Complex phenotypes and phenomenon of genome-wide inter-chromosomal linkage disequilibrium in the human genome

Diversity of major histocompatibility complex (MHC) and natural killer cell receptor (NKR) genes and their interactions in domestic horses

The immunogenome is the part of the genome that underlies immune mechanisms and evolves under various selective pressures. Two complex regions of the immunogenome, major histocompatibility complex (MHC) and natural killer cell receptor (NKR) genes, play an important role in the response to selective pressures of pathogens. Their importance is expressed by their genetic polymorphism at the molecular level, and their diversity associated with different types of diseases at the population level. Findings of associations between specific combinations of MHC/NKR haplotypes with different diseases in model species suggest that these gene complexes did not evolve independently. No such associations have been described in horses so far. The aim of the study was to detect associations between MHC and NKR gene/microsatellite haplotypes in three horse breed groups (Camargue, African, and Romanian) by statistical methods; chi-square test, Fisher's exact test, Pearson's goodness-of-fit test and logistic regression. Associations were detected for both MHC/NKR genes and microsatellites; the most significant associations were found between the most variable KLRA3 gene and the EQCA-1 or EQCA-2 genes. This finding supports the assumption that the KLRA3 is an important receptor for MHC I and that interactions of these molecules play important roles in the horse immunity and reproduction. Despite some limitations of the study such as low numbers of horses or lack of knowledge of the selected genes functions, the results were consistent across different statistical methods and remained significant even after overconservative Bonferroni corrections. We therefore consider them biologically plausible.

Utility of multi-omics data to inform genomic prediction of heifer fertility traits

Biologically informed single nucleotide polymorphisms (SNPs) impact genomic prediction accuracy of the target traits. Our previous genomics, proteomics, and transcriptomics work identified candidate genes related to puberty and fertility in Brahman heifers. We aimed to test this biological information for capturing heritability and predicting heifer fertility traits in another breed i.e., Tropical Composite. The SNP from the identified genes including 10 kilobases (kb) region on either side were selected as biologically informed SNP set. The SNP from the rest of the Bos taurus genes including 10-kb region on either side were selected as biologically uninformed SNP set. Bovine high-density (HD) complete SNP set (628,323 SNP) was used as a control. Two populations-Tropical Composites (N = 1331) and Brahman (N = 2310)-had records for three traits: pregnancy after first mating season (PREG1, binary), first conception score (FCS, score 1 to 3), and rebreeding score (REB, score 1 to 3.5). Using the best linear unbiased prediction method, effectiveness of each SNP set to predict the traits was tested in two scenarios: a 5-fold cross-validation within Tropical Composites using biological information from Brahman studies, and application of prediction equations from one breed to the other. The accuracy of prediction was calculated as the correlation between genomic estimated breeding values and adjusted phenotypes. Results show that biologically informed SNP set estimated heritabilities not significantly better than the control HD complete SNP set in Tropical Composites; however, it captured all the observed genetic variance in PREG1 and FCS when modeled together with the biologically uninformed SNP set. In 5-fold cross-validation within Tropical Composites, the biologically informed SNP set performed marginally better (statistically insignificant) in terms of prediction accuracies (PREG1: 0.20, FCS: 0.13, and REB: 0.12) as compared to HD complete SNP set (PREG1: 0.17, FCS: 0.10, and REB: 0.11), and biologically uninformed SNP set (PREG1: 0.16, FCS: 0.10, and REB: 0.11). Across-breed use of prediction equations still remained a challenge: accuracies by all SNP sets dropped to around zero for all traits. The performance of biologically informed SNP was not significantly better than other sets in Tropical Composites. However, results indicate that biological information obtained from Brahman was successful to predict the fertility traits in Tropical Composite population.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers

Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.

Association genetics of bunch weight and its component traits in East African highland banana (Musa spp. AAA group)

The major quantitative trait loci associated with bunch weight and its component traits in the East African highland banana-breeding population are located on chromosome 3. Bunch weight increase is one of the major objectives of banana improvement programs, but little is known about the loci controlling bunch weight and its component traits. Here we report for the first time some genomic loci associated with bunch weight and its component traits in banana as revealed through a genome-wide association study. A banana-breeding population of 307 genotypes varying in ploidy was phenotyped in three locations under different environmental conditions, and data were collected on bunch weight, number of hands and fruits; fruit length and circumference; and diameter of both fruit and pulp for three crop cycles. The population was genotyped with genotyping by sequencing and 27,178 single nucleotide polymorphisms (SNPs) were generated. The association between SNPs and the best linear unbiased predictors of traits was performed with TASSEL v5 using a mixed linear model accounting for population structure and kinship. Using Bonferroni correction, false discovery rate, and long-range linkage disequilibrium (LD), 25 genomic loci were identified with significant SNPs and most were localized on chromosome 3. Most SNPs were located in genes encoding uncharacterized and hypothetical proteins, but some mapped to transcription factors and genes involved in cell cycle regulation. Inter-chromosomal LD of SNPs was present in the population, but none of the SNPs were significantly associated with the traits. The clustering of significant SNPs on chromosome 3 supported our hypothesis that fruit filling in this population was under control of a few quantitative trait loci with major effects.

