Evolutionary evidence of the effect of rare variants on disease etiology

Relation Between Obesity and Type 2 Diabetes: Evolutionary Insights, Perspectives and Controversies

PURPOSE OF REVIEW

Since the mid-twentieth century, obesity and its related comorbidities, notably insulin resistance (IR) and type 2 diabetes (T2D), have surged. Nevertheless, their underlying mechanisms remain elusive. Evolutionary medicine (EM) sheds light on these issues by examining how evolutionary processes shape traits and diseases, offering insights for medical practice. This review summarizes the pathogenesis and genetics of obesity-related IR and T2D. Subsequently, delving into their evolutionary connections. Addressing limitations and proposing future research directions aims to enhance our understanding of these conditions, paving the way for improved treatments and prevention strategies.

RECENT FINDINGS

Several evolutionary hypotheses have been proposed to unmask the origin of obesity-related IR and T2D, e.g., the "thrifty genotype" hypothesis suggests that certain "thrifty genes" that helped hunter-gatherer populations efficiently store energy as fat during feast-famine cycles are now maladaptive in our modern obesogenic environment. The "drifty genotype" theory suggests that if thrifty genes were advantageous, they would have spread widely, but proposes genetic drift instead. The "behavioral switch" and "carnivore connection" hypotheses propose insulin resistance as an adaptation for a brain-dependent, low-carbohydrate lifestyle. The thrifty phenotype theory suggests various metabolic outcomes shaped by genes and environment during development. However, the majority of these hypotheses lack experimental validation. Understanding why ancestral advantages now predispose us to diseases may aid in drug development and prevention of disease. EM helps us to understand the evolutionary relation between obesity-related IR and T2D. But still gaps and contradictions persist. Further interdisciplinary research is required to elucidate complete mechanisms.

Single nucleotide polymorphisms in the cannabinoid CB2 receptor: Molecular pharmacology and disease associations

Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non-synonymous SNP (ns-SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns-SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns-SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune-related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses.

Novel mutation leading to splice donor loss in a conserved site of DMD gene causes Duchenne muscular dystrophy with cryptorchidism

BACKGROUND

As one of the most common congenital abnormalities in male births, cryptorchidism has been found to have a polygenic aetiology according to previous studies of common variants. However, little is known about genetic predisposition of rare variants for cryptorchidism, since rare variants have larger effective size on diseases than common variants.

METHODS

In this study, a cohort of 115 Chinese probands with cryptorchidism was analysed using whole-genome sequencing, alongside 19 parental controls and 2136 unaffected men. Additionally, CRISPR-Cas9 editing of a conserved variant was performed in a mouse model, with MRI screening used to observe the phenotype.

RESULTS

In 30 of 115 patients (26.1%), we identified four novel genes (ARSH, DMD, MAGEA4 and SHROOM2) affecting at least five unrelated patients and four known genes (USP9Y, UBA1, BCORL1 and KDM6A) with the candidate rare pathogenic variants affecting at least two cases. Burden tests of rare variants revealed the genome-wide significances for newly identified genes (p<2.5×10-6) under the Bonferroni correction. Surprisingly, novel and known genes were mainly found on X chromosome (seven on X and one on Y) and all rare X-chromosomal segregating variants exhibited a maternal inheritance rather than de novo origin. CRISPR-Cas9 mouse modelling of a splice donor loss variant in DMD (NC_000023.11:g.32454661C>G), which resides in a conserved site across vertebrates, replicated bilateral cryptorchidism phenotypes, confirmed by MRI at 4 and 10 weeks. The movement tests further revealed symptoms of Duchenne muscular dystrophy (DMD) in transgenic mice.

CONCLUSION

Our results revealed the role of the DMD gene mutation in causing cryptorchidism. The results also suggest that maternal-X inheritance of pathogenic defects could have a predominant role in the development of cryptorchidism.

Exploring the frequency of a TP53 polyadenylation signal variant in tumor DNA from patients diagnosed with lung adenocarcinomas, sarcomas and uterine leiomyomas

The TP53 3'UTR variant rs78378222 A>C has been detected in different tumor types as a somatic alteration that reduces p53 expression through modification of polyadenylation and miRNA regulation. Its prevalence is not yet known in all tumors. Herein, we examine tumor tissue prevalence of rs7837822 in Brazilian cohorts of patients from south and southeast regions diagnosed with lung adenocarcinoma (LUAD, n=586), sarcoma (SARC, n=188) and uterine leiomyoma (ULM, n=41). The minor allele (C) was identified in heterozygosity in 6/586 LUAD tumors (prevalence = 1.02 %) and none of the SARC and ULM samples. Additionally, next generation sequencing analysis revealed that all variant-positive tumors (n=4) with sample availability had additional pathogenic or likely pathogenic somatic variants in the TP53 coding regions. Among them, 3/4 (75 %) had the same pathogenic or likely pathogenic sequence variant (allele frequency <0.05 in tumor DNA) namely c.751A>C (p.Ile251Leu). Our results indicate a low somatic prevalence of rs78378222 in LUAD, ULM and SARC tumors from Brazilian patients, which suggests that no further analysis of this variant in the specific studied regions of Brazil is warranted. However, these findings should not exclude tumor molecular testing of this TP53 3'UTR functional variant for different populations.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Variants of IL6, IL10, FCN2, RNASE3, IL12B and IL17B loci are associated with Schistosoma mansoni worm burden in the Albert Nile region of Uganda

BACKGROUND

Individuals genetically susceptible to high schistosomiasis worm burden may contribute disproportionately to transmission and could be prioritized for control. Identifying genes involved may guide development of therapy.

METHODOLOGY/PRINCIPAL FINDINGS

A cohort of 606 children aged 10-15 years were recruited in the Albert Nile region of Uganda and assessed for Schistosoma mansoni worm burden using the Up-Converting Particle Lateral Flow (UCP-LF) test detecting circulating anodic antigen (CAA), point-of-care Circulating Cathodic Antigen (POC-CCA) and Kato-Katz tests. Whole genome genotyping was conducted on 326 children comprising the top and bottom 25% of worm burden. Linear models were fitted to identify variants associated with worm burden in preselected candidate genes. Expression quantitative trait locus (eQTL) analysis was conducted for candidate genes with UCP-LF worm burden included as a covariate. Single Nucleotide Polymorphism loci associated with UCP-LF CAA included IL6 rs2066992 (OR = 0.43, p = 0.0006) and rs7793163 (OR = 2.0, p = 0.0007); IL21 SNP kgp513476 (OR 1.79, p = 0.0025) and IL17B SNP kgp708159 (OR = 0.35, p = 0.0028). A haplotype in the IL10 locus was associated with lower worm burden (OR = 0.53, p = 0.015) and overlapped SNPs rs1800896, rs1800871 and rs1800872. Significant haplotypes (p<0.05, overlapping significant SNP) associated with worm burden were observed in IL6 and the Th17 pathway IL12B and IL17B genes. There were significant eQTL in the IL6, IL5, IL21, IL25 and IFNG regions.

