Rikard Reneland

Linkage and potential association of obesity-related phenotypes with two genes on chromosome 12q24 in a female dizygous twin cohort.

Obesity is a multifactorial disorder with a complex phenotype. It is a significant risk factor for diabetes and hypertension. We assessed obesity-related traits in a large cohort of twins and performed a genome-wide linkage scan and positional candidate analysis to identify genes that play a role in regulating fat mass and distribution in women. Dizygous female twin pairs from 1094 pedigrees were studied (mean age 47.0±11.5 years (range 18–79 years)). Nonparametric multipoint linkage analyses showed linkage for central fat mass to 12q24 (141 cM) with LOD 2.2 and body mass index to 8q11 (67 cM) with LOD 1.3, supporting previously established linkage data. Novel areas of suggestive linkage were for total fat percentage at 6q12 (LOD 2.4) and for total lean mass at 2q37 (LOD 2.4). Data from follow-up fine mapping in an expanded cohort of 1243 twin pairs reinforced the linkage for central fat mass to 12q24 (LOD 2.6; 143 cM) and narrowed the –1 LOD support interval to 22 cM. In all, 45 single-nucleotide polymorphisms (SNPs) from 26 positional candidate genes within the 12q24 interval were then tested for association in a cohort of 1102 twins. Single-point Monks–Kaplan analysis provided evidence of association between central fat mass and SNPs in two genes – PLA2G1B (P=0.0067) and P2RX4 (P=0.017). These data provide replication and refinement of the 12q24 obesity locus and suggest that genes involved in phospholipase and purinoreceptor pathways may regulate fat accumulation and distribution.

Identification of susceptibility genes and genetic modifiers of human diseases

The completion of the human genome sequence enables the discovery of genes involved in common human disorders. The successful identification of these genes is dependent on the availability of informative sample sets, validated marker panels, a high-throughput scoring technology, and a strategy for combining these resources. We have developed a universal platform technology based on mass spectrometry (MassARRAY) for analyzing nucleic acids with high precision and accuracy. To fuel this technology, we generated more than 100,000 validated assays for single nucleotide polymorphisms (SNPs) covering virtually all known and predicted human genes. We also established a large DNA sample bank comprised of more than 50,000 consented healthy and diseased individuals. This combination of reagents and technology allows the execution of large-scale genome-wide association studies. Taking advantage of MassARRAY’s capability for quantitative analysis of nucleic acids, allele frequencies are estimated in sample pools containing large numbers of individual DNAs. To compare pools as a first-pass “filtering” step is a tremendous advantage in throughput and cost over individual genotyping. We employed this approach in numerous genome-wide, hypothesis-free searches to identify genes associated with common complex diseases, such as breast cancer, osteoporosis, and osteoarthritis, and genes involved in quantitative traits like high density lipoproteins cholesterol (HDL-c) levels and central fat. Access to additional well-characterized patient samples through collaborations allows us to conduct replication studies that validate true disease genes. These discoveries will expand our understanding of genetic disease predisposition, and our ability for early diagnosis and determination of specific disease subtype or progression stage.