphism and age-related macular degeneration. Science. 2005, 308: 421-424. 10.1126/science.1110189.PubMedView ArticleGoogle Scholar
  23. Conley YP, Thalamuthu A, Jakobsdottir J, et al: Candidate gene analysis suggests a role for fatty acid biosynthesis and regulation of the complement system in the etiology of age-related maculopathy. Hum Mol Genet. 2005, 14: 1991-2002. 10.1093/hmg/ddi204.PubMedView ArticleGoogle Scholar
  24. Hageman GS, Anderson DH, Johnson LV, et al: A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci USA. 2005, 102: 7227-7232. 10.1073/pnas.0501536102.PubMed CentralPubMedView ArticleGoogle Scholar
  25. Haines JL, Hauser MA, Schmidt S, et al: Complement factor H variant increases the risk of age-related macular degeneration. Science. 2005, 308: 419-421. 10.1126/science.1110359.PubMedView ArticleGoogle Scholar
  26. Zareparsi S, Branham KEH, Li M, et al: Strong association of the Y402H variant in complement factor H at 1q32 with susceptibility to age-related macular degeneration. Am J Hum Genet. 2005, 77: 149-153. 10.1086/431426.PubMed CentralPubMedView ArticleGoogle Scholar
  27. Bertina RM, Koeleman BPC, Koster T, et al: Mutation in blood coagulation factor V associated with resistance to activated protein C. Nature. 1994, 369: 64-67. 10.1038/369064a0.PubMedView ArticleGoogle Scholar
  28. Ridker PM, Hennekens CH, Lindpaintner K, et al: Mutation in the gene coding for coagulation factor V and the risk of myocardial infarction, stroke, and venous thrombosis in apparently healthy men. N Engl J Med. 1995, 332: 912-917. 10.1056/NEJM199504063321403.PubMedView ArticleGoogle Scholar
  29. Zoller B, Dahlback B: Linkage between inherited resistance to activated protein C and factor V gene mutation in venous thrombosis. Lancet. 1994, 343: 1536-1538. 10.1016/S0140-6736(94)92940-8.PubMedView ArticleGoogle Scholar
  30. Zoller B, Svensson PJ, He X, Dahlback B: Identification of the same factor V gene mutation in 47 out of 50 thrombosis-prone families with inherited resistance to activated protein C. J Clin Invest. 1994, 94: 2521-2524. 10.1172/JCI117623.PubMed CentralPubMedView ArticleGoogle Scholar
  31. Ma DD, Aboud MR, Williams BG, Isbister JP: Activated protein c resistance (APC) and inherited factor V (FV) mis-sense mutation in patients with venous and arterial thrombosis in a haematology clinic. Aust N Z J Med. 1995, 25: 151-154. 10.1111/j.1445-5994.1995.tb02828.x.PubMedView ArticleGoogle Scholar
  32. Ridker PM, Miletich JP, Stampfer MJ, et al: Factor V Leiden and risks of recurrent idiopathic venous thromboembolism. Circulation. 1995, 92: 2800-2802. 10.1161/01.CIR.92.10.2800.PubMedView ArticleGoogle Scholar
  33. Arruda VR, Annichino-Bizzacchi JM, Costa FF, Reitsma PH: Factor V Leiden (FVQ 506) is common in a Brazilian population. Am J Hematol. 1995, 49: 242-243. 10.1002/ajh.2830490312.PubMedView ArticleGoogle Scholar
  34. Schobess R, Junker R, Auberger K, et al: Factor V G1691A and prothrombin G20210A in childhood spontaneous venous thrombosis -- Evidence of an age-dependent thrombotic onset in carriers of factor V G1691A and prothrombin G20210A mutation. Eur J Pediatr. 1999, 158 (Suppl 3): S105-S108.PubMedView ArticleGoogle Scholar
  35. Rees DC, Cox M, Clegg JB: World distribution of factor V Leiden. Lancet. 1995, 346: 1133-1134. 10.1016/S0140-6736(95)91803-5.PubMedView ArticleGoogle Scholar
  36. Miyata T, Kawasaki T, Fujimura H, et al: The prothrombin gene G20210A mutation is not found among Japanese patients with deep vein thrombosis and healthy individuals. Blood Coagul Fibrinolysis. 1998, 9: 451-452. 10.1097/00001721-199807000-00011.PubMedView ArticleGoogle Scholar
  37. Cumming AM, Keeney S, Salden A, et al: The prothrombin gene G20210A variant: Prevalence in a UK anticoagulant clinic population. Br J Haematol. 1997, 98: 353-355. 10.1046/j.1365-2141.1997.2353052.x.PubMedView ArticleGoogle Scholar