CONCLUSIONS

Variants associated with S. mansoni worm burden were in IL6, FCN2, RNASE3, IL10, IL12B and IL17B gene loci. However only eQTL associations remained significant after Bonferroni correction. In summary, immune balance, pathogen recognition and Th17 pathways may play a role in modulating Schistosoma worm burden. Individuals carrying risk variants may be targeted first in allocation of control efforts to reduce the burden of schistosomiasis in the community.

How to estimate heritability: a guide for genetic epidemiologists

Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.

10 Years of GWAS in intraocular pressure

Intraocular pressure (IOP) is the only modifiable risk factor for glaucoma, the leading cause of irreversible blindness worldwide. In this review, we summarize the findings of genome-wide association studies (GWASs) of IOP published in the past 10 years and prior to December 2022. Over 190 genetic loci and candidate genes associated with IOP have been uncovered through GWASs, although most of these studies were conducted in subjects of European and Asian ancestries. We also discuss how these common variants have been used to derive polygenic risk scores for predicting IOP and glaucoma, and to infer causal relationship with other traits and conditions through Mendelian randomization. Additionally, we summarize the findings from a recent large-scale exome-wide association study (ExWAS) that identified rare variants associated with IOP in 40 novel genes, six of which are drug targets for clinical treatment or are being evaluated in clinical trials. Finally, we discuss the need for future genetic studies of IOP to include individuals from understudied populations, including Latinos and Africans, in order to fully characterize the genetic architecture of IOP.

Enrichment of titin-truncating variants in exon 327 in dilated cardiomyopathy and its relevance to reduced nonsense-mediated mRNA decay efficiency

Titin truncating variants (TTNtvs) are the most common genetic cause of dilated cardiomyopathy (DCM). Among four regions of titin, A-band enrichment of DCM-causing TTNtvs is widely accepted but the underlying mechanism is still unknown. Meanwhile, few reports have identified exon 327 as a highly mutated A-band exon but the degree of exon 327 enrichment has not been quantitatively investigated. To find the real hotspot of DCM-causing TTNtvs, we aimed to reassess the degree of TTNtv enrichment in known titin regions and in exon 327, separately. In addition, we tried to explain exon 327 clustering in terms of nonsense-mediated mRNA decay (NMD) efficiency and a dominant negative mechanism recently proposed. Research papers focusing on TTNtvs found in patients with DCM were collected. A total of 612 patients with TTNtv-realated DCM were obtained from 10 studies. In the four regions of TTN and exon 327, the degree of TTNtvs enrichment was calculated in a way that the effect of distribution of highly expressed exons was normalized. As a result, exon 327 was the only region that showed significant enrichment for DCM-related TTNtv (p < .001). On the other hand, other A-band exons had almost the same number of TTNtv of random distribution. A review of RNAseq data revealed that the median allelic imbalance deviation of exon 327 TTNtvs was .04, indicating almost zero NMD. From these findings, we propose that the widely accepted A-band enrichment of DCM-related TTNtv is mostly attributable to exon 327 enrichment. In addition, based on the recently demonstrated dominant negative mechanism, the extremely low NMD efficiency seems to contribute to exon 327 enrichment.

A suggested shared aetiology of dementia - a colocalization study

Identification of shared causal genes between dementia and its related clinical outcomes can help understand shared aetiology and multimorbidity surrounding dementia. We performed the HyPrColoc colocalization analysis to detect possible shared causal genes between dementia or Alzheimer's disease (AD) and 5 selected traits: stroke, diabetes, atherosclerosis, cholesterol level, and alcohol consumption within 601 dementia or AD associated genetic regions using summary results of the UK Biobank genome-wide association studies. Functional analysis was performed on the candidate causal genes to explore potential biological pathways. Rs150562240 in the LPIN3 gene was identified as a candidate shared causal variant across dementia, AD and atherosclerosis. Evidence for pairwise colocalization between dementia and stroke, dementia (or AD) and atherosclerosis, and dementia (or AD) and diabetes was found in 2, 6 and 2 genetic regions respectively. Colocalization signals between diabetes and the other 3 non-dementia/AD traits were detected in 5 regions. The colocalization evidence shown in our study suggested shared aetiology between dementia and related diseases such as stroke, atherosclerosis, and diabetes.

Rare Variants in Inborn Errors of Immunity Genes Associated With Covid-19 Severity

Host genetic factors have been shown to play an important role in SARS-CoV-2 infection and the course of Covid-19 disease. The genetic contributions of common variants influencing Covid-19 susceptibility and severity have been extensively studied in diverse populations. However, the studies of rare genetic defects arising from inborn errors of immunity (IEI) are relatively few, especially in the Chinese population. To fill this gap, we used a deeply sequenced dataset of nearly 500 patients, all of Chinese descent, to investigate putative functional rare variants. Specifically, we annotated rare variants in our call set and selected likely deleterious missense (LDM) and high-confidence predicted loss-of-function (HC-pLoF) variants. Further, we analyzed LDM and HC-pLoF variants between non-severe and severe Covid-19 patients by (a) performing gene- and pathway-level association analyses, (b) testing the number of mutations in previously reported genes mapped from LDM and HC-pLoF variants, and (c) uncovering candidate genes via protein-protein interaction (PPI) network analysis of Covid-19-related genes and genes defined from LDM and HC-pLoF variants. From our analyses, we found that (a) pathways Tuberculosis (hsa:05152), Primary Immunodeficiency (hsa:05340), and Influenza A (hsa:05164) showed significant enrichment in severe patients compared to the non-severe ones, (b) HC-pLoF mutations were enriched in Covid-19-related genes in severe patients, and (c) several candidate genes, such as IL12RB1, TBK1, TLR3, and IFNGR2, are uncovered by PPI network analysis and worth further investigation. These regions generally play an essential role in regulating antiviral innate immunity responses to foreign pathogens and in responding to many inflammatory diseases. We believe that our identified candidate genes/pathways can be potentially used as Covid-19 diagnostic markers and help distinguish patients at higher risk.

