Hidden Heritability Research Articles

Genetic Susceptibility to Chronic Kidney Disease - Some More Pieces for the Heritability Puzzle.

Chronic kidney disease (CKD) is a major global health problem with an increasing prevalence partly driven by aging population structure. Both genomic and environmental factors contribute to this complex heterogeneous disease. CKD heritability is estimated to be high (30–75%). Genome-wide association studies (GWAS) and GWAS meta-analyses have identified several genetic loci associated with CKD, including variants in UMOD, SHROOM3, solute carriers, and E3 ubiquitin ligases. However, these genetic markers do not account for all the susceptibility to CKD, and the causal pathways remain incompletely understood; other factors must be contributing to the missing heritability. Less investigated biological factors such as telomere length; mitochondrial proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded genes; structural variants, such as copy number variants (CNVs), insertions, deletions, inversions and translocations are poorly covered and may explain some of the missing heritability. The sex chromosomes, often excluded from GWAS studies, may also help explain gender imbalances in CKD. In this review, we outline recent findings on molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes) that typically have received less attention than gene polymorphisms. Shorter telomere length has been associated with renal dysfunction and CKD progression, however, most publications report small numbers of subjects with conflicting findings. CNVs have been linked to congenital anomalies of the kidney and urinary tract, posterior urethral valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA biomarkers for CKD comes primarily from case reports, therefore the data are scarce and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene, mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found associations between X-chromosome and renal function (rs12845465 and rs5987107). No loci in the Y-chromosome have reached genome-wide significance. In conclusion, despite the efforts to find the genetic basis of CKD, it remains challenging to explain all of the heritability with currently available methods and datasets. Although additional biomarkers have been investigated in less common suspects such as telomeres, CNVs, mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more comprehensive approaches, particularly through the integration of multiple –“omics” data, are needed.

Genetic Susceptibility to Leprosy—From Classic Immune-Related Candidate Genes to Hypothesis-Free, Whole Genome Approaches

Genetics plays a crucial role in controlling susceptibility to infectious diseases by modulating the interplay between humans and pathogens. This is particularly evident in leprosy, since the etiological agent, Mycobacterium leprae, displays semiclonal characteristics not compatible with the wide spectrum of disease phenotypes. Over the past decades, genetic studies have unraveled several gene variants as risk factors for leprosy per se, disease clinical forms and the occurrence of leprosy reactions. As expected, several of these genes are immune-related; yet, hypothesis-free approaches have led to genes not classically linked to immune response. The PARK2, originally described as a Parkinson’s disease gene, illustrates the case: Parkin—the protein coded by PARK2—was defined as an important player regulating innate and adaptive immune responses only years after its description as a leprosy susceptibility gene. Interestingly, even with the use of powerful hypothesis-free study designs such as genome-wide association studies, most of the major gene effect controlling leprosy susceptibility remains elusive. One hypothesis to explain this “hidden heritability” is that rare variants not captured by classic association studies are of critical importance. To address this question, massively parallel sequencing of large segments of the human genome—even whole exomes/genomes—is an alternative to properly identify rare, disease-causing mutations. These mutations may then be investigated through sophisticated approaches such as cell reprogramming and genome editing applied to create in vitro models for functional leprosy studies.

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data

We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the “hidden heritability” of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10−7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10−8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10−7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.

Gene-environment interactions in inflammatory bowel disease pathogenesis.

Inflammatory bowel disease (IBD) has long been known to have genetic risk factors because of increased prevalence in the relatives of affected individuals. However, genome-wide association studies have only explained limited heritability in IBD. The observed globally rising incidence of IBD has implicated the role of environmental factors. The hidden unexplained heritability remains to be explored. Recent aggregate evidence has highlighted the extent and nature of host genome-microbiome associations, a key next step in understanding the mechanisms of pathogenesis in IBD. An individual's gut microbiota is shaped not only by genetic but also by environmental factors like diet. Minimizing exposure of the intestinal lumen to selected food items has shown to prolong the remission state of IBD. Among a genetically susceptible host, the shift of gut microbiota (or 'dysbiosis') can lead to increasing the susceptibility to IBD. With the advances in high-throughput large-scale 'omics' technologies in combination with creative data mining and system biology-based network analyses, the complexity of biological functional networks behind the cause of IBD has become more approachable. Therefore, the hidden heritability in IBD has become more explainable, and can be attributable to the changing environmental factors, epigenetic modifications, and gene-host microbial ('in-vironmental') or gene-extrinsic environmental interactions. This review discusses the perspectives of relevance to clinical translation with emphasis on gene-environment interactions. No doubt, the use of system-based approaches will lead to the development of alternative, and hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD.