  38. Cattaneo M, Chantarangkul V, Taioli E, et al: The G20210A mutation of the prothrombin gene in patients with previous first episodes of deep-vein thrombosis: Prevalence and association with factor V G1691A, methylenetetrahydrofolate reductase C677T and plasma prothrombin levels. Thromb Res. 1999, 93: 1-8. 10.1016/S0049-3848(98)00136-4.PubMedView ArticleGoogle Scholar
  39. Margaglione M, Brancaccio V, Giuliani N, et al: Increased risk for venous thrombosis in carriers of the prothrombin G - > A20210 gene variant. Ann Intern Med. 1998, 129: 89-93.PubMedView ArticleGoogle Scholar
  40. Poort SR, Rosendaal FR, Reitsma PH, Bertina RM: A common genetic variation in the 3'-untranslated region of the prothrombin gene is associated with elevated plasma prothrombin levels and an increase in venous thrombosis. Blood. 1996, 88: 3698-3703.PubMedGoogle Scholar
  41. Sachchithananthan M, Stasinopoulos SJ, Wilusz J, Medcalf RL: The relationship between the prothrombin upstream sequence element and the G20210A polymorphism: The influence of a competitive environment for mRNA 3'-end formation. Nucleic Acids Res. 2005, 33: 1010-1020. 10.1093/nar/gki245.PubMed CentralPubMedView ArticleGoogle Scholar
  42. Rees DC, Chapman NH, Webster MT, et al: Born to clot: The European burden. Br J Haematol. 1999, 105: 564-566. 10.1111/j.1365-2141.1999.01361.x.PubMedView ArticleGoogle Scholar
  43. Lesage S, Zouali H, Cezard JP, et al: CARD15/NOD2 mutational analysis and genotype-phenotype correlation in 612 patients with inflammatory bowel disease. Am J Hum Genet. 2002, 70: 845-857. 10.1086/339432.PubMed CentralPubMedView ArticleGoogle Scholar
  44. Hampe J, Cuthbert A, Croucher PJ, et al: Association between insertion mutation in NOD2 gene and Crohn's disease in German and British populations. Lancet. 2001, 357: 1925-1928. 10.1016/S0140-6736(00)05063-7.PubMedView ArticleGoogle Scholar
  45. Ogura Y, Bonen DK, Inohara N, et al: A frameshift mutation in NOD2 associated with susceptibility to Crohn's disease. Nature. 2001, 411: 603-606. 10.1038/35079114.PubMedView ArticleGoogle Scholar
  46. Hugot JP, Chamaillard M, Zouali H, et al: Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease. Nature. 2001, 411: 599-603. 10.1038/35079107.PubMedView ArticleGoogle Scholar
  47. Kim TH, Rahman P, Jun JB, et al: Analysis of CARD15 polymorphisms in Korean patients with ankylosing spondylitis reveals absence of common variants seen in western populations. J Rheumatol. 2004, 31: 1959-1961.PubMedGoogle Scholar
  48. Yamazaki K, Takazoe M, Tanaka T, et al: Absence of mutation in the NOD2/CARD15 gene among 483 Japanese patients with Crohn's disease. J Hum Genet. 2002, 47: 469-472. 10.1007/s100380200067.PubMedView ArticleGoogle Scholar
  49. Stockton JC, Howson JM, Awomoyi AA, et al: Polymorphism in NOD2, Crohn's disease, and susceptibility to pulmonary tuberculosis. FEMS Immunol Med Microbiol. 2004, 41: 157-160. 10.1016/j.femsim.2004.02.004.PubMedView ArticleGoogle Scholar
  50. CHEK2 Breast Cancer Case-Control Consortium: CHEK2*1100delC and susceptibility to breast cancer: A collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies. Am J Hum Genet. 2004, 74: 1175-1182.View ArticleGoogle Scholar
  51. Broeks A, de Witte L, Nooijen A, et al: Excess risk for contralateral breast cancer in CHEK2*1100delC germline mutation carriers. Breast Cancer Res Treat. 2004, 83: 91-93. 10.1023/B:BREA.0000010697.49896.03.PubMedView ArticleGoogle Scholar
  52. Cybulski C, Gorski B, Huzarski T, et al: CHEK2 is a multiorgan cancer susceptibility gene. Am J Hum Genet. 2004, 75: 1131-1135. 10.1086/426403.PubMed CentralPubMedView ArticleGoogle Scholar
  53. Dufault MR, Betz B, Wappenschmidt B, et al: Limited relevance of the CHEK2 gene in hereditary breast cancer. Int J Cancer. 2004, 110: 320-325. 10.1002/ijc.20073.PubMedView ArticleGoogle Scholar
  54. Gorski B, Cybulski C, Huzarski T, et al: Breast cancer predisposing alleles in Poland. Breast Cancer Res Treat. 2005, 92: 19-24. 10.1007/s10549-005-1409-1.PubMedView ArticleGoogle Scholar