QTL mapping in Fusarium graminearum identified an allele of FgVe1 involved in reduced aggressiveness

Fusarium graminearum is one of the most frequent causal agents of the Fusarium Head Blight, a cereal disease spread throughout the world, reducing grain production and quality. F. graminearum isolates are genetically and phenotypically highly diverse. Notably, remarkable variations of aggressiveness between isolates have been observed, which could reflect an adaptive potential of this pathogen. In this study, we aimed to characterize the genetic basis of aggressiveness variation observed in an F1 population (n = 94), for which genome sequences of both parental strains are available. Aggressiveness was assessed by a panel of in planta and in vitro proxies during two phenotyping trials including, among others, disease severity and mycotoxin accumulation in wheat spike. One major and single QTL was mapped for all the traits measured, on chromosome I, that explained up to 90% of the variance for disease severity. The confidence interval at the QTL spanned 1.2 Mb and contained 428 genes on the reference genome. Of these, four candidates were selected based on the postulate that a non-synonymous mutation affecting protein function may be responsible for phenotypic differences. Finally, a new mutation was identified and functionally validated in the gene FgVe1, coding for a velvet protein known to be involved in pathogenicity and secondary metabolism production in several fungi.

Rare deleterious germline variants and risk of lung cancer

Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04-75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71-8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3' UTR (OR 4.33, 95%CI 2.03-9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73-11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33-5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.

The rs78378222 prevalence and the copy loss of the protective allele A in the tumor tissue of diffuse large B-cell lymphoma

Background

Rare single nucleotide polymorphisms (SNPs) are likely to be a crucial genetic factor for human diseases, including cancer. rs78378222 is rare SNP in 3′-untranslated region (UTR) of TP53 gene leading to disturbance of 3′-end mRNA processing. The frequency of rs78378222 varies in several studied populations. The meta-analysis of 34 genome-wide association studies indicated that rs78378222 was significantly associated with an increased risk of cancer overall. Bioinformatic analysis indicates that somatic loss of the protective A allele of rs78378222 occurs in the tumor tissue of some malignant. The goal of the current study is to document the rs78378222 prevalence and evaluate the copy loss status of the protective allele A in the tumor tissue of patients with diffuse large B-cell lymphoma (DLBCL).

Methods

Total DNA was isolated from FFPE-samples and peripheral blood of patients with DLBCL and comparable in age and sex controls. rs78378222 genotyping was performed by the PCR-RFLP method using restriction endonuclease HindIII. Direct Sanger’s sequencing was used to confirm the presence of C allele of the rs78378222. The search for TP53 gene mutations was carried out by Sanger’s direct sequencing method, according to the IARC protocol.

Results

The result of genotyping of 136 DNA samples from DLBCL tumor tissue suggested that frequency of the rs78378222 was 11/136 (8.1%). Rare allele C frequency was 11/272 (4.2%). A total of 5/11 DLBCL rs78378222 heterozygous samples had the heterozygosity loss in the TP53 gene. Only one of these cases was combined with TP53 gene mutations which have proven oncogenic potential—p.Arg196Gln, other four cases have not mutations in the coding regions of gene.

Conclusions

At the stages of DLBCL initiation or progression a loss of the protective allele A of rs78378222 occurs. Further efforts are needed to study possible molecular mechanisms underlying somatic alterations in DLBCL in this region of the TP53 3′-UTR as well as functional studies to illustrate how the presents of rs78378222 may affect tumor progression of lymphoma.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

Detection of Driver Modules with Rarely Mutated Genes in Cancers

Identifying driver modules or pathways is a key challenge to interpret the molecular mechanisms and pathogenesis underlying cancer. An increasing number of studies suggest that rarely mutated genes are important for the development of cancer. However, the driver modules consisting of mutated genes with low-frequency driver mutations are not well characterized. To identify driver modules with rarely mutated genes, we propose a functional similarity index to quantify the functional relationship between rarely mutated genes and other ones in the same module. Then, we develop a method to detect Driver Modules with Rarely mutated Genes (DMRG) by incorporating the functional similarity, coverage and mutual exclusivity. By applying DMRG on TCGA cancer dataset on three networks: HINT+HI2012, iRefIndex and MultiNet, we detect driver modules intersecting with the well-known signalling pathways and protein complexes, such as the cell cycle pathway and the mediator complex. DMRG can also detect driver modules effectively with 20, 40, 60 and 80 percent of samples by random selection. When compared with HotNet2, DMRG detects more rarely mutated cancer genes and has higher pathway enrichment. Overall, DMRG provides an effective method for the identification of driver modules with rarely mutated genes.

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer immunotherapy

Premature termination codons (PTCs) can result in the production of truncated proteins or the degradation of messenger RNAs by nonsense-mediated mRNA decay (NMD). Which of these outcomes occurs can alter the effect of a mutation, with the engagement of NMD being dependent on a series of rules. Here, by applying these rules genome-wide to obtain a resource called NMDetective, we explore the impact of NMD on genetic disease and approaches to therapy. First, human genetic diseases differ in whether NMD typically aggravates or alleviates the effects of PTCs. Second, failure to trigger NMD is a cause of ineffective gene inactivation by CRISPR-Cas9 gene editing. Finally, NMD is a determinant of the efficacy of cancer immunotherapy, with only frameshifted transcripts that escape NMD predicting a response. These results demonstrate the importance of incorporating the rules of NMD into clinical decision-making. Moreover, they suggest that inhibiting NMD may be effective in enhancing cancer immunotherapy.

Functional characterization of a SNP (F51S) found in human alpha 1-antitrypsin

BACKGROUND

Alpha 1-antitrypsin (A1AT) deficiency is related to lung and liver diseases, including pulmonary emphysema and liver cirrhosis in humans. Genetic variations including single nucleotide polymorphisms (SNPs) of SERPINA1 are responsible for A1AT deficiency, but the characteristics of the SNPs are not well-understood. Here, we investigated the features of a rare SNP (F51S) of A1AT, which introduces an additional N-glycosylation site in the N-terminal region of A1AT.

METHODS

We evaluated the F51S variant compared with the wild-type (WT) A1AT with regard to expression in CHO-K1 cells, trypsin inhibitory activity, polymerization, and thermal stability.

RESULTS

The recombinant F51S protein expressed in CHO-K1 cells was mostly retained inside cells. The F51S variant had trypsin inhibitory activity, but reduced thermal stability compared with the WT A1AT. The native acrylamide gel data showed that F51S tended to prevent polymerization of A1AT.

CONCLUSION

The results of this study indicate that Phe51 and the surrounding hydrophobic residue cluster plays an important role in the conformation and secretion of A1AT and suggest the harmful effects of a rare F51S SNP in human health.

Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?

