Chapter 6. What Have we Learned from Genomewide Scans on Lipid-Related Phenotypes so Far? Fixing Perspective with a New Genomewide Search on Apo B and Apo AI Levels in the Québec Family Study.

Yohan Bossé, Yvon C Chagnon, Jean-Pierre Després, Treva Rice, D.C. Rao, Claude Bouchard, Louis Pérusse, Marie-Claude Vohl.

Un grand nombre de criblages génomiques sur les variables lipidiques ont été rapportés dans la littérature. À cet effet, nous avons créé une banque de données contenant les résultats des criblages génomiques effectués jusqu’à ce jour. Cette synthèse va permettre aux investigateurs de positionner leurs prochains résultats sans être obligés de digérer la grande quantité d’articles scientifiques. L’utilité de cette banque de données a ensuite été démontrée avec un nouveau criblage génomique sur les niveaux d’apolipoprotéine (apo) B, d’apoB-LDL et d’apoAI, chez 679 sujets. Deux nouveaux loci ont été identifiés, soit le 18q21.32 et le 3p25.2 pour les niveaux d’apoB-LDL et d’apoAI, respectivement. La banque de données nous a permis de dévoiler que ce dernier est un nouveau locus relié aux lipides sanguins. Cet exercice nous a aussi permis de constater qu’une grande portion du génome est maintenant couverte avec des évidences de liaison.


What Have we Learned from Genomewide Scans on Lipid-Related Phenotypes so Far? Fixing Perspective with a New Genomewide Search on Apo B and Apo AI Levels in the Québec Family Study.

Yohan Bossé1,2, Yvon C. Chagnon3, Jean-Pierre Després1,2,4, Treva Rice5, D.C. Rao5, Claude Bouchard6, Louis Pérusse7, Marie-Claude Vohl1,2

1- Lipid Research Center, CHUL Research Center; 2- Department of Food Sciences and Nutrition; 3- Laval University Robert-Giffard Research Center, Beauport; 4- The Quebec Heart Institute; 5- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri; 6- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA; 7- Division of Kinesiology, Department of Social and Preventive Medicine, Laval University, Québec, Canada.

Short title: Genome Scans on Lipid-Related Phenotypes

Address all correspondence to:

Marie-Claude Vohl, Ph.D., Lipid Research Center, CHUL Research Center, TR-93, 2705, Boul. Laurier, Sainte-Foy, Quebec, G1V 4G2, Canada, Fax: (418) 654-2145; Tel: (418) 656-4141 extension 48280; E-mail:


The genetic dissection of complex inherited diseases is a major challenge. Although the success rate is highly limited in finding potentially relevant genes, a large body of data based on genomewide scan strategies is now available for a variety of diseases and related phenotypes. This can perhaps be best appreciated in the field of lipid and lipoprotein levels. The amount of information generated from genomewide scans on lipid-related phenotypes may soon overwhelm even the most dedicated investigators. We have created a database containing the results from whole-genome scans undertaken to date. This synthesis may be helpful to investigators in positioning new findings without having to digest a large body of scientific papers. The usefulness of this database is then demonstrated by performing a new autosomal genomic scan on apolipoprotein (apo) B, LDL-apo B and apo AI levels, measured in 679 subjects of 243 nuclear families. Linkage was tested using both allele sharing and variance component methods. Only two loci provided support for linkage with both methods, including a LDL-apo B locus on 18q21.32 and an apo AI locus on 3p25.2. Adding those findings to the database highlighted the fact that the former is a first time reported lipid-related locus, whereas the later has been observed before. However, displaying all data on the same map revealed that a large portion of the genome is now covered with loci supported by at least suggestive evidence of linkage.

Keywords : Genome scans, lipid-related phenotypes, compendium, apolipoproteins, lipoproteins, quantitative trait locus, cardiovascular risk factors, linkage, dyslipidemia.


Mapping genes involved in complex human diseases is one of the major challenges in human genetics. With the increasing incidence of chronic diseases in industrialized societies, finding these genes is clinically and economically relevant. During the past few years, considerable research resources have been deployed to study the genetic etiology of complex human diseases in order to better understand their pathogenesis and, ultimately, improve prevention strategies, diagnostic tools, and therapies1. Enthused by the early success in the identification of genes responsible for monogenic diseases, many investigators have embraced genome scan strategies. This trend has resulted in an enormous amount of information, which is now typically difficult to synthesize and interpret for a given complex disease.