  55. Meijers-Heijboer H, van den Ouweland A, Klijn J, et al: Low-penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet. 2002, 31: 55-59. 10.1038/ng879.PubMedView ArticleGoogle Scholar
  56. Vahteristo P, Bartkova J, Eerola H, et al: A CHEK2 genetic variant contributing to a substantial fraction of familial breast cancer. Am J Hum Genet. 2002, 71: 432-438. 10.1086/341943.PubMed CentralPubMedView ArticleGoogle Scholar
  57. Corder EH, Saunders AM, Risch NJ, et al: Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science. 1993, 261: 921-923. 10.1126/science.8346443.PubMedView ArticleGoogle Scholar
  58. Saunders AM, Strittmatter WJ, Schmechel D, et al: Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology. 1993, 43: 1467-1472. 10.1212/WNL.43.8.1467.PubMedView ArticleGoogle Scholar
  59. Mayeux R, Stern Y, Ottman R, et al: The apolipoprotein epsilon 4 allele in patients with Alzheimer's disease. Ann Neurol. 1993, 34: 752-754. 10.1002/ana.410340527.PubMedView ArticleGoogle Scholar
  60. Anon: Apolipoprotein E genotype and Alzheimer's disease. Alzheimer's Disease Collaborative Group. Lancet. 1993, 342: 737-738. 10.1016/0140-6736(93)91728-5.View ArticleGoogle Scholar
  61. Strittmatter WJ, Roses AD: Apolipoprotein E and Alzheimer disease. Proc Natl Acad Sci USA. 1995, 92: 4725-4727. 10.1073/pnas.92.11.4725.PubMed CentralPubMedView ArticleGoogle Scholar
  62. 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. 10.1046/j.1469-1809.1999.6340301.x.PubMedView ArticleGoogle Scholar
  63. Sayi JG, Patel NB, Premkumar DR, et al: Apolipoprotein E polymorphism in elderly east Africans. East Afr Med J. 1997, 74: 668-670.PubMedGoogle Scholar
  64. Lane KA, Gao S, Hui SL, et al: Apolipoprotein E and mortality in African-Americans and Yoruba. J Alzheimers Dis. 2003, 5: 383-390.PubMed CentralPubMedGoogle Scholar
  65. Wu JH, Lo SK, Wen MS, Kao JT: Characterization of apolipoprotein E genetic variations in Taiwanese: Association with coronary heart disease and plasma lipid levels. Hum Biol. 2002, 74: 25-31. 10.1353/hub.2002.0012.PubMedView ArticleGoogle Scholar
  66. Gloyn AL, Weedon MN, Owen KR, et al: Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003, 52: 568-572.PubMedView ArticleGoogle Scholar
  67. Laukkanen O, Pihlajamaki J, Lindstrom J, et al: Polymorphisms of the SUR1 (ABCC8) and Kir6.2 (KCNJ11) genes predict the conversion from impaired glucose tolerance to type 2 diabetes. The Finnish Diabetes Prevention Study. J Clin Endocrinol Metab. 2004, 89: 6286-6290. 10.1210/jc.2004-1204.PubMedView ArticleGoogle Scholar
  68. McCarthy MI: Progress in defining the molecular basis of type 2 diabetes mellitus through susceptibility-gene identification. Hum Mol Genet. 2004, 13: R33-R41. 10.1093/hmg/ddh057.PubMedView ArticleGoogle Scholar
  69. Dean M, Carrington M, Winkler C, et al: Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Hemophilia Growth and Development Study, Multicenter AIDS Cohort Study, Multicenter Hemophilia Cohort Study, San Francisco City Cohort, ALIVE Study. Science. 1996, 273: 1856-1862. 10.1126/science.273.5283.1856.PubMedView ArticleGoogle Scholar
  70. Huang Y, Paxton WA, Wolinsky SM, et al: The role of a mutant CCR5 allele in HIV-1 transmission and disease progression. Nat Med. 1996, 2: 1240-1243. 10.1038/nm1196-1240.PubMedView ArticleGoogle Scholar
  71. Liu R, Paxton WA, Choe S, et al: Homozygous defect in HIV-1 coreceptor accounts for resistance of some multiply-exposed individuals to HIV-1 infection. Cell. 1996, 86: 367-377. 10.1016/S0092-8674(00)80110-5.PubMedView ArticleGoogle Scholar
  72. Samson M, Libert F, Doranz BJ, et al: Resistance to HIV-1 infection in caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature. 1996, 382: 722-725. 10.1038/382722a0.PubMedView ArticleGoogle Scholar
  73. Zimmerman PA, Buckler-White A, Alkhatib G, et al: Inherited resistance to HIV-1 conferred by an inactivating mutation in CC chemokine receptor 5: Studies in populations with contrasting clinical phenotypes, defined racial background, and quantified risk. Mol Med. 1997, 3: 23-36.PubMed CentralPubMedGoogle Scholar