Sepsis remains one of the leading causes of death in the USA and it is expected to get worse as the population ages. Moreover, the standard of care, which recommends aggressive treatment with appropriate antibiotics, has led to an increase in multiple drug-resistant organisms. There is a dire need for the development of new antibiotics, improved antibiotic stewardship, and therapies that treat the host response. Development of new sepsis therapeutics has been a disappointment as no drugs are currently approved to treat the various complications from sepsis. Much of the failure has been blamed on animal models that do not accurately reflect the course of the disease. However, recent improvements in metabolomic, transcriptomic, genomic, and proteomic platforms have allowed for a broad-spectrum look at molecular changes in the host response using clinical samples. Integration of these multi-omic datasets allows researchers to perform systems biology approaches to identify novel pathophysiology of the disease. In this review, we highlight what is currently known about sepsis and how integrative omics has identified new diagnostic and predictive models of sepsis as well as novel mechanisms. These changes may improve patient care as well as guide future preclinical analysis of sepsis.

Neuropsychiatric disease-associated genetic variants of the dopamine transporter display heterogeneous molecular phenotypes

Genetic factors are known to significantly contribute to the etiology of psychiatric diseases such as attention deficit hyperactivity disorder (ADHD) and autism spectrum and bipolar disorders, but the underlying molecular processes remain largely elusive. The dopamine transporter (DAT) has received continuous attention as a potential risk factor for psychiatric disease, as it is critical for dopamine homeostasis and serves as principal target for ADHD medications. Constrain metrics for the DAT-encoding gene, solute carrier family 6 member 3 (SLC6A3), indicate that missense mutations are under strong negative selection, pointing to pathophysiological outcomes when DAT function is compromised. Here, we systematically characterized six rare genetic variants of DAT (I312F, T356M, D421N, A559V, E602G, and R615C) identified in patients with neuropsychiatric disorders. We evaluated dopamine uptake and ligand interactions, along with ion coordination and electrophysiological properties, to elucidate functional phenotypes, and applied Zn²⁺ exposure and a substituted cysteine-accessibility approach to identify shared structural changes. Three variants (I312F, T356M, and D421N) exhibited impaired dopamine uptake associated with changes in ligand binding, ion coordination, and distinct conformational disturbances. Remarkably, we found that all three variants displayed gain-of-function electrophysiological phenotypes. I312F mediated an increased uncoupled anion conductance previously suggested to modulate neuronal excitability. T356M and D421N both mediated a cocaine-sensitive leakage of cations, which for T356M was potentiated by Zn²⁺, concurrent with partial functional rescue. Collectively, our findings support that gain of disruptive functions due to missense mutations in SLC6A3 may be key to understanding how dopaminergic dyshomeostasis arises in heterozygous carriers.

The Epithelial Sodium Channel Is a Modifier of the Long-Term Nonprogressive Phenotype Associated with F508del CFTR Mutations

Cystic fibrosis (CF) remains the most lethal genetic disease in the Caucasian population. However, there is great variability in clinical phenotypes and survival times, even among patients harboring the same genotype. We identified five patients with CF and a homozygous F508del mutation in the CFTR gene who were in their fifth or sixth decade of life and had shown minimal changes in lung function over a longitudinal period of more than 20 years. Because of the rarity of this long-term nonprogressive phenotype, we hypothesized these individuals may carry rare genetic variants in modifier genes that ameliorate disease severity. Individuals at the extremes of survival time and lung-function trajectory underwent whole-exome sequencing, and the sequencing data were filtered to include rare missense, stopgain, indel, and splicing variants present with a mean allele frequency of <0.2% in general population databases. Epithelial sodium channel (ENaC) mutants were generated via site-directed mutagenesis and expressed for Xenopus oocyte assays. Four of the five individuals carried extremely rare or never reported variants in the SCNN1D and SCNN1B genes of the ENaC. Separately, an independently enriched rare variant in SCNN1D was identified in the Exome Variant Server database associated with a milder pulmonary disease phenotype. Functional analysis using Xenopus oocytes revealed that two of the three variants in δ-ENaC encoded by SCNN1D exhibited hypomorphic channel activity. Our data suggest a potential role for δ-ENaC in controlling sodium reabsorption in the airways, and advance the plausibility of ENaC as a therapeutic target in CF.

Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits

In genetic association studies of rare variants, low statistical power and potential violations of established estimator properties are among the main challenges of association tests. Multi-marker tests (MMTs) have been proposed to target these challenges, but any comparison with single-marker tests (SMTs) has to consider that their aim is to identify causal genomic regions instead of variants. Valid power comparisons have been performed for the analysis of binary traits indicating that MMTs have higher power, but there is a lack of conclusive studies for quantitative traits. The aim of our study was therefore to fairly compare SMTs and MMTs in their empirical power to identify the same causal loci associated with a quantitative trait. The results of extensive simulation studies indicate that previous results for binary traits cannot be generalized. First, we show that for the analysis of quantitative traits, conventional estimation methods and test statistics of single-marker approaches have valid properties yielding association tests with valid type I error, even when investigating singletons or doubletons. Furthermore, SMTs lead to more powerful association tests for identifying causal genes than MMTs when the effect sizes of causal variants are large, and less powerful tests when causal variants have small effect sizes. For moderate effect sizes, whether SMTs or MMTs have higher power depends on the sample size and percentage of causal SNVs. For a more complete picture, we also compare the power in studies of quantitative and binary traits, and the power to identify causal genes with the power to identify causal rare variants. In a genetic association analysis of systolic blood pressure in the Genetic Analysis Workshop 19 data, SMTs yielded smaller p-values compared to MMTs for most of the investigated blood pressure genes, and were least influenced by the definition of gene regions.

Impact of Genetic Variation on Human CaMKK2 Regulation by Ca2+-Calmodulin and Multisite Phosphorylation

The Ca²⁺-calmodulin dependent protein kinase kinase-2 (CaMKK2) is a key regulator of neuronal function and whole-body energy metabolism. Elevated CaMKK2 activity is strongly associated with prostate and hepatic cancers, whereas reduced CaMKK2 activity has been linked to schizophrenia and bipolar disease in humans. Here we report the functional effects of nine rare-variant point mutations that were detected in large-scale human genetic studies and cancer tissues, all of which occur close to two regulatory phosphorylation sites and the catalytic site on human CaMKK2. Four mutations (G87R, R139W, R142W and E268K) cause a marked decrease in Ca²⁺-independent autonomous activity, however S137L and P138S mutants displayed increased autonomous and Ca²⁺-CaM stimulated activities. Furthermore, the G87R mutant is defective in Thr85-autophosphorylation dependent autonomous activity, whereas the A329T mutation rendered CaMKK2 virtually insensitive to Ca²⁺-CaM stimulation. The G87R and R139W mutants behave as dominant-negative inhibitors of CaMKK2 signaling in cells as they block phosphorylation of the downstream substrate AMP-activated protein kinase (AMPK) in response to ionomycin. Our study provides insight into functionally disruptive, rare-variant mutations in human CaMKK2, which have the potential to influence risk and burden of disease associated with aberrant CaMKK2 activity in human populations carrying these variants.

