Skip to main content
American Journal of Human Genetics logoLink to American Journal of Human Genetics
. 2004 Nov 1;76(1):147–151. doi: 10.1086/426734

Mapping of a Major Locus that Determines Telomere Length in Humans

Mariuca Vasa-Nicotera 1,,*, Scott Brouilette 1,,*, Massimo Mangino 1, John R Thompson 2, Peter Braund 1, Jenny-Rebecca Clemitson 1, Andrea Mason 1, Clare L Bodycote 1, Stuart M Raleigh 1, Edward Louis 3, Nilesh J Samani 1
PMCID: PMC1196417  PMID: 15520935

Abstract

Telomere length is a crucial factor for both normal chromosomal function and senescence. Mean telomere length in humans shows considerable interindividual variation and strong genetic determination. To see if a locus (or loci) affecting telomere length in humans could be mapped, we performed a quantitative-trait linkage analysis of mean leukocyte telomere-restriction–fragment (TRF) lengths, measured by Southern blotting, in 383 adult subjects comprising 258 sib pairs. Heritability of mean (±SE) TRF was 81.9%±11.8%. There was significant linkage (LOD score 3.20) of mean TRF length to a locus on chromosome 12, which explained 49% of the overall variability in mean TRF length. We present preliminary analysis of a strong candidate gene in the region, the DNA helicase DDX11. In conclusion, we report mapping of the first locus that determines mean telomere length in humans. Identification of the gene involved and elucidation of its mechanism of action could have important implications for our understanding of chromosomal assembly, telomere biology, and susceptibility to age-related diseases.


Telomeres are special functional complexes, at the ends of eukaryotic chromosomes, involved in maintaining genetic stability and in regulation of cellular life span (Blackburn 2001; Blasco 2003). Telomere homeostasis is relevant to normal aging and a wide range of disease states, including cancers and age-related disorders (Blackburn 2001; Blasco 2003). Telomeres are made up of a variable number of tandem repeats (TTTAGGG in humans) that extend over several thousand base pairs. Telomere lengths are characteristic in each human individual (Takubo et al. 2002) but show wide interindividual variability (Slagboom et al. 1994; Jeanclos et al. 2000; Takubo et al. 2002). Heritability of mean telomere length in leukocytes has been estimated at 78%–84%, on the basis of twin and family studies (Slagboom et al. 1994; Jeanclos et al. 2000). In proliferation of somatic cells that lack telomerase, telomeres progressively shorten because of the end-replication problem of linear DNA molecules; telomere length has emerged as an important determinant of replicative senescence and cell fate (Allsopp et al. 1992), possibly by its effect on telomere capping (Blackburn 2000). Telomere length therefore reflects aging, and shorter telomeres have been associated with a variety of age-related diseases (Blackburn 2001; Blasco 2003; Serrano and Andres 2004). Although a locus that determines telomere length in some mouse strains has been mapped to chromosome 2 (Zhou et al. 2000), the nature of the genetic determinants of telomere length in humans remains unclear.

To see if a locus (or loci) affecting telomere length in humans could be mapped, we performed a quantitative-trait linkage analysis of mean leukocyte telomere-restriction–fragment (TRF) lengths, measured by Southern blotting, of 383 adult subjects (291 males, 92 females) from 173 families comprising 258 sib pairs (table A1 [online only]). The mean age (±SD) of the subjects was 65.8±6.4 years (range 47–82 years). As was found elsewhere (Benetos et al. 2001; Brouilette et al. 2003; Nawrot et al. 2004), there was an age-related decrease in mean TRF length (±SE) in both men (29.9±5.6 bp/year) and women (16.8±9.9 bp/year) (fig. 1A). The difference in rate of decline between the sexes was not significant (P=.61). However, consistent with previous data (Benetos et al. 2001; Nawrot et al. 2004), the mean age-adjusted TRF length in women was shorter than in men (difference [±SE] 271.5±64.7 bp; P<.001). There was a highly significant intersibling correlation in TRF length (fig. 1B). Under the assumption that the entire familial resemblance in telomere length is genetic, a heritability index (h2) (±SE) of 81.9%±11.8% was obtained for mean TRF length. This is similar to previous estimates (Slagboom et al. 1994; Jeanclos et al. 2000). Age (P<.001) and sex (P<.001) were significant covariants, together accounting for 8.7% of the variance in the trait, and were included in the linkage-analysis model to allow an appropriate adjustment.

Figure 1.