  74. Martinson JJ, Chapman NH, Rees DC, et al: Global distribution of the CCR5 gene 32-basepair deletion. Nat Genet. 1997, 16: 100-103. 10.1038/ng0597-100.PubMedView ArticleGoogle Scholar
  75. Shiffman D, Ellis SG, Rowland CM, et al: Identification of four gene variants associated with myocardial infarction. Am J Hum Genet. 2005, 77: 596-605. 10.1086/491674.PubMed CentralPubMedView ArticleGoogle Scholar
  76. Smith MW, O'Brien SJ: Mapping by admixture linkage disequilibrium: Advances, limitations and guidelines. Nat Rev Genet. 2005, 6: 623-632. 10.1038/nrg1657.PubMedView ArticleGoogle Scholar
  77. Abecasis GR, Ghosh D, Nichols TE: Linkage disequilibrium: Ancient history drives the new genetics. Hum Hered. 2005, 59: 118-124. 10.1159/000085226.PubMedView ArticleGoogle Scholar
  78. Halder I, Shriver MD: Measuring and using admixture to study the genetics of complex diseases. Hum Genomics. 2003, 1: 52-62.PubMed CentralPubMedView ArticleGoogle Scholar
  79. Vaisse C, Clement K, Durand E, et al: Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J Clin Invest. 2000, 106: 253-262. 10.1172/JCI9238.PubMed CentralPubMedView ArticleGoogle Scholar
  80. Cohen JC, Kiss RS, Pertsemlidis A, et al: Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science. 2004, 305: 869-872. 10.1126/science.1099870.PubMedView ArticleGoogle Scholar
  81. Margulies M, Egholm M, Altman E, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.PubMed CentralPubMedGoogle Scholar
  82. Faham M, Zheng J, Moorhead M, et al: Multiplexed variation scanning for 1,000 amplicons in hundreds of patients using mismatch repair detection (MRD) on tag arrays. Proc Natl Acad Sci USA. 2005, 102: 14717-14722. 10.1073/pnas.0506677102.PubMed CentralPubMedView ArticleGoogle Scholar
  83. Cargill M, Altshuler D, Ireland J, et al: Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 1999, 22: 231-238. 10.1038/10290.PubMedView ArticleGoogle Scholar
  84. de Bakker PI, Yelensky R, Pe'er I, et al: Efficiency and power in genetic association studies. Nat Genet. 2005, 37: 1217-1223. 10.1038/ng1669.PubMedView ArticleGoogle Scholar
  85. Van Eerdewegh P, Little RD, Dupuis J, et al: Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature. 2002, 418: 426-430. 10.1038/nature00878.PubMedView ArticleGoogle Scholar
  86. Saleh M, Vaillancourt JP, Graham RK, et al: Differential modulation of endotoxin responsiveness by human caspase-12 polymorphisms. Nature. 2004, 429: 75-79. 10.1038/nature02451.PubMedView ArticleGoogle Scholar
  87. Kim TH, Barrera LO, Qu C, et al: Direct isolation and identification of promoters in the human genome. Genome Res. 2005, 15: 830-839. 10.1101/gr.3430605.PubMed CentralPubMedView ArticleGoogle Scholar
  88. Ahmadi KR, Weale ME, Xue ZY, et al: A single-nucleotide polymorphism tagging set for human drug metabolism and transport. Nat Genet. 2005, 37: 84-89.PubMedView ArticleGoogle Scholar
  89. Evans DM, Cardon LR, Morris AP: Genotype prediction using a dense map of SNPs. Genet Epidemiol. 2004, 27: 375-384. 10.1002/gepi.20045.PubMedView ArticleGoogle Scholar
  90. Carlson CS, Eberle MA, Rieder MJ, et al: Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004, 74: 106-120. 10.1086/381000.PubMed CentralPubMedView ArticleGoogle Scholar
  91. Hu X, Schrodi SJ, Ross DA, Cargill M: Selecting tagging SNPs for association studies using power calculations from genotype data. Hum Hered. 2004, 57: 156-170. 10.1159/000079246.PubMedView ArticleGoogle Scholar
  92. Ke X, Durrant C, Morris AP, et al: Efficiency and consistency of haplotype tagging of dense SNP maps in multiple samples. Hum Mol Genet. 2004, 13: 2557-2565. 10.1093/hmg/ddh294.PubMedView ArticleGoogle Scholar
  93. Reich DE, Lander ES: On the allelic spectrum of human disease. Trends Genet. 2001, 17: 502-510. 10.1016/S0168-9525(01)02410-6.PubMedView ArticleGoogle Scholar