Genome-wide association analysis of salinity responsive traits in Medicago truncatula

Salinity stress is an important cause of crop yield loss in many parts of the world. Here, we performed genome-wide association studies of salinity-stress responsive traits in 132 HapMap genotypes of the model legume Medicago truncatula. Plants grown in soil were subjected to a step-wise increase in NaCl concentration, from 0 through 0.5% and 1.0% to 1.5%, and the following traits were measured: vigor, shoot biomass, shoot water content, leaf chlorophyll content, leaf size, and leaf and root concentrations of proline and major ions (Na+ , Cl- , K+ , Ca2+ , etc.). Genome-wide association studies were carried out using 2.5 million single nucleotide polymorphisms, and 12 genomic regions associated with at least four traits each were identified. Transcript-level analysis of the top eight candidate genes in five extreme genotypes revealed association between salinity tolerance and transcript-level changes for seven of the genes, encoding a vacuolar H+ -ATPase, two transcription factors, two proteins involved in vesicle trafficking, one peroxidase, and a protein of unknown function. Earlier functional studies on putative orthologues of two of the top eight genes (a vacuolar H+ -ATPase and a peroxidase) demonstrated their involvement in plant salinity tolerance.

Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data

Background

High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.

Results

We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.

Conclusions

We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.

Inter-chromosomal level of genome organization and longevity-related phenotypes in humans

Studies focusing on unraveling the genetic origin of health span in humans assume that polygenic, aging-related phenotypes are inherited through Mendelian mechanisms of inheritance of individual genes. We use the Framingham Heart Study (FHS) data to examine whether non-Mendelian mechanisms of inheritance can drive linkage of loci on non-homologous chromosomes and whether such mechanisms can be relevant to longevity-related phenotypes. We report on genome-wide inter-chromosomal linkage disequilibrium (LD) and on chromosome-wide intra-chromosomal LD and show that these are real phenomena in the FHS data. Genetic analysis of inheritance in families based on Mendelian segregation reveals that the alleles of single nucleotide polymorphisms (SNPs) in LD at loci on non-homologous chromosomes are inherited as a complex resembling haplotypes of a genetic unit. This result implies that the inter-chromosomal LD is likely caused by non-random assortment of non-homologous chromosomes during meiosis. The risk allele haplotypes can be subject to dominant-negative selection primary through the mechanisms of non-Mendelian inheritance. They can go to extinction within two human generations. The set of SNPs in inter-chromosomal LD (N=68) is nearly threefold enriched, with high significance (p=1.6 × 10(-9)), on non-synonymous coding variants (N=28) compared to the entire qualified set of the studied SNPs. Genes for the tightly linked SNPs are involved in fundamental biological processes in an organism. Survival analyses show that the revealed non-genetic linkage is associated with heritable complex phenotype of premature death. Our results suggest the presence of inter-chromosomal level of functional organization in the human genome and highlight a challenging problem of genomics of human health and aging.

Genomics of human health and aging

Despite notable progress of the candidate-gene and genome-wide association studies (GWAS), understanding the role of genes contributing to human health and lifespan is still very limited. We use the Framingham Heart Study to elucidate if recognizing the role of evolution and systemic processes in an aging organism could advance such studies. We combine throughput methods of GWAS with more detail methods typical for candidate-gene analyses and show that both lifespan and ages at onset of CVD and cancer can be controlled by the same allelic variants. The risk allele carriers are at highly significant risk of premature death (e.g., RR=2.9, p=5.0 × 10(-66)), onset of CVD (e.g., RR=1.6, p=4.6 × 10(-17)), and onset of cancer (e.g., RR=1.6, p=1.5 × 10(-6)). The mechanism mediating the revealed genetic associations is likely associated with biological aging. These aging-related phenotypes are associated with a complex network which includes, in this study, 62 correlated SNPs even so these SNPs can be on non-homologous chromosomes. A striking result is three-fold, highly significant (p=3.6 × 10(-10)) enrichment of non-synonymous SNPs (N=27) in this network compared to the entire qualified set of the studied SNPs. Functional significance of this network is strengthened by involvement of genes for these SNPs in fundamental biological processes related to aging (e.g., response to stimuli, protein degradation, apoptosis) and by connections of these genes with neurological (20 genes) and cardio-vascular (nine genes) processes and tumorigenesis (10 genes). These results document challenging role of gene networks in regulating human health and aging and call for broadening focus on genomics of such phenotypes.