A vast genomic deletion in the C56BL/6 genome affects different genes within the Ifi200 cluster on chromosome 1 and mediates obesity and insulin resistance

Background

Obesity, the excessive accumulation of body fat, is a highly heritable and genetically heterogeneous disorder. The complex, polygenic basis for the disease consisting of a network of different gene variants is still not completely known.

Results

In the current study we generated a BAC library of the obese-prone NZO strain to clarify the genomic alteration within the gene cluster Ifi200 on chr.1 including Ifi202b, an obesity gene that is in contrast to NZO not expressed in the lean B6 mouse. With the PacBio sequencing data of NZO BAC clones we identified a deletion spanning approximately 261.8 kb in the B6 reference genome. The deletion affects different members of the Ifi200 gene family which also includes the original first exon and 5′-regulatory parts of the Ifi202b gene and suggests to be the relevant cause of its expression deficiency in B6. In addition, the generation and characterization of congenic mice carrying the critical fragment on the B6 background demonstrate its crucial role for obesity and insulin resistance.

Conclusions

Our data reveal the reconstruction of a complex genomic region on mouse chr.1 resulting from deletions and duplications of Ifi200 genes and suggest to be relevant for the development of obesity. The results further demonstrate the complexity of the disease and highlight the importance for studying rare genetic variants as they can be causal for large effects.

Next Generation Sequencing of Pooled Samples: Guideline for Variants' Filtering

Sequencing large number of individuals, which is often needed for population genetics studies, is still economically challenging despite falling costs of Next Generation Sequencing (NGS). Pool-seq is an alternative cost- and time-effective option in which DNA from several individuals is pooled for sequencing. However, pooling of DNA creates new problems and challenges for accurate variant call and allele frequency (AF) estimation. In particular, sequencing errors confound with the alleles present at low frequency in the pools possibly giving rise to false positive variants. We sequenced 996 individuals in 83 pools (12 individuals/pool) in a targeted re-sequencing experiment. We show that Pool-seq AFs are robust and reliable by comparing them with public variant databases and in-house SNP-genotyping data of individual subjects of pools. Furthermore, we propose a simple filtering guideline for the removal of spurious variants based on the Kolmogorov-Smirnov statistical test. We experimentally validated our filters by comparing Pool-seq to individual sequencing data showing that the filters remove most of the false variants while retaining majority of true variants. The proposed guideline is fairly generic in nature and could be easily applied in other Pool-seq experiments.

Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls

Detection of rare causal variants can help uncover the etiology of complex diseases. Recruiting case-parent trios is a popular study design in family-based studies. If researchers can obtain data from population controls, utilizing them in trio analyses can improve the power of methods. The transmission disequilibrium test (TDT) is a well-known method to analyze case-parent trio data. It has been extended to rare-variant association testing (abbreviated as "rvTDT"), with the flexibility to incorporate population controls. The rvTDT method is robust to population stratification. However, power loss may occur in the conditioning process. Here we propose a "conditioning adaptive combination of P-values method" (abbreviated as "conADA"), to analyze trios with/without unrelated controls. By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants. Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification. With regard to statistical methods for next-generation sequencing data analyses, validity may be hampered by population stratification, whereas power may be affected by the inclusion of neutral variants. We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

A novel TP53 variant (rs78378222 A > C) in the polyadenylation signal is associated with increased cancer susceptibility: evidence from a meta-analysis

Polymorphisms in TP53 are involved in the progression of different types of cancer. A rare novel TP53 variant (rs78378222 A > C allele) was found via whole-genome sequencing in 2011. This variant was shown to significantly increase the risk of glioma, colorectal adenoma and prostate cancer. Functional analysis further revealed that this variant hindered TP53 expression and its downstream effect on apoptosis. Several studies have investigated the relationship between rs78378222 and cancer susceptibility. However, the results were not consistent. We conducted the first meta-analysis to give a more credible assessment on the association about this variant and cancer risk. Our meta-analysis included 34 studies consisting of 36599 cases and 91272 controls. These studies were mostly on the basis of high-grade data from Genome-wide association studies (GWASs). The results indicated that TP53 rs78378222 was significantly associated with an increased risk of overall cancer (AC vs. AA: OR = 1.511, 95% CI = 1.285-1.777). Furthermore, stratified analyses indicated that rs78378222 increased the risk of nervous system cancer, skin cancer and other cancer. To summarize, this meta-analysis suggested that rs78378222 C allele is a potent risk factor for overall cancer.

Polymorphisms in HLA Class II Genes Are Associated With Susceptibility to Staphylococcus aureus Infection in a White Population

BACKGROUND

Staphylococcus aureus can cause life-threatening infections. Human susceptibility to S. aureus infection may be influenced by host genetic variation.

METHODS

A genome-wide association study (GWAS) in a large health plan-based cohort included biologic specimens from 4701 culture-confirmed S. aureus cases and 45 344 matched controls; 584 535 single-nucleotide polymorphisms (SNPs) were genotyped on an array specific to individuals of European ancestry. Coverage was increased by imputation of >25 million common SNPs, using the 1000 Genomes Reference panel. In addition, human leukocyte antigen (HLA) serotypes were also imputed.

RESULTS

Logistic regression analysis, performed under the assumption of an additive genetic model, revealed several imputed SNPs (eg, rs115231074: odds ratio [OR], 1.22 [P = 1.3 × 10(-10)]; rs35079132: OR, 1.24 [P = 3.8 × 10(-8)]) achieving genome-wide significance on chromosome 6 in the HLA class II region. One adjacent genotyped SNP was nearly genome-wide significant (rs4321864: OR, 1.13; P = 8.8 × 10(-8)). These polymorphisms are located near the genes encoding HLA-DRA and HLA-DRB1. Results of further logistic regression analysis, in which the most significant GWAS SNPs were conditioned on HLA-DRB1*04 serotype, showed additional support for the strength of association between HLA class II genetic variants and S. aureus infection.

CONCLUSIONS

Our study results are the first reported evidence of human genetic susceptibility to S. aureus infection.