Figure  1

Age relationship and intersibling correlations for mean TRF length. A, Each subject’s mean TRF length, plotted as a function of the subject's age. Note the high interindividual variability in mean TRF length at any age. The regression lines show the mean decrease in TRF length with age of females (solid lines) and of males (dotted lines). The coefficient of variation in males was 9.6% and in women was 8.8%. B, The correlation in mean TRF length (unadjusted) between sib pairs.

An initial genome scan with 400 microsatellite markers—at intervals of ∼10 cM (ABI-Prism Linkage Mapping set, MD-10 panels version 2.5), with an average heterozygosity of 0.79—identified significant linkage to chromosome 12 (fig. 2A). A maximum two-point LOD score of 3.21 was obtained for marker D12S345, by use of the Sequential Oligogenic Linkage Analysis Routines (SOLAR) package. Four additional markers in the region were typed to confirm linkage and for fine mapping of the genetic interval. Marker D12S168 showed a LOD score of 3.03, and multipoint analysis showed a maximum LOD score of 3.20 between markers D12S1640 and D12S1589 (fig. 2B). Analysis by use of the Multipoint Engine for Rapid Likelihood Inference (MERLIN) program gave similar results (LOD score 3.07, P=.00008 at marker D12S345; LOD score 3.04, P=.00009 at marker D12S1698). Using MERLIN, we made 1,500 simulated genome scans under the same conditions used during the analysis (family structure, phenotype, marker spacing, allele frequencies, and missing-data patterns). Sixty-seven simulations gave two consecutive markers with a LOD score >3.0. This equates to a genomewide P value of .044 for our finding. Heritability analysis showed that 49% of the total interindividual variability of mean TRF length could be attributed to the locus on chromosome 12. For the remainder of the heritable influence on mean TRF length, the likely scenario is that this influence is the result of several genes with smaller effects, although our findings cannot exclude the possibility that another major locus is responsible. We observed peaks with LOD scores of 1–1.5 on chromosomes 2, 9, and 13 (fig. 2A) that could harbor such loci, but larger studies are necessary to confirm this. Our findings, however, do not support the recent proposal—which is based on patterns of correlations within nuclear families—that the majority of inheritance of telomere length is X linked (Nawrot et al. 2004).

Figure 2.

Figure  2

Results of genome-scan analysis for mean TRF length. A, The peaks and corresponding LOD scores obtained on each chromosome from multipoint linkage analysis performed using SOLAR (see the “Methods” section of appendix A [online only] for details). B, A more detailed representation of the linkage observed on chromosome 12, with results from the additional markers typed in the region of the observed peak.

A feature of our study that requires comment is the measurement of mean TRF length in sib pairs with coronary heart disease (CHD) (see the “Methods” section of appendix A [online only]). This choice was pragmatic: the initial genome-scan data were already available for these subjects. Because we (Brouilette et al. 2003) and others (Cawthon et al. 2003) have found an association between shorter telomeres and risk of CHD, the question arises as to whether this association could have affected our findings. We believe that this scenario is unlikely, because the subjects in the study form a relatively homogeneous sample of patients with CHD. Selection into the study from within the population of patients with CHD is unlikely to be related either to telomere length or to genotype. Furthermore, multipoint linkage analysis within the sample with CHD as the phenotype showed no evidence of significant or even suggestive linkage for any of the telomere-length QTL peaks. Therefore, although the findings need to be confirmed in patients unaffected by CHD, there is every reason to suppose that our findings would generally apply