The importance ascribed to lipid and lipoprotein levels in risk estimation and in the treatment of CHD2 has stimulated molecular studies to investigate the genetic etiology underlying human variation in these traits. A large number of genomewide screens on serum lipid-related phenotypes have been performed to date and a review of such studies seems timely. Since linkage results must be replicated to be credible3, a compendium of published QTLs may facilitate the identification of replicated findings. To provide an example on how such information can be useful, we are adding herein the results of a new genome scan on apolipoprotein (apo) B and apo AI levels to this compendium.

Apo B and apo AI levels are good markers of CHD risk4,5. A number of studies have clearly established that genetic factors contribute to interindividual differences in apo levels. An elegant study comparing identical and fraternal twins reared together with twins reared apart has shown that a large portion of the variance in apo B and apo AI levels is attributable to genetic factors, with heritability estimates above 50%6. In addition, based on complex segregation analyses, major gene effects have been reported for these two phenotypes7,8. Mutations in genes that encode apo B, LDL receptor and ABCA1 have been implicated in monogenic disorders altering plasma apolipoprotein levels including familial hypobetalipoproteinemia (OMIM 605019), familial hypercholesterolemia (OMIM 143890) and hypoalphalipoproteinemia (OMIM 604091). However, these mutations do not account for the variation in plasma apo B and apo A levels in the general population. In an attempt to identify the responsible genes, a large number of association and linkage studies have been performed with candidate genes. However, these studies have been difficult to interpret due to conflicting results, lack of replication, and the occurrence of positive findings only in specific subgroups. Perhaps the highest linkage signal for apo B levels was reported in Dutch pedigrees on chromosome 1p31 (LOD = 4.7)9. Other suggestive linkages (LOD > 1.7) have been found on chromosome 12q24 for apo AI10 and on 1p, 11q24, 21q21 and Xq23 for apo B11,12. However, other genomewide scans failed to identify QTL for apo B levels10,13. To search for additional loci influencing apo B and apo AI levels or to replicate previous findings, we performed an autosomal genome scan among 243 nuclear families participating in the Québec Family Study.

Materials and Methods


Subjects were participants of the Québec Family Study (QFS) ― an ongoing project with French-Canadian families investigating the genetics of obesity and its comorbidities14. In this study, a total of 679 subjects of 243 nuclear families had apolipoprotein measurements available. This cohort represents a mixture of random sampling and ascertainment through obese (BMI > 32 kg/m2) probands. Table 1 presents the characteristics of subjects in each of the sex and generation groups. The study was approved by the Laval University Medical Ethics Commitee, and all subjects provided written informed consent. All procedures followed were in accordance with institutional guidelines.

Apolipoprotein measurements

Blood samples were obtained from an antecubital vein in the morning after a 12-hour overnight fast. The apo measurements were performed with the rocket immunoelectrophoretic method15. Apo B concentrations were measured in plasma whereas LDL apo B and apo AI concentrations were measured in the infranatant (d > 1.006 g/ml) obtained after separation of very-low density lipoprotein from the plasma by ultracentrifugation. The measurements were calibrated with reference standards obtained from the Center for Disease Control (Atlanta, GA, USA).

Linkage analysis

A total of 443 markers spanning the 22 autosomal chromosomes with an average intermarker distance of 7.2 centimorgans were genotyped as described in Chagnon et al.16. The apo traits were adjusted for the effects of age (up to cubic polynomial to allow for non-linearity), gender and body mass index (BMI) using a stepwise multiple regression procedure retaining only significant covariates (p < 0.05) as described previously17. Adjustments of the phenotypes were performed using SAS (version 8.2).

We conducted quantitative trait linkage analyses using both an allele sharing and a variance component methods. For the allele sharing method, we used the new Haseman-Elston regression-based method18 which models the mean-corrected cross product of the sibs’ trait values, instead of the squared sib pair trait difference used in the original method19. Two-point and multipoint (at 1 cM interval) estimates of alleles shared IBD were generated using the GENIBD software and linkage was tested using the SIBPAL2 software from the S.A.G.E. 4.0 statistical package20. The maximum number of sib pairs was 347. Empirical p values of the test statistic were also computed using a Monte Carlo permutation procedure with 10000 replicate permutations for genomic regions containing two-point linkage markers with suggestive evidence of linkage (p<0.0023). Linkage was also performed with a variance component model using the quantitative transmission disequilibrium test (QTDT) computer program21. Under this model, a phenotype is influenced by the additive effects of a QTL (q), a residual familial component due to polygenes (g) and a residual nonfamilial component (e). Hypothesis testing was performed by the likelihood ratio test. The likelihood of the null hypothesis is obtained by restricting the additive genetic variance due to the QTL (σq) equal to zero (σq = 0). The test is conducted by contrasting this restricted model with the alternative where σq is estimated (σq ≠ 0). The difference in minus twice the log-likelihoods between the null and alternate hypotheses is approximately distributed as a χ2 which allowed LOD score computation as χ2/(2 loge 10). We have taken a LOD score of ≥ 3.00 (p ≤ 0.0001) as evidence of linkage and a LOD of ≥ 1.75 (p ≤ 0.0023) as evidence of suggestive linkage22. We have also retained LOD scores ≥ 1.18 (p ≤ 0.01) to identify potential independent confirmation of a previously reported significant linkage23.