  94. Pritchard JK: Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet. 2001, 69: 124-137. 10.1086/321272.PubMed CentralPubMedView ArticleGoogle Scholar
  95. Pritchard JK, Cox NJ: The allelic architecture of human disease genes: Common disease-common variant... or not?. Hum Mol Genet. 2002, 11: 2417-2423. 10.1093/hmg/11.20.2417.PubMedView ArticleGoogle Scholar
  96. Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005, 6: 95-108.PubMedView ArticleGoogle Scholar
  97. Gordon D, Finch SJ, Nothnagel M, Ott J: Power and sample size calculations for case-control genetic association tests when errors are present: Application to single nucleotide polymorphisms. Hum Hered. 2002, 54: 22-33. 10.1159/000066696.PubMedView ArticleGoogle Scholar
  98. Fan JB, Oliphant A, Shen R, et al: Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol. 2003, 68: 69-78. 10.1101/sqb.2003.68.69.PubMedView ArticleGoogle Scholar
  99. Hardenbol P, Yu F, Belmont J, et al: Highly multiplexed molecular inversion probe genotyping: Over 10,000 targeted SNPs genotyped in a single tube assay. Genome Res. 2005, 15: 269-275. 10.1101/gr.3185605.PubMed CentralPubMedView ArticleGoogle Scholar
  100. Reich DE, Goldstein DB: Detecting association in a case-control study while correcting for population stratification. Genet Epidemiol. 2001, 20: 4-16. 10.1002/1098-2272(200101)20:1<4::AID-GEPI2>3.0.CO;2-T.PubMedView ArticleGoogle Scholar
  101. Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.PubMed CentralPubMedGoogle Scholar
  102. Jones HB, Faham M: Evidence and implications for multiplicative interactions among loci predisposing to human common disease. Hum Hered. 2005, 59: 176-184. 10.1159/000086118.PubMedView ArticleGoogle Scholar
  103. Sunyaev S, Ramensky V, Koch I, et al: Prediction of deleterious human allele. Hum Mol Genet. 2001, 10: 591-597. 10.1093/hmg/10.6.591.PubMedView ArticleGoogle Scholar
  104. Ramensky V, Bork P, Sunyaev S: Human non-synonymous SNPs: Server and survey. Nucleic Acids Res. 2002, 30: 3894-3900. 10.1093/nar/gkf493.PubMed CentralPubMedView ArticleGoogle Scholar
  105. Ireland J, Carlton VE, Falkowski M, et al: Large-scale characterization of public database SNPs causing non-synonymous changes in three ethnic groups. Hum Genet. 2006, 119: 75-83. 10.1007/s00439-005-0105-x.PubMedView ArticleGoogle Scholar
  106. Lin S, Chakravarti A, Cutler DJ: Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat Genet. 2004, 36: 1181-1188. 10.1038/ng1457.PubMedView ArticleGoogle Scholar
  107. Altshuler D, Hirschhorn JN, Klannemark M, et al: The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000, 26: 76-80. 10.1038/79216.PubMedView ArticleGoogle Scholar
  108. Haga H, Yamada R, Ohnishi Y, et al: Gene-based SNP discovery as part of the Japanese Millennium Genome Project 2002. Identification of 190,562 genetic variations in the human genome. J Hum Genet. 2002, 47: 605-610. 10.1007/s100380200092.PubMedView ArticleGoogle Scholar
  109. Botstein D, Risch N: Discovering genotypes underlying human phenotypes: Past successes for Mendelian disease, future approaches for complex disease. Nat Genet. 2003, 33: 228-237. 10.1038/ng1090.PubMedView ArticleGoogle Scholar
  110. Halushka MK, Fan J-B, Bentley K, et al: Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet. 1999, 22: 239-247. 10.1038/10297.PubMedView ArticleGoogle Scholar
  111. Cargill M, Altshuler D, Ireland J, et al: Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 1999, 22: 231-238. 10.1038/10290.PubMedView ArticleGoogle Scholar
  112. Siepel A, Bejerano G, Pedersen JS, et al: Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005, 15: 1034-1050. 10.1101/gr.3715005.PubMed CentralPubMedView ArticleGoogle Scholar
  113. Crawford DC, Akey DT, Nickerson DA: The patterns of natural variation in human genes. Annu Rev Genomics Hum Genet. 2005, 6: 287-312. 10.1146/annurev.genom.6.080604.162309.PubMedView ArticleGoogle Scholar