Genetic architecture of colorectal cancer

Colorectal cancer (CRC) is a complex disease that develops as a consequence of both genetic and environmental risk factors. A small proportion (3-5%) of cases arise from hereditary syndromes predisposing to early onset CRC as a result of mutations in over a dozen well defined genes. In contrast, CRC is predominantly a late onset 'sporadic' disease, developing in individuals with no obvious hereditary syndrome. In recent years, genome wide association studies have discovered that over 40 genetic regions are associated with weak effects on sporadic CRC, and it has been estimated that increasingly large genome wide scans will identify many additional novel genetic regions. Subsequent experimental validations have identified the causally related variant(s) in a limited number of these genetic regions. Further biological insight could be obtained through ethnically diverse study populations, larger genetic sequencing studies and development of higher throughput functional experiments. Along with inherited variation, integration of the tumour genome may shed light on the carcinogenic processes in CRC. In addition to summarising the genetic architecture of CRC, this review discusses genetic factors that modify environmental predictors of CRC, as well as examples of how genetic insight has improved clinical surveillance, prevention and treatment strategies. In summary, substantial progress has been made in uncovering the genetic architecture of CRC, and continued research efforts are expected to identify additional genetic risk factors that further our biological understanding of this disease. Subsequently these new insights will lead to improved treatment and prevention of colorectal cancer.

Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases

Genome-wide association studies (GWAS) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs. Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor (<50%) alleles. Indeed, we found that the overall proportion of risk alleles was higher among alleles with frequency <50% (minor alleles) compared to that in the group of major alleles. We hypothesized that negative selection may have different effects on environment (or lifestyle)-dependent versus environment (or lifestyle)-independent diseases. We used an environment/lifestyle index (ELI) to assess influence of environmental/lifestyle factors on disease etiology. ELI was defined as the number of publications mentioning "environment" or "lifestyle" AND disease per 1,000 disease-mentioning publications. We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model, while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection. We hypothesized that previously selectively neutral variants become risk alleles when environment changes. The hypothesis of ancestrally neutral, currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies. The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases.

Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity

Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.

Interleukin polymorphisms associated with overall survival, disease-free survival, and recurrence in non-small cell lung cancer patients

Biomarkers based on germline DNA variations could have translational implications by identifying prognostic factors and sub-classifying patients to tailored, patient-specific treatment. To investigate the association between germline variations in interleukin (IL) genes and lung cancer outcomes, we genotyped 251 single nucleotide polymorphisms (SNPs) from 33 different IL genes in 651 non-small cell lung cancer (NSCLC) patients. Analyses were performed to investigate overall survival, disease-free survival, and recurrence. Our analyses revealed 24 different IL SNPs significantly associated with one or more of the lung cancer outcomes of interest. The GG genotype of IL16:rs7170924 was significantly associated with disease-free survival (HR = 0.65; 95% CI 0.50-0.83) and was the only SNP that produced a false discovery rate (FDR) of modest confidence that the association is unlikely to represent a false-positive result (FDR = 0.142). Classification and regression tree (CART) analyses were used to identify potential higher-order interactions. We restricted the CART analyses to the five SNPs that were significantly associated with multiple endpoints (IL1A:rs1800587, IL1B:rs1143634, IL8:s12506479, IL12A:rs662959, and IL13:rs1881457) and IL16:rs7170924 which had the lowest FDR. CART analyses did not yield a tree structure for overall survival; separate CART tree structures were identified for recurrence, based on three SNPs (IL13:rs1881457, IL1B:rs1143634, and IL12A:rs662959), and for disease-free survival, based on two SNPs (IL12A:rs662959 and IL16:rs7170924), which may suggest that these candidate IL SNPs have a specific impact on lung cancer progression and recurrence. These data suggest that germline variations in IL genes are associated with clinical outcomes in NSCLC patients.

Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools

Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.

Genetics of allergic diseases

Genome-wide association studies (GWAS) have been employed in the field of allergic disease, and significant associations have been published for nearly 100 asthma genes/loci. An outcome of GWAS in allergic disease has been the formation of national and international collaborations leading to consortia meta-analyses, and an appreciation for the specificity of genetic associations to sub-phenotypes of allergic disease. Molecular genetics has undergone a technological revolution, leading to next-generation sequencing strategies that are increasingly employed to hone in on the causal variants associated with allergic diseases. Unmet needs include the inclusion of diverse cohorts and strategies for managing big data.

Summary of results and discussions from the gene-based tests group at Genetic Analysis Workshop 18

I present a summary of the results and discussions held within the working group on gene-based tests at Genetic Analysis Workshop 18 (GAW18). The main focus of interest in our working group was modeling the action of combinations or "groups" of genetic variants, with a group of variants most often defined as a set of single-nucleotide polymorphisms lying within a known gene. Some contributions investigated the performance of previously proposed methods (particularly rare variant collapsing or burden-type methods) for addressing this question, applied to the GAW18 data, and other contributions developed novel approaches and addressed novel questions. Most approaches were successful in detecting significant effects at MAP4 in the simulated data. No other genetic effects were consistently detected across different analyses. Low power was noted, particularly for those methods that restricted analysis to purely the subset of unrelated individuals.

Unraveling genetic origin of aging-related traits: evolving concepts

Discovering the genetic origin of aging-related traits could greatly advance strategies aiming to extend health span. The results of genome-wide association studies (GWAS) addressing this problem are controversial, and new genetic concepts have been fostered to advance the progress in the field. A limitation of GWAS and new genetic concepts is that they do not thoroughly address specifics of aging-related traits. Integration of theoretical concepts in genetics and aging research with empirical evidence from different disciplines highlights the conceptual problems in studies of genetic origin of aging-related traits. To address these problems, novel approaches of systemic nature are required. These approaches should adopt the non-deterministic nature of linkage of genes with aging-related traits and, consequently, reinforce research strategies for improving our understanding of mechanisms shaping genetic effects on these traits. Investigation of mechanisms will help determine conditions that activate specific genetic variants or profiles and explore to what extent these conditions that shape genetic effects are conserved across human lives and generations.

Chip-based direct genotyping of coding variants in genome wide association studies: utility, issues and prospects

There is considerable debate about the most efficient way to interrogate rare coding variants in association studies. The options include direct genotyping of specific known coding variants in genes or, alternatively, sequencing across the entire exome to capture known as well as novel variants. Each strategy has advantages and disadvantages, but the availability of cost-efficient exome arrays has made the former appealing. Here we consider the utility of a direct genotyping chip, the Illumina HumanExome array (HE), by evaluating its content based on: 1. functionality; and 2. amenability to imputation. We explored these issues by genotyping a large, ethnically diverse cohort on the HumanOmniExpressExome array (HOEE) which combines the HE with content from the GWAS array (HOE). We find that the use of the HE is likely to be a cost-effective way of expanding GWAS, but does have some drawbacks that deserve consideration when planning studies.