According to the Ensembl database, the 1-LOD interval for the locus on chromosome 12 spans 13.2 Mb and contains 34 genes (42, if isoforms and other predicted transcripts are included) (table A2 [online only]). Among these 34, the DNA helicase DDX11 (MIM 601150) immediately emerged as a strong positional candidate to explain the effect on telomere length. Helicases unwind double-stranded DNA and RNA and are involved in a wide range of chromosome-related functions, including transcription, replication, segregation, and DNA repair. Many helicases have roles in maintenance of telomeres and in telomere-length control. These include the RecQ helicase involved in Werner syndrome in humans, a disorder associated with premature aging (Yu et al. 1996), and the Sgs1 and Pif1 helicases in yeast (Zhou et al. 2000). The yeast homologue of DDX11, Chl1, is involved in chromosome transmission and normal cell-cycle progression, with mutants exhibiting a senescent phenotype (Amann et al. 1997). The DDX11 gene spans 31 kb and comprises 27 exons. The gene is highly polymorphic, with >350 SNPs already described in the SNP Consortium Database. Analysis of SNPs in the region typed in the HapMap Project suggested that the DDX11 gene lies within a 61-kb linkage-disequilibrium block with two major haplotypes (see the “Methods” section of appendix A [online only]). In a preliminary analysis, we examined the association of five SNPs that spanned the entire DDX11 gene (including three tag SNPs for the common haplotypes and two additional SNPs to cover the 3′ end of the gene) (table A3 [online only]) with mean TRF length in our subjects. To gather data from independent chromosomes for this analysis, one individual randomly selected from each family (n=173) was initially studied. The results confirmed that the DDX11 gene is in a linkage-disequilibrium block. The block is defined by two principal haplotypes—hap A: AGGCA (frequency 0.445) and hap B: TCATG (frequency 0.536)—at the SNPs shown in table A3 (online only). However, the mean TRF length, adjusted for age and sex, was not significantly different between the DDX11 genotype groups defined by the two haplotypes (AA: 6.47±0.50 kb [±SD]; AB: 6.48±0.60 kb; BB: 6.60±0.61 kb; P=.43). To include all the information available, we also genotyped the remainder of the cohort and analyzed the full data (n=383) with a random-effects model, taking familial correlations into account. This analysis was also nonsignificant (P=.28). These findings suggest that the DDX11 haplotypes defined by these SNPs do not have a large effect on mean TRF length nor solely explain the linkage observed. However, the results do not exclude an involvement of DDX11 in telomere-length determination, which will require a more detailed determination of the extent of variation in the gene. This work is currently ongoing, as is the study of other possible candidate genes in the region.

Acknowledgments

This study was supported by grants from the British Heart Foundation and the Sir Jules Thorn Charitable Trust.

Appendix A

Methods

Subjects

Families were selected from those participating in the British Heart Foundation Family Heart Study. This is a large two-center study (Leicester and Leeds) of >2,000 white families recruited from throughout the United Kingdom, the primary objective of which is to map loci predisposing to premature CHD. Families analyzed for telomere length were chosen randomly from those recruited by the Leicester center. Only sibships were studied; parental DNA was not available.

Telomere-length measurement

Measurement of mean leukocyte-telomere TRF length was performed by Southern blotting by use of a standard approach described elsewhere (Allsopp et al. 1992; Slagboom et al. 1994; Jeanclos et al. 2000; Brouilette et al. 2003). Briefly, DNA was extracted from blood samples by use of the PureGene DNA Extraction Kit (Gentra Systems), and quality was assessed by agarose-gel electrophoresis. Aliquots of DNA (8 μg) were digested overnight at 37°C with 15 IU RsaI and HinfI (Invitrogen) and were quantified by fluorimetry. Then, 2 μg of each sample was resolved by electrophoresis on a 0.5% agarose gel (50 V; 18 h). Size standards were run on either side of the gel (500 ng Kb DNA [Invitrogen] and 700 ng Hyperladders I and VI [Bioline]). After transfer to Hybond-N membrane (Amersham Pharmacia Biotech) by Southern-blot technique, DNA samples were hybridized at 42°C for 2 h to a 3′-end–labeled 32P-(AATCC)3 oligonucleotide telomere probe and to randomly primed labeled Kb DNA ladder (30 ng) and Hyperladders I and VI (45 ng). After washes in 3× standard saline citrate/0.1% sodium dodecyl sulfate at room temperature, telomere smears were visualized by exposure to autoradiographic film (Kodak Biomax-MR [Eastman Kodak]) and were digitized by use of a Phospho-imager (Molecular Dynamics). IMAGEQUANT software (Molecular Dynamics) was used to analyze telomere smears, by drawing a grid object (1 column ×30 rows) over each lane, from 24 to 2.5 kb. The mean size of the TRF was estimated using the formula TRF=[(ODi)/(ODi/MWti)] , where ODi is the optical density at a given position and MWti is the mean molecular weight at the same position. A single control sample was run on each gel, to adjust for intergel variability. The interassay variance in estimate of mean TRF length (±SE) was calculated from repeat analysis of 50 samples selected at random and was 3.3%±2.7%.