  114. Krawczak M, Reiss J, Cooper DN: The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: Causes and consequences. Hum Genet. 1992, 90: 41-54.PubMedView ArticleGoogle Scholar
  115. Treisman R, Orkin SH, Maniatis T: Specific transcription and RNA splicing defects in five cloned beta-thalassaemia genes. Nature. 1983, 302: 591-596. 10.1038/302591a0.PubMedView ArticleGoogle Scholar
  116. Mitchell GA, Labuda D, Fontaine G, et al: Splice-mediated insertion of an Alu sequence inactivates ornithine delta-aminotransferase: A role for Alu elements in human mutation. Proc Natl Acad Sci USA. 1991, 88: 815-819. 10.1073/pnas.88.3.815.PubMed CentralPubMedView ArticleGoogle Scholar
  117. Pagani F, Buratti E, Stuani C, et al: A new type of mutation causes a splicing defect in ATM. Nat Genet. 2002, 30: 426-429. 10.1038/ng858.PubMedView ArticleGoogle Scholar
  118. Min GL, Martiat P, Pu GA, Goldman J: Use of pulsed field gel electrophoresis to characterize BCR gene involvement in CML patients lacking M-BCR rearrangement. Leukemia. 1990, 4: 650-656.PubMedGoogle Scholar
  119. Zhang XH, Leslie CS, Chasin LA: Dichotomous splicing signals in exon flanks. Genome Res. 2005, 15: 768-779. 10.1101/gr.3217705.PubMed CentralPubMedView ArticleGoogle Scholar
  120. Fairbrother WG, Holste D, Burge CB, Sharp PA: Single nucleotide polymorphism-based validation of exonic splicing enhancers. PLoS Biol. 2004, 2: E268-10.1371/journal.pbio.0020268.PubMed CentralPubMedView ArticleGoogle Scholar
  121. Senapathy P, Shapiro MB, Harris NL: Splice junctions, branch point sites, and exons: Sequence statistics, identification, and applications to genome project. Methods Enzymol. 1990, 183: 252-278.PubMedView ArticleGoogle Scholar
  122. Cartegni L, Chew SL, Krainer AR: Listening to silence and understanding nonsense: Exonic mutations that affect splicing. Nat Rev Genet. 2002, 3: 285-298. 10.1038/nrg775.PubMedView ArticleGoogle Scholar
  123. Liu HX, Zhang M, Krainer AR: Identification of functional exonic splicing enhancer motifs recognized by individual SR proteins. Genes Dev. 1998, 12: 1998-2012. 10.1101/gad.12.13.1998.PubMed CentralPubMedView ArticleGoogle Scholar
  124. Schaal TD, Maniatis T: Multiple distinct splicing enhancers in the protein-coding sequences of a constitutively spliced pre-mRNA. Mol Cell Biol. 1999, 19: 261-273.PubMed CentralPubMedView ArticleGoogle Scholar
  125. Zhang XH, Chasin LA: Computational definition of sequence motifs governing constitutive exon splicing. Genes Dev. 2004, 18: 1241-1250. 10.1101/gad.1195304.PubMed CentralPubMedView ArticleGoogle Scholar
  126. Fairbrother WG, Yeh RF, Sharp PA, Burge CB: Predictive identification of exonic splicing enhancers in human genes. Science. 2002, 297: 1007-1113. 10.1126/science.1073774.PubMedView ArticleGoogle Scholar
  127. Smale ST, Kadonaga JT: The RNA polymerase II core promoter. Annu Rev Biochem. 2003, 72: 449-479. 10.1146/annurev.biochem.72.121801.161520.PubMedView ArticleGoogle Scholar
  128. Callahan III, Balbinder E: Tryptophan operon: Structural gene mutation creating a 'promoter' and leading to 5-methyltryptophan dependence. Science. 1970, 168: 1586-1589. 10.1126/science.168.3939.1586.PubMedView ArticleGoogle Scholar
  129. Roberts JW: Promoter mutation in vitro. Nature. 1969, 223: 480-482. 10.1038/223480a0.PubMedView ArticleGoogle Scholar
  130. Kulozik AE, Bellan-Koch A, Bail S, et al: Thalassemia intermedia: Moderate reduction of beta globin gene transcriptional activity by a novel mutation of the proximal CACCC promoter element. Blood. 1991, 77: 2054-2058.PubMedGoogle Scholar
  131. Bosma PJ, Chowdhury JR, Bakkerm C, et al: The genetic basis of the reduced expression of bilirubin UDP-glucuronosyltransferase 1 in Gilbert's syndrome. N Engl J Med. 1995, 333: 1171-1175. 10.1056/NEJM199511023331802.PubMedView ArticleGoogle Scholar
  132. Trinklein ND, Aldred SJ, Saldanha AJ, Myers RM: Identification and functional analysis of human transcriptional promoters. Genome Res. 2003, 13: 308-312. 10.1101/gr.794803.PubMed CentralPubMedView ArticleGoogle Scholar