Two-phase and family-based designs for next-generation sequencing studies

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

Functional characterization of genetic enzyme variations in human lipoxygenases

Mammalian lipoxygenases play a role in normal cell development and differentiation but they have also been implicated in the pathogenesis of cardiovascular, hyperproliferative and neurodegenerative diseases. As lipid peroxidizing enzymes they are involved in the regulation of cellular redox homeostasis since they produce lipid hydroperoxides, which serve as an efficient source for free radicals. There are various epidemiological correlation studies relating naturally occurring variations in the six human lipoxygenase genes (SNPs or rare mutations) to the frequency for various diseases in these individuals, but for most of the described variations no functional data are available. Employing a combined bioinformatical and enzymological strategy, which included structural modeling and experimental site-directed mutagenesis, we systematically explored the structural and functional consequences of non-synonymous genetic variations in four different human lipoxygenase genes (ALOX5, ALOX12, ALOX15, and ALOX15B) that have been identified in the human 1000 genome project. Due to a lack of a functional expression system we resigned to analyze the functionality of genetic variations in the hALOX12B and hALOXE3 gene. We found that most of the frequent non-synonymous coding SNPs are located at the enzyme surface and hardly alter the enzyme functionality. In contrast, genetic variations which affect functional important amino acid residues or lead to truncated enzyme variations (nonsense mutations) are usually rare with a global allele frequency<0.1%. This data suggest that there appears to be an evolutionary pressure on the coding regions of the lipoxygenase genes preventing the accumulation of loss-of-function variations in the human population.

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Non-synonymous coding variations in human lipoxygenases are mostly rare with a global allele frequency <1%.

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Common ALOX SNPs are mainly localized on the enzyme surface and hardly effect the enzyme functionality.

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hALOX15B Ala416Asp is a newly discovered loss-of-function mutation in the hALOX gene family while inactivity seems to be caused by severe structural alterations.

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Our data indicate that there is evolutionary pressure on these redox enzymes preventing the accumulation of loss-of-function variations in the human population.

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1000 Genome database is a useful tool to analyze the distribution and functionality of variations in genes of interest.

Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease

Some individuals with a particular disease-causing mutation or genotype fail to express most if not all features of the disease in question, a phenomenon that is known as 'reduced (or incomplete) penetrance'. Reduced penetrance is not uncommon; indeed, there are many known examples of 'disease-causing mutations' that fail to cause disease in at least a proportion of the individuals who carry them. Reduced penetrance may therefore explain not only why genetic diseases are occasionally transmitted through unaffected parents, but also why healthy individuals can harbour quite large numbers of potentially disadvantageous variants in their genomes without suffering any obvious ill effects. Reduced penetrance can be a function of the specific mutation(s) involved or of allele dosage. It may also result from differential allelic expression, copy number variation or the modulating influence of additional genetic variants in cis or in trans. The penetrance of some pathogenic genotypes is known to be age- and/or sex-dependent. Variable penetrance may also reflect the action of unlinked modifier genes, epigenetic changes or environmental factors. At least in some cases, complete penetrance appears to require the presence of one or more genetic variants at other loci. In this review, we summarize the evidence for reduced penetrance being a widespread phenomenon in human genetics and explore some of the molecular mechanisms that may help to explain this enigmatic characteristic of human inherited disease.

Next-generation sequence analysis of genes associated with obesity and nonalcoholic fatty liver disease-related cirrhosis in extreme obesity

OBJECTIVES

Genome-wide association studies (GWAS) have led to the identification of single nucleotide polymorphisms in or near several loci that are associated with the risk of obesity and nonalcoholic fatty liver disease (NAFLD). We hypothesized that missense variants in GWAS and related candidate genes may underlie cases of extreme obesity and NAFLD-related cirrhosis, an extreme manifestation of NAFLD.

METHODS

We performed whole-exome sequencing on 6 Caucasian patients with extreme obesity [mean body mass index (BMI) 84.4] and 4 obese Caucasian patients (mean BMI 57.0) with NAFLD-related cirrhosis.

RESULTS

Sequence analysis was performed on 24 replicated GWAS and selected candidate obesity genes and 5 loci associated with NAFLD. No missense variants were identified in 19 of the 29 genes analyzed, although all patients carried at least 2 missense variants in the remaining genes without excess homozygosity. One patient with extreme obesity carried 2 novel damaging mutations in BBS1 and was homozygous for benign and damaging MC3R variants. In addition, 1 patient with NAFLD-related cirrhosis was compound heterozygous for rare damaging mutations in PNPLA3.

CONCLUSIONS

These results indicate that analyzing candidate loci previously identified by GWAS analyses using whole-exome sequencing is an effective strategy to identify potentially causative missense variants underlying extreme obesity and NAFLD-related cirrhosis.

Incremental value of rare genetic variants for the prediction of multifactorial diseases

Background

It is often assumed that rare genetic variants will improve available risk prediction scores. We aimed to estimate the added predictive ability of rare variants for risk prediction of common diseases in hypothetical scenarios.

Methods

In simulated data, we constructed risk models with an area under the ROC curve (AUC) ranging between 0.50 and 0.95, to which we added a single variant representing the cumulative frequency and effect (odds ratio, OR) of multiple rare variants. The frequency of the rare variant ranged between 0.0001 and 0.01 and the OR between 2 and 10. We assessed the resulting AUC, increment in AUC, integrated discrimination improvement (IDI), net reclassification improvement (NRI(>0.01)) and categorical NRI. The analyses were illustrated by a simulation of atrial fibrillation risk prediction based on a published clinical risk model.

Results

We observed minimal improvement in AUC with the addition of rare variants. All measures increased with the frequency and OR of the variant, but maximum increment in AUC remained below 0.05. Increment in AUC and NRI(>0.01) decreased with higher AUC of the baseline model, whereas IDI remained constant. In the atrial fibrillation example, the maximum increment in AUC was 0.02 for a variant with frequency = 0.01 and OR = 10. IDI and NRI showed at most minimal increase for variants with frequency greater than or equal to 0.005 and OR greater than or equal to 5.

Conclusions

Since rare variants are present in only a minority of affected individuals, their predictive ability is generally low at the population level. To improve the predictive ability of clinical risk models for complex diseases, genetic variants must be common and have substantial effect on disease risk.

