Model-Based Linkage Analysis of a Quantitative Trait.

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Melanocortin-3 receptor gene variants in a Maori kindred with obesity and early onset type 2 diabetes.

Genetic studies suggest a diabetes susceptibility locus on human chromosome 20, near the melanocortin receptor-3 (MC3-R) gene. We examined the MC3-R as a candidate gene for type 2 diabetes in 12 members of a large Maori kindred with multiple affected members. The coding region of the MC3-R gene was sequenced for both diabetic and non-diabetic individuals. Two separate single base pair substitutions were found in the MC3-R coding sequence and these resulted in amino acid changes, Lysine6Threonine and Isoleucine81Valine. Neither of these MC3-R variants tracked with the presence of diabetes. Furthermore, the variant and wild type MC3-R showed similar functional coupling to adenylyl cyclase. A polymorphic marker (D20S32e) close to the human MC3-R (hMC3-R) coding sequence was investigated in a 60-member pedigree for association with diabetes and other metabolic parameters. There was an association between D20S32e genotype and fasting insulin (P=0.0085) and the insulin resistance index, HOMA-R (P=0.0042). An association was also found between genotype and HDL levels during oral glucose tolerance testing (P=0.0037). These associations were independent of BMI, age, gender and diabetes. Our data do not support a role for variations in the coding region of the hMC3-R in the development of type 2 diabetes in this Maori kindred, but suggest that a locus on chromosome 20 q, close to D20S32e, may contribute to both insulin secretion and action in the Maori kindred.

Model-based linkage analysis of a quantitative trait.

Linkage analysis is a family-based method of analysis to examine whether any typed genetic markers co-segregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis, the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single marker analysis and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Comparison of requirements and capabilities of major multipurpose software packages.

The aim of this chapter is to introduce the reader to commonly used software packages and illustrate their input requirements, analysis options, strengths, and limitations. We focus on packages that perform more than one function and include a program for quality control, linkage, and association analyses. Additional inclusion criteria were (1) programs that are free to academic users and (2) currently supported, maintained, and developed. Using those criteria, we chose to review three programs: Statistical Analysis for Genetic Epidemiology (S.A.G.E.), PLINK, and Merlin. We will describe the required input format and analysis options. We will not go into detail about every possible program in the packages, but we will give an overview of the packages requirements and capabilities.

Introduction to Genetic Analysis Workshop 15 summaries.

The 15th biennial Genetic Analysis Workshop (GAW15) took place November 11-15, 2006 in St. Pete Beach, Florida. The workshop's primary focus was on the appropriate linkage, association, and other analyses of the increasingly large datasets generated by genetics research. A record number of participants (N=350) contributed 252 papers to GAW15. These contributions were organized into 17 presentation groups, with a range of 11 to 18 papers in each group (median of 15 papers per group). The data sets--or "problems"--for GAW15 included information from two real data sets and a simulated data set. The first problem utilizing real data included gene expression as the phenotype and genome-wide markers for linkage and association studies. The second problem allowed for detecting and characterizing genetic effects for rheumatoid arthritis. And the simulated problem was generated to reflect the data structure underlying the rheumatoid arthritis study. Further details on GAW15 are provided here, and the primary findings from the workshop are highlighted in the following group summary papers.

Segregation analysis of 389 Icelandic pedigrees with Breast and prostate cancer.

Breast cancer and prostate cancer are the most commonly occurring cancers in females and males, respectively. The objective of this project was to test the hypothesis that breast cancer in females and prostate cancer in males represent homologous cancers that may be controlled by one or more common unidentified genes that may explain some of the observed familial aggregation. We modeled the transmission of a breast-prostate cancer phenotype in 389 pedigrees ascertained through a breast cancer proband drawn from the Icelandic Cancer Registry. Assuming that age at diagnosis of this combined phenotype followed a logistic distribution, segregation analyses were performed to evaluate residual parental effects, a sibship covariate, and a dichotomous cohort effect. The most parsimonious model was a Mendelian codominant model, which could partly explain the familial aggregation of both cancers. Inheritance of a putative high-risk allele (A) predicted gender-specific mean ages of onset for females as 53.8 years, 59.7 years, and 65.6 years for the putative AA, AB, and BB genotypes, respectively. Similarly, the predicted means were 73.7 years, 75.6 years, and 78.3 years, respectively, among males. Under this codominant model, the lifetime risk of a woman being affected was 19% by age 80 years. This implies that when prostate cancer among male relatives of breast cancer probands (unselected for family history or early-onset disease) is considered a pleiotrophic effect of the same gene that increases the risk for breast cancer, women are predicted to have a less than 1 in 5 risk of developing breast cancer when they carry the putative high-risk allele. However, this is a higher risk than in the general Icelandic population. Our results suggest that BRCA2 mutations alone are inadequate to explain all of the excess clustering of prostate cancer cases in families of breast cancer probands, and that additional genes conferring excess risk to both breast and prostate cancer may exist in this population.