  133. Imanishi T, Itoh T, Suzuki Y, et al: Integrative annotation of 21,037 human genes validated by full-length cDNA clones. PLoS Biol. 2004, 2: e162-10.1371/journal.pbio.0020162.PubMed CentralPubMedView ArticleGoogle Scholar
  134. Suzuki Y, Yamashita R, Sugano S, Nakai K: DBTSS, DataBase of Transcriptional Start Sites: Progress report 2004. Nucleic Acids Res. 2004, 32: D78-D81. 10.1093/nar/gkh076.PubMed CentralPubMedView ArticleGoogle Scholar
  135. Suzuki Y, Yamashita R, Shirota M, et al: Large-scale collection and characterization of promoters of human and mouse genes. In Silico Biol. 2004, 4: 429-444.PubMedGoogle Scholar
  136. Rodriguez-Jato S, Nicholls RD, Driscoll DJ, Yang TP: Characterization of cis- and trans-acting elements in the imprinted human SNURF-SNRPN locus. Nucleic Acids Res. 2005, 33: 4740-4753. 10.1093/nar/gki786.PubMed CentralPubMedView ArticleGoogle Scholar
  137. Lettice LA, Heaney SJ, Purdie LA, et al: A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyl. Hum Mol Genet. 2003, 12: 1725-1735. 10.1093/hmg/ddg180.PubMedView ArticleGoogle Scholar
  138. The ENCODE (ENCyclopedia Of DNA Elements) Project: Science. 2004, 306: 636-640.View ArticleGoogle Scholar
  139. Kolbe D, Taylor J, Elnitski L, et al: Regulatory potential scores from genome-wide three-way alignments of human, mouse, and rat. Genome Res. 2004, 14: 700-707. 10.1101/gr.1976004.PubMed CentralPubMedView ArticleGoogle Scholar
  140. Elnitski L, Hardison RC, Li J, et al: Distinguishing regulatory DNA from neutral sites. Genome Res. 2003, 13: 64-72. 10.1101/gr.817703.PubMed CentralPubMedView ArticleGoogle Scholar
  141. Woolfe A, Goodson M, Goode DK, et al: Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biol. 2005, 3: e7-10.1371/journal.pbio.0030007.PubMed CentralPubMedView ArticleGoogle Scholar
  142. Dermitzakis ET, Reymond A, Lyle R, et al: Numerous potentially functional but non-genic conserved sequences on human chromosome 21. Nature. 2002, 420: 578-582. 10.1038/nature01251.PubMedView ArticleGoogle Scholar
  143. Cooper GM, Stone EA, Asimenos G, et al: Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005, 15: 901-913. 10.1101/gr.3577405.PubMed CentralPubMedView ArticleGoogle Scholar
  144. Dermitzakis ET, Reymond A, Antonarakis SE: Conserved non-genic sequences -- An unexpected feature of mammalian genomes. Nat Rev Genet. 2005, 6: 151-157.PubMedView ArticleGoogle Scholar
  145. Margulies EH, Blanchette M, Haussler D, Green ED: Identification and characterization of multi-species conserved sequences. Genome Res. 2003, 13: 2507-2518. 10.1101/gr.1602203.PubMed CentralPubMedView ArticleGoogle Scholar
  146. Boffelli D, McAuliffe J, Ovcharenko D, et al: Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science. 2003, 299: 1391-1394. 10.1126/science.1081331.PubMedView ArticleGoogle Scholar
  147. Frazer KA, Tao H, Osoegawa K, et al: Noncoding sequences conserved in a limited number of mammals in the SIM2 interval are frequently functional. Genome Res. 2004, 14: 367-372. 10.1101/gr.1961204.PubMed CentralPubMedView ArticleGoogle Scholar
  148. Pennacchio LA, Rubin EM: Genomic strategies to identify mammalian regulatory sequences. Nat Rev Genet. 2001, 2: 100-109. 10.1038/35052548.PubMedView ArticleGoogle Scholar
  149. Hardison RC: Comparative genomics. PLoS Biol. 2003, 1: E58-PubMed CentralPubMedView ArticleGoogle Scholar
  150. Culi J, Modolell J: Proneural gene self-stimulation in neural precursors: An essential mechanism for sense organ development that is regulated by Notch signaling. Genes Dev. 1998, 12: 2036-2047. 10.1101/gad.12.13.2036.PubMed CentralPubMedView ArticleGoogle Scholar
  151. Renucci A, Zappavigna V, Zàkàny J, et al: Comparison of mouse and human HOX-4 complexes defines conserved sequences involved in the regulation of Hox-4.4. EMBO J. 1992, 11: 1459-1468.PubMed CentralPubMedGoogle Scholar