Genotyping for linkage analysis

In the first phase of the genome scan, 400 microsatellite markers from the ABI-Prism Linkage Mapping set v2.5-MD10 (PE Applied Biosystems), spaced at ∼10 cM and with an average heterozygosity of 0.79, were analyzed. PCR was performed under conditions described by the manufacturer, and the products were pooled in panels and were analyzed on an ABI-PRISM 3700 DNA sequencer. Genotypes from each marker were examined by the GeneMapper software v2.0 (PE Applied Biosystems). In the second phase, an additional four markers were analyzed to refine the chromosome 12 linkage region. Two markers (D12S1640 and D12S1663) were selected from ABI-Prism Linkage Mapping set v2.5-HD5 (PE Applied Biosystems), and two markers (D12S1698 and D12S1589) were selected from the National Center for Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unsts).

Quality control of genotype data

All genotypes, including those that passed GeneMapper’s internal quality control, were manually read by at least one individual; those genotypes that did not fully pass the program's quality control were read by two independent individuals. Unless there was complete agreement on allele calls, genotypes were rejected and the samples rerun. RELATIVE (Goring and Ott 1997) and GRR (Abecasis et al. 2001) programs were used to confirm family relationships. Inheritance within families was verified using the PEDCHECK program (O’Connell and Weeks 1998). If there was a likelihood of inheritance errors, the complete family was genotyped again for the marker in question.

Analysis of DDX11

The structure of the DDX11 gene was obtained from the Human Genome Mapping Project by use of Ensembl and information on SNPs in the gene gathered from the SNP Consortium Database and the HapMap Project. We selected all the SNPs genotyped from 90 CEPH pedigrees (Ke et al. 2004) that were present in the 100-Mb region of the DDX11 gene and that had a minor-allele frequency of 0.2. We used Haploview (version 2.03) to generate haplotypes, haplotypes frequencies, and linkage-disequilibrium blocks in the region. We defined three tag SNPs to analyze the DDX11 gene. Two additional SNPs were selected to cover the 3′ end of the gene. The analysis was then repeated in our samples for the five SNPs identified. Genotyping of the SNPs chosen for analysis (table A3) was performed by allelic discrimination through use of TaqMan genotyping technology (Livak 1999). Briefly, one allelic probe was labeled with the fluorescent FAM dye, and the other was labeled with the fluorescent VIC dye. PCRs were set up under conditions described by the manufacturers. The TaqMan assay plates were transferred from the thermal cyclers to the ABI PRISM 7900HT Sequence Detection System (SDS) (Applied Biosystems), in which the fluorescence intensity in each well of the plate was read. Fluorescence data files from each plate were analyzed by automated allele-calling software of the instrument (SDS v2.1) and were reviewed by an operator. Allele frequencies were determined from each SNP, and genotypes were checked for deviation from Hardy-Weinberg equilibrium.

Statistical analysis

Summary statistics on the pattern of change in mean TRF length by age of men and women were obtained from random-effects regressions that allowed for correlation within families.

Two-point and multipoint quantitative-trait linkage analyses were conducted by use of the SOLAR package (version 1.7.3) (Almasy and Blangero 1998). Files were converted to the SOLAR format by use of MEGA2 (version 2.5R2) (Mukhopadhyay et al. 1999). Allele frequencies were calculated from the observed genotypes. The order of the marker loci and their recombination distances used for multipoint linkage analysis were based on the deCODE map (Kong et al. 2002), supplemented with data from NCBI.

Linkage of variance components was assessed by fitting a polygenic model that does not incorporate genetic marker information and by comparison of that model with models that incorporate genotype data at a specific marker (two-point analysis) or across a chromosome (multipoint analysis). To adjust for age, for sex, and for age and sex combined, these factors were initially included as covariates in the linkage analysis. Age and sex were found to be significant and were retained in the linkage-analysis model. Heritability values were obtained after adjustment for covariates. The data were also analyzed using the variance-components function in the MERLIN program (Abecasis et al. 2002), after prior adjustment for age and sex. Simulations were conducted in MERLIN by use of the same conditions of the main QTL analyses with the option “simulate.”

The association of the common DDX11 haplotypes with mean TRF length was performed using random-effects analysis of variance, adjusting for age, sex, and familial correlations, as described.

Table A1.

Details of Sib-Pair Study

Subjects Analyzed n
No. of individuals:
 Total 383
 Males 291
 Females 92
No. of families:
 Total 173
 With two sib pairs 141
 With three sib pairs 25
 With four sib pairs 5
 With five sib pairs 2
No. of sib pairs:
 Total 258
 Male-male 154
 Female-female 24
 Mixed 80
Table A2.