Research review: Polygenic methods and their application to psychiatric traits.

Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years. We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs; a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability. Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. Sample sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs. Increasing the sample size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of functional analyses that might eventually lead to new prevention and treatment options and to improved biological validity of diagnostic constructs. Polygenic analyses will contribute further to our understanding of complex genetic traits as sample sizes increase and as sample resources become richer in phenotypic descriptors, both in terms of clinical symptoms and of nongenetic risk factors.

Nutrigenetics and personalised/stratified approaches to the provision of dietary advice

Nutrigenetics refers to the interaction of genotype and diet composition to influence metabolism, health status and the risk of diet-related diseases. Gene variants influence food choice and appetite and therefore the intake of dietary components, their absorption, metabolism and tissue uptake along with their impact on molecular targets and physiological processes. The first draft of the majority (~90%) of the sequence of the human genome was published in a Nature article entitled ‘Initial sequencing and analysis of the human genome’ in 2001 [1] with the complete sequence (~99.7%) available in 2004 [2]. At the time such information was considered by many to be the panaceas and one of the greatest ever medical achievements, with genetic analysis likely to refine disease risk and prediction and the personalisation/stratification of interventions in order to afford maximum benefits. Thirteen years on many consider progress based on the human genome to be limited, with the exception of the identification of the genetic basis of Mendelian (monogenic) disorders. Much of the estimated heritable component of disease risk and response to environmental change is unaccounted for. Based on available information, it appears that rather than being overestimated, the heritability is dark matter (i.e. it is real but we cannot see it yet), attributable to as yet undetected rare variants, or the underestimation of the impact of known variants. Genotype-diet-phenotype associations are not homogenous and influenced by a whole range of variables such as sex, ethnicity, drug use and other lifestyle variables which could in part account for the apparent ‘hidden heritability’ [3] Specific examples of nutrigenetic interactions will be presented, in particular in the area of APOE genotype, along with some consideration of the practical, ethical, social and cost implications of the wider use of genetic information in public health and clinical practice.

The genetic contribution to disease risk and variability in response to diet: where is the hidden heritability?

Ten years ago, it was assumed that disease risk prediction and personalised nutrition based on genetic information would now be in widespread use. However, this has not (yet) transpired. The interaction of genetic make-up, diet and health is far more complex and subtle than originally thought. With a few notable exceptions, the impact of identified common genetic variants on phenotype is relatively small and variable in their penetrance. Furthermore, the known variants account for only a fraction of what we believe to be the total genetic contribution to disease risk and heterogeneity in response to environmental change. Here, the question 'how far have we progressed and are we likely to get there' (Rimbach and Minihane, 2009) is revisited with regard to the translation of genetic knowledge into public health benefit. It is concluded that progress to date has been modest. It is hoped that recent technological developments allowing the detection of rarer variants and future use of more hypothesis-driven targeted data analysis will reveal most of the currently 'hidden' significant genetic variability.

Discovery by the Epistasis Project of an epistatic interaction between the GSTM3 gene and the HHEX/IDE/KIF11 locus in the risk of Alzheimer's disease

Despite recent discoveries in the genetics of sporadic Alzheimer's disease, there remains substantial “hidden heritability.” It is thought that some of this missing heritability may be because of gene–gene, i.e., epistatic, interactions. We examined potential epistasis between 110 candidate polymorphisms in 1757 cases of Alzheimer's disease and 6294 control subjects of the Epistasis Project, divided between a discovery and a replication dataset. We found an epistatic interaction, between rs7483 in GSTM3 and rs1111875 in the HHEX/IDE/KIF11 gene cluster, with a closely similar, significant result in both datasets. The synergy factor (SF) in the combined dataset was 1.79, 95% confidence interval [CI], 1.35–2.36; p = 0.00004. Consistent interaction was also found in 7 out of the 8 additional subsets that we examined post hoc: i.e., it was shown in both North Europe and North Spain, in both men and women, in both those with and without the ε4 allele of apolipoprotein E, and in people older than 75 years (SF, 2.27; 95% CI, 1.60–3.20; p < 0.00001), but not in those younger than 75 years (SF, 1.06; 95% CI, 0.59–1.91; p = 0.84). The association with Alzheimer's disease was purely epistatic with neither polymorphism showing an independent effect: odds ratio, 1.0; p ≥ 0.7. Indeed, each factor was associated with protection in the absence of the other factor, but with risk in its presence. In conclusion, this epistatic interaction showed a high degree of consistency when stratifying by sex, the ε4 allele of apolipoprotein E genotype, and geographic region.

