Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

Abstract

This article is available online at http://www.jlr.org among these different subpopulations are not understood, and the genes responsible for the subpopulation differences among individuals are unknown.Several approaches, including genetic mapping studies, have been used to identify novel genes involved in the regulation of HDL levels. In mice, the technique of quantitative trait locus (QTL) mapping, which uses crosses between different inbred strains of mice, has been heavily employed over the past 15 years. These results have been summarized in several reviews ( 6, 7 ). One limitation of traditional QTL analysis is low mapping resolution, which is a result of the limited genetic recombination possible in onegeneration backcrosses [i.e., A×(A×B)] and two-generation intercrosses [i.e., (A×B)×(A×B)]. The 95% confi dence interval (CI), the interval in which the causative gene is most likely to reside, is usually very broad. For example, one large study of bone mineral density QTL found that the average CI width for traditionally mapped QTL was 32 cM ( 8 ). As it can be assumed that there are, on average, 20 genes per cM ( 9 ), the number of candidate genes per QTL can be very large, making the identifi cation of the causative gene very diffi cult. In the past few years, several methods have been developed that combine accumulated data from the different crosses, allowing for narrowing of the CI for QTL and reducing candidate gene lists ( 10 ). However, the success of these methods heavily depends on the accuracy of the QTL mapping.The current standard genetic map for the mouse is curated and maintained by the Mouse Genome Informatics (MGI) Group at The Jackson Laboratory (www.informatics. jax.org) ( 11 ). Mapping QTL requires accurate genetic map information for both the relative order of markers and the distances between them ( 12 ). Recently, Shifman and colleagues published a new genetic map based on a large population of a heterogeneous stock ( 13 ). Cox and colleagues integrated a total of 7,080 standard, simplesequence length polymorphism (SSLP) markers to this single-nucleotide polymorphism (SNP)-based map, generating a corrected mouse genetic map ( 14 ). This new map Abstract In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confi dence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identifi ed novel QTL and QTL that were mapped...

Mapping and Congenic Dissection of Genetic Loci Contributing to Hyperglycemia and Dyslipidemia in Mice

“…Though partially overlapping, the position of this QTL was noticeably different from that of Bglu16 ( Fig 4 ). The Chr1 locus replicated Cq1 and Hdlq5 , which have been mapped in numerous crosses[ 27 ]. The Chr13 QTL replicated Hdlcl2 , initially mapped in (PERA/EiJ x B6- Ldlr )) x B6- Ldlr backcross [ 28 ].…”

Genetic determinants of atherosclerosis, obesity, and energy balance in consomic mice

“…There are known lipid QTLs on these chromosomes (e.g. for HDL(Ackert-Bicknell et al 2013)) and further congenic strain development from CSSs-3 and -19 may reveal if the ATH QTLs are due to such plasma lipid genetic factors.…”

Genetic dissection of femoral and tibial microarchitecture

“…Defining causal genes is also finally becoming more practical by combining mapping with omics data sets-in our case by combining mapping results with expression data and with GO analyses to generate both bone scores and summary candidate scores (96)(97)(98)(99) . The hard job of validation and mechanistic interpretation is also getting easier, with faster gene engineering methods to modify DNA sequence (91) .…”