Genes within the 1-LOD Drop Region of the Chromosome 12p11.2-q12 Linkage Peak

Gene IDa Description
PPFIBP1 PTPRF-interacting protein-binding protein 1 isoform 1
MRPS35 Mitochondrial ribosomal protein S35
YD40_Human Hypothetical protein YD40_Human
PTHLH Parathyroid hormone-related protein precursor
NM_018318 Hypothetical protein NM_018318
NM_018099 Hypothetical protein NM_018099
NM_016570 Hypothetical protein similar to PTX1
NM_175861 Hypothetical protein similar to ARG99 protein
IPO8 Importin 8
C1QDC1 C1Q domain containing 1 isoform L
DDX11 DEAD/H box polypeptide 11; yeast Chl1 homologue
Q9BZ57 Hypothetical protein Q9BZ57
NM_021238 Hypothetical protein similar to TERA protein
NM_024799 Hypothetical protein NM_024799
NM_144973 Hypothetical protein NM_144973
AK3 Adenylate kinase isoenzyme 4
NM_173802 Hypothetical protein NM_173802
Q86X98 Hypothetical protein Q86X98
Q16776 Gene fragment for histone H3
NM_018169 Hypothetical protein NM_018169
BICD1 Bicaudal D homologue 1
NM_139241 Actin-filament–binding protein frabin
DNM1L Dynamin 1–like protein isoform 3; dynamin-like protein
NM_015936 Hypothetical protein NM_015936
PKP2 Plakophilin 2
NM_032834 Hypothetical protein NM_032834
NM_153634 Hypothetical protein similar to COPINE VIII
KIF21A Ny-Ren-62 antigen
ABCD2 ATP-binding cassette D2
NM_173599 Hypothetical protein NM_173599
SLC2A13 Proton myo-inositol cotransporter (HMIT)
Q8NCX9 Hypothetical protein Q8NCX9
CNTN1 Contactin precursor
NM_013377 Hypothetical protein NM_013377
a

GenBank accession numbers (http://www.ncbi.nlm.nih.gov/Genbank/).

Table A3.

SNPs Analyzed in the DDX11 Gene

dbSNPa Alleles Chromosome Position MAFb
rs7953706 A/T 31118102 .462
rs1808348 C/G 31136113 .467
rs2075321 A/G 31137008 .459
rs9788047 T/C 31140492 .453
rs9750 A/G 31148731 .467
b

MAF = minor-allele frequency.

Electronic-Database Information

The URLs for data presented herein are as follows:

  1. Ensembl, http://www.ensembl.org/ (for genetic distance between markers and identification of genes in the interval of interest)
  2. HapMap Project, http://www.hapmap.org/ (for SNP information)
  3. Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/ (for DDX11) [PubMed]
  4. SNP Consortium Database, http://snp.cshl.org (for SNP information)