The initial search for genomewide scan publications on lipid-related phenotypes was accomplished with keywords (genome scan + lipoprotein and linkage + lipoprotein + genome) at the bioinformatic site of the National Center for Biotechnology Information ( The publication list was completed and verified by examination of both the discussion section and the reference list of the publication found in the initial search. The search focused on results published before the end of April 2003 and excluded abstract presented at meetings.

A whole-genome scan excel database for lipid-related phenotypes was established. The database contained bibliographic details (first author, source and years), study population (ethnicity), ascertainment scheme, phenotypic traits, sample-size details (number of individuals, sib pairs and families), linkage analysis methods, and results. Any evidence of linkage, from suggestive and better, (LOD score ≥ 1.7 or P value ≤ 0.0023) was treated as an observation (a hit). Results were entered in the database with the name of the linked marker/gene, its location (megabase and chromosomal band), and its maximum LOD score or Z score or P value. For most studies, markers were provided in the papers and were those defining the peak or were the closest to the signal. When the marker’s name or the specific location of the QTL (hits) was not available in the original manuscript, the authors were contacted and asked to provide the missing information. To identify possible replication and compared loci across studies, the location of each linked marker/gene was positioned on a single map provided by the human genome browser of the University of California, Santa Cruz (assembly, June 2002) ( When a two-stage strategy was reported in the publication, the lowest P value attained at any phase of the analytical strategy was considered. Similarly, when multiple linkage methods were used in the same publication, the most significant result was kept for the database.

To evaluate whether QTLs were randomly distributed across the genome, we regressed the observed hit ratio against the expected hit ratio as reported previously24. The observed hit ratio of each chromosome was obtained as: (number of hits on a specific chromosome / number of hits across all chromosomes) x 100, and the expected hit ratio of each chromosome was obtained as: (number of genes on a specific chromosome / total number of genes in the genome) x 100. The gene content of each chromosome and for the whole genome are from Venter et al.25. A significant association (positive slope) between the observed and expected hit ratio would suggest that the positive linkage reported in the literature are distributed randomly across the genome. In contrast, if the association is missing, it would suggest that the observed hits are concentrated within specific chromosomes containing the genes controlling lipid and lipoprotein levels.


Genome scan on apo B, LDL-apo B and apo AI

Detailed results for all chromosomes and phenotypes are available in supplementary information. Table 2 summarizes the markers showing weak to moderate evidence of linkage (p ≤ 0.01 or LOD score ≥ 1.18) with the allele sharing (two-point and multipoint) and the variance component linkage methods. The highest variance component LOD score was obtained for LDL-apo B on chromosome 18q21.32 (LOD = 2.05) (Figure 1). Hits were also observed by the variance component method for total apo B on 6p22.3-p21.1 and 6q23.1, for LDL-apo B on 2q35 and 11q22.3, and for apo AI on 3p25.2.

In this study, the new Haseman-Elston linkage method yielded more genetic loci which are summarized in Table 2. However, most of the strong linkage evidence observed with the allele sharing linkage method (both in two-point and multipoint) were not supported by the variance component method. Only two loci, one at 18q21.32 (marker D18S38, Figure 1) for LDL-apo B and the other at 3p25.2 (D3S1259, Figure 2) for apo AI were supported by both the allele sharing and the variance component methods. These findings were added to the accumulating database derived from the published genomewide scans for lipid-related phenotypes.

