Detecting pleiotropy and epistasis using variance components linkage analysis in jPAP

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

INTRODUCTION

Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex.

METHODS

In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa).

RESULTS

Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer.

CONCLUSION

Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

APOH interacts with FTO to predispose to healthy thinness

We identified eight candidate thinness predisposition variants from the Illumina HumanExome chip genotyped on members of pedigrees selected for either healthy thinness or severe obesity. For validation, we tested the candidates for association with healthy thinness in additional pedigree members while accounting for effects of obesity-associated genes: NPFFR2, NPY2R, FTO, and MC4R. Significance was obtained for the interaction of FTO rs9939609 with APOH missense variant rs52797880 (minor allele frequency 0.054). The thinness odds ratio was estimated as 2.15 (p < 0.05) for the combination of APOH heterozygote with the homozygote for the non-obesity FTO allele. Significance was not obtained for any other combination of a candidate variant with an obesity gene or for any of the eight candidates tested independently.

A Copy Number Variant on Chromosome 20q13.3 Implicated in Thinness and Severe Obesity

BACKGROUND/OBJECTIVES

To identify copy number variants (CNVs) which are associated with body mass index (BMI).

SUBJECTS/METHODS

CNVs were identified using array comparative genomic hybridization (aCGH) on members of pedigrees ascertained through severely obese (BMI ≥ 35 kg/m(2)) sib pairs (86 pedigrees) and thin (BMI ≤ 23 kg/m(2)) probands (3 pedigrees). Association was inferred through pleiotropy of BMI with CNV log⁡2 intensity ratio.

RESULTS

A 77-kilobase CNV on chromosome 20q13.3, confirmed by real-time qPCR, exhibited deletions in the obese subjects and duplications in the thin subjects (P = 2.2 × 10(-6)). Further support for the presence of a deletion derived from inference by likelihood analysis of null alleles for SNPs residing in the region.

CONCLUSIONS

One or more of 7 genes residing in a chromosome 20q13.3 CNV region appears to influence BMI. The strongest candidate is ARFRP1, which affects glucose metabolism in mice.

Five linkage regions each harbor multiple type 2 diabetes genes in the African American subset of the GENNID Study

We previously localized type 2 diabetes (T2D)-susceptibility genes to five chromosomal regions through a genome-wide linkage scan of T2D and age of diagnosis (AOD) in the African American subset of the GENNID sample. To follow up these findings, we repeated the linkage and association analysis using genotypes on an additional 9203 fine-mapping single nucleotide polymorphisms (SNPs) selected to tag genes under the linkage peaks. In each of the five regions, we confirmed linkage and inferred the presence of ≥2 susceptibility genes. The evidence of multiple susceptibility genes consisted of: (1) multiple linkage peaks in four of the five regions; and (2) association of T2D and AOD with SNPs within ≥2 genes in every region. The associated genes included 3 previously reported to associate with T2D or related traits (GRB10, NEDD4L, LIPG) and 24 novel candidate genes, including genes in lipid metabolism (ACOXL) and cell-cell and cell-matrix adhesion (MAGI2, CLDN4, CTNNA2).

Genetic variants associated with protein C levels

BACKGROUND

Normal protein C (PC) plasma levels range widely in the general population. Factors influencing normal PC levels are thought to influence the risk of venous thrombosis. Little is known about the underlying genetic variants.

OBJECTIVES

We performed a genome scan of normal PC levels to identify genes that regulate normal PC levels.

PATIENTS/METHODS

We performed a variance components linkage analysis for normal PC levels in 275 individuals from a single, large family. We then sequenced candidate genes under the identified linkage peak in eight family members: four with high and four with low, but normal, PC levels. For variants showing a difference in carriers between those with high and low PC levels, we re-evaluated linkage in the 275 family members conditional on the measured genotype effect. Genotype-specific mean PC levels were determined using likelihood analysis. Findings were replicated in the Leiden Thrombophilia Study (LETS).

RESULTS

We identified a quantitative trait locus at chromosome 5q14.1 affecting normal PC plasma level variability. Next-generation sequencing of 113 candidate genes under the linkage peak revealed four SNPs in BHMT2, ACOT12, SSBP2 and XRCC4, which significantly increased PC levels in our thrombophilic family, but not in LETS.

CONCLUSIONS

We identified four genes at chromosome 5q14.1 that might influence normal PC levels. BHMT2 seems the most likely candidate to regulate PC levels via homocysteine, a competitive inhibitor to thrombin. Failure to replicate our findings in LETS might be due to differences between the studies in genetic background and linkage disequilibrium patterns.