References

  1. Allsopp RC, Vaziri H, Patterson C, Goldstein S, Younglai EV, Futcher AB, Greider CW, Harley CB (1992) Telomere length predicts replicative capacity of human fibroblasts. Proc Natl Acad Sci USA 89:10114–10118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Amann J, Kidd VJ, Lahti JM (1997) Characterization of putative human homologues of the yeast chromosome transmission fidelity gene, CHL1. J Biol Chem 272:3823–3832 10.1074/jbc.272.6.3823 [DOI] [PubMed] [Google Scholar]
  3. Benetos A, Okuda K, Lajemi M, Kimura M, Thomas F, Skurnick J, Labat C, Bean K, Aviv A (2001) Telomere length as an indicator of biological aging: the gender effect and relation with pulse pressure and pulse wave velocity. Hypertension 37:381–385 [DOI] [PubMed] [Google Scholar]
  4. Blackburn EH (2000) Telomere states and cell fates. Nature 408:53–56 10.1038/35040500 [DOI] [PubMed] [Google Scholar]
  5. ——— (2001) Switching and signaling at the telomere. Cell 106:661–673 10.1016/S0092-8674(01)00492-5 [DOI] [PubMed] [Google Scholar]
  6. Blasco MA (2003) Mammalian telomeres and telomerase: why they matter for cancer and aging. Eur J Cell Biol 82:441–446 [DOI] [PubMed] [Google Scholar]
  7. Brouilette S, Singh RK, Thompson JR, Goodall AH, Samani NJ (2003) White cell telomere length and risk of premature myocardial infarction. Arterioscler Thromb Vasc Biol 23:842–846 10.1161/01.ATV.0000067426.96344.32 [DOI] [PubMed] [Google Scholar]
  8. Cawthon RM, Smith KR, O’Brien E, Sivatchenko A, Kerber RA (2003) Association between telomere length in blood and mortality in people aged 60 years or older. Lancet 361:393–395 10.1016/S0140-6736(03)12384-7 [DOI] [PubMed] [Google Scholar]
  9. Jeanclos E, Schork NJ, Kyvik KO, Kimura M, Skurnick JH, Aviv A (2000) Telomere length inversely correlates with pulse pressure and is highly familial. Hypertension 36:195–200 [DOI] [PubMed] [Google Scholar]
  10. Nawrot TS, Staessen JA, Gardner JP, Aviv A (2004) Telomere length and possible link to X chromosome. Lancet 363:507–510 10.1016/S0140-6736(04)15535-9 [DOI] [PubMed] [Google Scholar]
  11. Serrano AL, Andres V (2004) Telomeres and cardiovascular disease: does size matter? Circ Res 94:575–584 10.1161/01.RES.0000122141.18795.9C [DOI] [PubMed] [Google Scholar]
  12. Slagboom PE, Droog S, Boomsma DI (1994) Genetic determination of telomere size in humans: a twin study of three age groups. Am J Hum Genet 55:876–882 [PMC free article] [PubMed] [Google Scholar]
  13. Takubo K, Izumiyama-Shimomura N, Honma N, Sawabe M, Arai T, Kato M, Oshimura M, Nakamura K (2002) Telomere lengths are characteristic in each human individual. Exp Gerontol 37:523–531 10.1016/S0531-5565(01)00218-2 [DOI] [PubMed] [Google Scholar]
  14. Yu CE, Oshima J, Fu YH, Wijsman EM, Hisama F, Alisch R, Matthews S, Nakura J, Miki T, Ouais S, Martin GM, Mulligan J, Schellenberg GD (1996) Positional cloning of the Werner’s syndrome gene. Science 272:258–262 [DOI] [PubMed] [Google Scholar]
  15. Zhou J, Monson EK, Teng S, Schulz VP, Zakian VA (2000) Pif1p helicase, a catalytic inhibitor of telomerase in yeast. Science 289:771–774 10.1126/science.289.5480.771 [DOI] [PubMed] [Google Scholar]

Supplemental References

  1. Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2001) GRR: graphical representation of relationship errors. Bioinformatics 17:742–743 10.1093/bioinformatics/17.8.742 [DOI] [PubMed] [Google Scholar]
  2. ——— (2002) Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:97–101 10.1038/ng786 [DOI] [PubMed] [Google Scholar]
  3. Almasy L, Blangero J (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Goring HH, Ott J (1997) Relationship estimation in affected sib pair analysis of late-onset diseases. Eur J Hum Genet 5:69–77 [PubMed] [Google Scholar]
  5. Ke X, Hunt S, Tapper W, Lawrence R, Stavrides G, Ghori J, Whittaker P, Collins A, Morris AP, Bentley D, Cardon LR, Deloukas P (2004) The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum Mol Genet 13:577–588 10.1093/hmg/ddh060 [DOI] [PubMed] [Google Scholar]
  6. Kong A, Gudbjartsson DF, Sainz J, Jonsdottir GM, Gudjonsson SA, Richardsson B, Sigurdardottir S, Barnard J, Hallbeck B, Masson G, Shlien A, Palsson ST, Frigge ML, Thorgeirsson TE, Gulcher JR, Stefansson K (2002) A high-resolution recombination map of the human genome. Nat Genet 31:241–247 [DOI] [PubMed] [Google Scholar]
  7. Livak KJ (1999) Allelic discrimination using fluorogenic probes and the 5′ nuclease assay. Genet Anal 14:143–149 [DOI] [PubMed] [Google Scholar]
  8. Mukhopadhyay N, Almasy L, Schroeder M, Mulvihill WP, Weeks DE (1999) Mega2, a data-handling program for facilitating genetic linkage and association analyses. Am J Hum Genet Suppl 65:A436 [DOI] [PubMed] [Google Scholar]
  9. O’Connell JR, Weeks DE (1998) PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 63:259–266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Slagboom PE, Droog S, Boomsma DI (1994) Genetic determination of telomere size in humans: a twin study of three age groups. Am J Hum Genet 55:876–882 [PMC free article] [PubMed] [Google Scholar]

Articles from American Journal of Human Genetics are provided here courtesy of American Society of Human Genetics

RESOURCES