Descriptive statistics of the database

The database included 32 citations published during the 1998 through 2003 period. Phenotypes incorporated in the database and the number of genome scans for each phenotype are presented in Table 3. The most frequently studied phenotypes were total cholesterol (n = 10), LDL-C (n = 11), HDL-C (n = 18) and triglyceride (n = 16). Studies on familial hypercholesterolemia, familial combined hyperlipidemia and familial hypobetalipoproteinemia typically used a disease affliction status (affected or unaffected) based on lipid and non-lipid criteria. The other phenotypes were treated as either quantitative or qualitative variables. The study design, the sample size as well as linkage methods varied greatly between studies. Only 15.6% of the investigations were conducted among families ascertained randomly. The remaining were ascertained based on specific clinical criteria such as familial combined hyperlipidemia, familial hypercholesterolemia, familial hypobetalipoproteinemia, CHD, myocardial infarction, low HDL-C concentrations, hypertension, obesity and type 2 diabetes. Few studies were from genetically isolated populations, such as the Hutterites, North-Eastern Indian and the Pima Indians.

Table 4 presents a summary of the loci providing evidence of linkage from the compendium of whole-genome scans. A total of 152 hits were identified which suggests that an average of 4.8 positive loci per study reached the suggestive threshold of significance (p ≤ 0.0023 or LOD ≥ 1.7). This number is very similar to what has been observed for other complex traits when positive loci are summarized from a number of studies24. In order to evaluate whether positive loci were randomly distributed across the genome, we plotted the observed number of hits against the expected number of hits for chromosome 1 to 22 (Figure 3) (see Materials and Methods). A close relationship between positive loci and theoretical genes-content was apparent. This suggests that the null hypothesis of random linkage across the genome cannot be rejected. On the other hand, some chromosomes showed an increased number of observed hits, relative to expected. Indeed chromosomes 21, 13, 15 and 2 had an observed to expected hit ratios of 2.7, 2.4, 1.8 and 1.5, respectively.


The avalanche of information anticipated from whole-genome linkage scans23 has certainly been confirmed for the field of blood lipids and lipoproteins. The accumulating information may soon be overwhelming even for the scientists. Here we have produced a summary of the loci providing evidence of linkage from published genomewide scans carried out on blood lipid-related phenotypes (Table 4). We believe that such compendium will be useful to others in the field. For instance, it may help investigators to access quickly the data on linkage for a specific genomic region or a particular phenotype. We have integrated all linkage signals on the same map to facilitate comparisons across studies.

To provide an example of the usefulness of this compendium, we performed a new genome-wide search on apo B, LDL-apo B and apo AI levels. The results suggested the existence of a susceptibility locus for LDL-apo B on 18q21.32 and a second one for apo AI on 3p25.2. Additional linkages were observed with the allele sharing linkage method but the lack of consistency across linkage methods made the significance of the findings quit doubtful. From Table 4, we can easily identify the other QTLs that have been reported in the same regions from previous genomewide scan studies. Interestingly, the apo AI locus on 3p overlaps with the locus for low HDL-C levels reported in Finnish families26 and with the locus for LDL-3 (phenotype defined as the cholesterol concentration in small LDL particles) observed in Mexican Americans27. The region is also close to the locus for familial hypobetalipoproteinemia28. In contrast, the LDL-apo B locus (18q21.32) observed in this study represents a newly identified locus. Although some genomewide scans have been performed on apo B levels before9-11, this study was the first to investigate the LDL-apo B subfraction. Genomewide scans with subphenotypes have been successful in the past27,29 and may explain the identification of this new locus on 18q21.32.

Our biggest challenge in the compilation of Table 4 was the choice of a significance level for inclusion of a linkage result. This question is related to the ongoing debate concerning significance levels appropriate for reporting evidence of linkage from genome-wide scans on complex traits23,30-34. With the emergence of genomewide scans to identify loci underlying complex traits, geneticists have proposed a refinement of the originally proposed 3 LOD score threshold35. While some advocated a continuation of the more stringent guideline in order to control false positives23, others suggested more flexible guidelines to hunt down genes with small effects believed to be involved in complex traits31. Rao et al.32 proposed a middle ground, for the purpose of carrying out follow up studies, to deal with both false positive and false negative claims. The recommendation was to increase our tolerance from one false positive in 20 genomic scans assuming a continuous map, as suggested by Lander and Kruglyak23, to one per scan assuming a more realistic map density of 400 markers, and to additionally rely on replication. These modifications set the nominal p value to 0.0023 which corresponds to a LOD score of 1.7522,36. However, it is interesting to note that this new threshold corresponded to what was called putative linkage by Thomson31 and suggestive linkage by Lander and Kruglyak23. Accordingly, all point-wise significance levels below this threshold were included in Table 4.