Genetics of rheumatoid arthritis: time for a change!

To review recent progress in the genetics of rheumatoid arthritis (RA) and discuss the implications for understanding the pathogenesis of the disease as well as clinical application. Protection against anticitrullinated protein antibody (ACPA) positive RA was shown to be associated wit DRB1*1301. Genome-wide association studies (GWASs) added about 10 new loci to the list of already more than 20 loci associated with RA, so the list is now over 30. Typing for the known risk loci is not helpful for prediction of the risk for RA. It is remarkable how few functional studies have been published. Known genetic factors explain 50-60% of the genetic variance for susceptibility to ACPA-positive and 30-50% for ACPA-negative RA. Searching for the remaining missing or hidden heritability is in all probability not going to yield much for prediction and/or targeted intervention. Therefore, I conclude that if you want to find more genes you should have a lot of patience, time and money, stop with convential GWAS and invest in large-scale sequencing of selected patients and controls. I have a better suggestion, however: use the information that is already available to perform functional studies in order to understand the mechanism of the known associations!

Correcting Away the Hidden Heritability

In the age of high-density genome-wide association (GWAS) data, correcting for multiple comparisons is a substantial issue for genetic epidemiological studies. However, the current manuscript review process generally requires both stringent correction and independent replication. The result of this stringency is that studies that are published suffer from inflated Type 2 error rates (false negatives), thereby removing many likely real signals from follow-up. Elimination of these alleles, if they are truly associated, from further study will slow research progress in studies of complex disease. We argue that this method of correction is overly conservative, especially in an age when high-density follow-up experiments are possible and reasonably inexpensive.

The quest for genetic risk factors for Crohn's disease in the post-GWAS era.

Multiple genome-wide association studies (GWASs) and two large scale meta-analyses have been performed for Crohn's disease and have identified 71 susceptibility loci. These findings have contributed greatly to our current understanding of the disease pathogenesis. Yet, these loci only explain approximately 23% of the disease heritability. One of the future challenges in this post-GWAS era is to identify potential sources of the remaining heritability. Such sources may include common variants with limited effect size, rare variants with higher effect sizes, structural variations, or even more complicated mechanisms such as epistatic, gene-environment and epigenetic interactions. Here, we outline potential sources of this hidden heritability, focusing on Crohn's disease and the currently available data. We also discuss future strategies to determine more about the heritability; these strategies include expanding current GWAS, fine-mapping, whole genome sequencing or exome sequencing, and using family-based approaches. Despite the current limitations, such strategies may help to transfer research achievements into clinical practice and guide the improvement of preventive and therapeutic measures.

Group selection and social evolution in domesticated animals.

Social interactions, especially those involving competition among individuals, are important in domesticated livestock and in natural populations. The heritability of traits affected by such interactions has two components, one originating in the individual like that of classical traits (direct effects) and the other originating in other group members (indirect effects). The latter type of trait represents a significant source of ‘hidden heritability’ and it requires population structure and knowledge from relatives in order to access it for selective breeding. When ignored, competitive interactions may increase as an indirect response to direct selection, resulting in diminished yields. We illustrate how population genetic structure affects the response to selection of traits with indirect genetic effects using population genetic and quantitative genetic theory. Population genetic theory permits us to connect our results to the existing body of theory on kin and group selection in natural populations. The quantitative genetic perspective allows us to see how breeders have used knowledge from relatives and family selection in the domestication of plants and animals to improve the welfare and production of livestock by incorporating social genetic effects in the breeding program. We illustrate the central features of these models by reviewing empirical studies from domesticated chickens.