  152. Loots GG, Locksley RM, Blankespoor CM, et al: Identification of a coordinate regulator of interleukins 4, 13, and 5 by cross-species sequence comparisons. Science. 2000, 288: 136-140. 10.1126/science.288.5463.136.PubMedView ArticleGoogle Scholar
  153. Poulin F, Nobrega MA, Plajzer-Frick I, et al: In vivo characterization of a vertebrate ultraconserved enhancer. Genomics. 2005, 85: 774-781. 10.1016/j.ygeno.2005.03.003.PubMedView ArticleGoogle Scholar
  154. Nobrega MA, Ovcharenko I, Afzal V, Rubin EM: Scanning human gene deserts for long-range enhancers. Science. 2003, 302: 413-10.1126/science.1088328.PubMedView ArticleGoogle Scholar
  155. Kimura-Yoshida C, Kitajima K, Oda-Ishii I, et al: Characterization of the pufferfish Otx2 cis-regulators reveals evolutionarily conserved genetic mechanisms for vertebrate head specification. Development. 2004, 131: 57-71. 10.1242/dev.00877.PubMedView ArticleGoogle Scholar
  156. Uchikawa M, Takemoto T, Kamachi Y, Kondoh H: Efficient identification of regulatory sequences in the chicken genome by a powerful combination of embryo electroporation and genome comparison. Mech Dev. 2004, 121: 1145-1158. 10.1016/j.mod.2004.05.009.PubMedView ArticleGoogle Scholar
  157. Ganley AR, Hayashi K, Horiuchi T, Kobayashi T: Identifying gene-independent noncoding functional elements in the yeast ribosomal DNA by phylogenetic footprinting. Proc Natl Acad Sci USA. 2005, 102: 11787-11792. 10.1073/pnas.0504905102.PubMed CentralPubMedView ArticleGoogle Scholar
  158. Xie X, Lu J, Kulbokas EJ, et al: Systematic discovery of regulatory motifs in human promoters and 3'UTRs by comparison of several mammals. Nature. 2005, 434: 338-345. 10.1038/nature03441.PubMed CentralPubMedView ArticleGoogle Scholar
  159. Glazko GV, Koonin EV, Rogozin IB, Shabalina SA: A significant fraction of conserved noncoding DNA in human and mouse consists of predicted matrix attachment regions. Trends Genet. 2003, 19: 119-124. 10.1016/S0168-9525(03)00016-7.PubMedView ArticleGoogle Scholar
  160. Drake JA, Bird C, Nemesh J, et al: Conserved noncoding sequences are selectively constrained and not mutation cold spots. Nat Genet. 2006, 38: 223-227. 10.1038/ng1710.PubMedView ArticleGoogle Scholar
  161. Altshuler D, Brooks LD, Chakravarti A, et al: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226.View ArticleGoogle Scholar
  162. Boffelli D, Nobrega MA, Rubin EM: Comparative genomics at the vertebrate extremes. Nat Rev Genet. 2004, 5: 456-465. 10.1038/nrg1350.PubMedView ArticleGoogle Scholar
  163. Clark AG, Glanowski S, Nielsen R, et al: Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science. 2003, 302: 1960-1963. 10.1126/science.1088821.PubMedView ArticleGoogle Scholar
  164. Gilad Y, Bustamante CD, Lancet D, Paabo S: Natural selection on the olfactory receptor gene family in humans and chimpanzees. Am J Hum Genet. 2003, 73: 489-501. 10.1086/378132.PubMed CentralPubMedView ArticleGoogle Scholar
  165. Kellis M, Patterson N, Endrizzi M, et al: Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature. 2003, 423: 241-254. 10.1038/nature01644.PubMedView ArticleGoogle Scholar
  166. Gibbs RA, Weinstock GM, Metzker ML, et al: Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature. 2004, 428: 493-521.PubMedView ArticleGoogle Scholar
  167. Kruglyak L, Nickerson DA: Variation is the spice of life. Nat Genet. 2001, 27: 234-236. 10.1038/85776.PubMedView ArticleGoogle Scholar
  168. The International Consortium: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226.View ArticleGoogle Scholar
  169. Matsuzaki H, Dong S, Loi H, et al: Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat Methods. 2004, 1: 109-111. 10.1038/nmeth718.PubMedView ArticleGoogle Scholar
  170. Fakhrai-Rad H, Zheng J, Willis TD, et al: SNP discovery in pooled samples with mismatch repair detection. Genome Res. 2004, 14: 1404-1412. 10.1101/gr.2373904.PubMed CentralPubMedView ArticleGoogle Scholar

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