For complex traits, independent replication of an earlier finding gives substantial credibility to the results. However, determining whether a given study has replicated an earlier finding is not simple particularly when different markers have been used. When do we accept that two location estimates in a genomic region representing the same QTL? This issue has been addressed before and it has been proposed that the location estimate may sometime be several centimorgans away from the true locus37. In fact, the 95% CI of the location estimate can span tens of centimorgans depending on family size and number, penetrance of locus, and heterogeneity. Based on the above, the cumulative evidence from genomewide screens for lipid-related phenotypes is now covering a very large portion of the genome (Table 4). It is likely that the entire genome will eventually be covered with at least suggestive evidence of linkage in a few years and replication of findings will be guaranteed in future genomewide scans. This phenomenon is not unique to lipid-related phenotypes. The evolution of the human obesity gene map is a good example of this trend, with more than 300 genes, markers, and chromosomal regions that have now been associated or linked with human obesity phenotypes38.

Despite the large number of QTLs reported to date, a coherent and comprehensive pictures of the loci contributing to variation in lipid and lipoprotein has not been achieved. This is demonstrated by the inability to reject the hypothesis of random positive linkage (Figure 3). We have learned that the genetic mechanisms underlying the predisposition to favorable or unfavorable plasma lipoprotein-lipid levels are more complicated than previously thought. The emergence of such a large number of potential susceptibility loci for lipid-related phenotypes may make it necessary to revisit the criteria for claiming linkage or linkage replication. It is commonly accepted, that a p value less than 0.01 from an independent study sample is sufficient to declare replication of an earlier significant linkage23. However, given the large number of genome scan reports and the inability to precisely localize the locus37, many regions are likely to be replicated solely by chance. For example, more than 30 loci reached the p < 0.01 threshold in the present genome scan study on apo levels and many of them could therefore be considered replicated linkage. New strategies to deal with these issues are urgently needed.

In summary, the identification of gene for complex human diseases and their associated biological traits has had limited success thus far. This limited of success may be explained by genetic heterogeneity, incomplete penetrance, epistasis, phenocopy and pleiotropy39, and undoubtedly other factors. In this paper, we provide a compendium of previous results from genome scan studies on lipid related-phenotypes. We have recorded a large number of loci covering a large portion of the genome. The number of false positives is difficult to assess but is likely to be high since positive findings are more frequently published. Accordingly, even though a single tool summarizing the extensive literature on the subject may prove to be useful, it should be used with caution since the probability of claiming replication just by chance is getting high. In the same paper, we also report a new genome scan on apo levels. Linkage was tested using both an allele sharing and a variance component methods. Many loci provided weak to moderate evidence of linkage but only two QTLs were supported by both analytical methods.


This study was supported by the Canadian Institutes of Health Research (MT-13960 and GR-15187). The authors would like to express their gratitude to the subjects for their excellent collaboration and to the staff of the Physical Activity Sciences Laboratory for their contribution to the study. Y. Bossé is the recipient of a studentship from the Canadian Institutes of Health Research. M.C. Vohl is a research scholar from the “Fonds de la recherche en santé du Québec”. C. Bouchard is supported in part by the George A. Bray Chair in Nutrition. J.P. Després is chair professor of human nutrition, lipidology and prevention of cardiovascular disease supported by Provigo and Pfizer Canada. The results of this paper were obtained using the program package S.A.G.E., which is supported by a U.S. Public Health Service Resource Grant (1 P41 RR03655) from the National Center for Research Ressources.


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Table 1. Characteristics of Genome Scan Participants by Gender and Generation Groups.

Table 2. Summary of LOD Scores ≥ 1.18 or P Values ≤ 0.01.

Table 3. Whole-Genome Scans on Lipid-Related Phenotypes.

Table 4. Evidence for the Presence of Linkage with Lipid-Related Phenotypes from Genomewide Scan Studies

Figure 1. Variance component-based linkage results for chromosome 18 with the total apo B and the LDL-apo B phenotypes. The two traits are adjusted for the effects of age, age2, age3, gender and BMI.

Figure 2. Two-point (solid line) and multipoint (dashed line) sib pairs linkage analysis for chromosome 3 with the apo AI phenotype. Apo AI is adjusted for the effects of age, age2, age3, gender and BMI. The horizontal dot line is a reference corresponding to a p value = 0.01.

Figure 3. Regression analysis of observed and expected hits on the autosomal chromosomes. The observed hit ratio of each chromosome was obtained as: (number of hits on a specific chromosome / all 152 hits) x 100, and the expected hit ratio of each chromosome was obtained as: (number of genes on a specific chromosome / total number of genes in the genome) x 100. The gene content of each chromosome and the genome are from Venter et al.25