Inactivating Mutations in GT198 in Familial and Early-Onset Breast and Ovarian Cancers

The human GT198 gene (gene symbol PSMC3IP) is located at chromosome 17q21, 470 kb proximal to BRCA1, a locus previously linked to breast and ovarian cancer predisposition. Its protein product (also known as TBPIP and Hop2) has been shown to regulate steroid hormone receptor-mediated gene activation and to stimulate homologous recombination in DNA repair. Here, we screened germline mutations in GT198 in familial and early-onset breast and ovarian cancer patients. We have identified 8 germline variants in a total of 212 index patients including reoccurring nonsense mutation c.310C>T (p.Q104X) and 5' UTR mutation c.-37A>T, each found in 2 unrelated families. Most identified index patients from cancer families had early onsets with a median age of 35 years. c.310C>T was absent in a total of 564 control individuals analyzed. GT198 gene amplification with an imbalanced mutant copy gain was identified in the blood DNA of one of the patients carrying c.310C>T. When tested, this truncating mutation abolished DNA damage-induced Rad51 foci formation. In addition, we have identified 15 somatic mutations in 2 tumors from 1 patient carrying germline mutation c.-37A>T. The presence of a somatic mutation on the wild-type allele showed that GT198 was biallelically mutated in the tumor. The somatic mutations identified near a splicing junction site caused defective alternative splicing and truncated the open reading frame. Therefore, distinct mutations may cause a similar consequence by truncating the full-length protein and inducing a loss of the wild type. Our study provides the first evidence of the presence of inactivating mutations in GT198 in familial and early-onset breast and ovarian cancer patients. Mutations in GT198, a gene regulating DNA repair, potentially contribute to an increased risk in familial breast and ovarian cancers.

A low-frequency GLIS3 variant associated with resistance to Japanese type 1 diabetes

The role of low-frequency variants in type 1 diabetes (T1D) susceptibility still remains to be clarified. In the present study, we analyzed low-frequency variants of the T1D candidate genes in Japanese. We first screened for protein-changing variants of 24 T1D candidate genes in 96 T1D patients and 96 control subjects, and then the association with T1D was tested in 706 T1D patients and 863 control subjects recruited from the collaborating institutions in Japan. In total, 56 protein-changing variants were discovered; among them, 34 were low-frequency variants (allele frequency < 5%). The association analysis of the low-frequency variants revealed that only the A908V variant of GLIS3 was strongly associated with resistance to T1D (Haldane's odds ratio = 0.046, p = 8.21 × 10(-4), and pc=2.22 × 10(-2)). GLIS3 is a zinc finger transcription factor that is highly expressed in pancreatic beta cells, and regulates beta cell development and insulin gene expression. GLIS3 mRNA is also moderately expressed in the human thymus. The precise mechanism responsible for the association is unclear at present, but the A908V variant may affect autoimmunity to the GLIS3 protein itself; the 908V containing epitope may induce central or peripheral tolerance more efficiently than that of 908A.

TNFRSF10B polymorphisms and haplotypes associated with increased risk of death in non-small cell lung cancer

Presently, there are few validated biomarkers that can predict survival or treatment response for non-small cell lung cancer (NSCLC) and most are based on tumor markers. Biomarkers based on germ line DNA variations represent a valuable complementary strategy, which could have translational implications by subclassifying patients to tailored, patient-specific treatment. We analyzed single nucleotide polymorphisms (SNPs) in 53 inflammation-related genes among 651 NSCLC patients. Multivariable Cox proportional hazard models, adjusted for lung cancer prognostic factors, were used to assess the association of genotypes and haplotypes with overall survival. Four of the top 15 SNPs associated with survival were located in the TNF-receptor superfamily member 10b (TNFRSF10B) gene. The T-allele of the top ranked SNP (rs11785599) was associated with a 41% increased risk of death (95% confidence interval [CI] = 1.16-1.70) and the other three TNFRSF10B SNPs (rs1047275, rs4460370 and rs883429) exhibited a 35% (95% CI = 1.11-1.65), 29% (95% CI = 1.06-1.57) and 24% (95% CI = 0.99-1.54) increased risk of death, respectively. Haplotype analyses revealed that the most common risk haplotype (TCTT) was associated with a 78% (95% CI = 1.25-2.54) increased risk of death compared with the low-risk haplotype (CGCC). When the data were stratified by treatment, the risk haplotypes exhibited statistically significantly increased risk of death among patients who had surgery only and no statistically significant effects among patients who had surgery and adjuvant chemotherapy. These data suggest that possessing one or more risk alleles in TNFRSF10B is associated with an increased risk of death. Validated germ line biomarkers may have potential important clinical implications by optimizing patient-specific treatment.

Association between a rare novel TP53 variant (rs78378222) and melanoma, squamous cell carcinoma of head and neck and lung cancer susceptibility in non-Hispanic Whites

Recently, several studies have investigated the association between a newly reported rare functional single nucleotide polymorphism (SNP) in TP53 (rs78378222) and cancer risk, but generated inconsistent findings. The present study further investigated this association with risk of melanoma, squamous cell carcinoma of head and neck (SCCHN) and lung cancer. Using volunteers of non-Hispanic Whites recruited for three large case–control studies, we genotyped the TP53 rs78378222 SNP in 1329 patients with melanoma, 1096 with SCCHN, 1013 with lung cancer and 3000 cancer-free controls. Overall, we did not observe any variant homozygotes in this study population, nor significant associations between the TP53 rs78378222AC genotype or C allele and risk for melanoma (P = 0.680 and 0.682 respectively) and lung cancer (P = 0.379 and 0.382 respectively), but a protection against SCCHN (P = 0.008 and 0.008 respectively), compared with the AA genotype or A allele. An additional meta-analysis including 19,423 cancer patients and 54,050 controls did not support such a risk association either. Our studies did not provide statistical evidence of an association between this rare TP53 variant and increased risk of melanoma, nor of lung cancer, but a possible protection against SCCHN.

GIGI: an approach to effective imputation of dense genotypes on large pedigrees

Recent emergence of the common-disease-rare-variant hypothesis has renewed interest in the use of large pedigrees for identifying rare causal variants. Genotyping with modern sequencing platforms is increasingly common in the search for such variants but remains expensive and often is limited to only a few subjects per pedigree. In population-based samples, genotype imputation is widely used so that additional genotyping is not needed. We now introduce an analogous approach that enables computationally efficient imputation in large pedigrees. Our approach samples inheritance vectors (IVs) from a Markov Chain Monte Carlo sampler by conditioning on genotypes from a sparse set of framework markers. Missing genotypes are probabilistically inferred from these IVs along with observed dense genotypes that are available on a subset of subjects. We implemented our approach in the Genotype Imputation Given Inheritance (GIGI) program and evaluated the approach on both simulated and real large pedigrees. With a real pedigree, we also compared imputed results obtained from this approach with those from the population-based imputation program BEAGLE. We demonstrated that our pedigree-based approach imputes many alleles with high accuracy. It is much more accurate for calling rare alleles than is population-based imputation and does not require an outside reference sample. We also evaluated the effect of varying other parameters, including the marker type and density of the framework panel, threshold for calling genotypes, and population allele frequencies. By leveraging information from existing genotypes already assayed on large pedigrees, our approach can facilitate cost-effective use of sequence data in the pursuit of rare causal variants.

DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels

Micro-indels (insertions or deletions shorter than 21 bps) constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associated mutations with putatively neutral mutations from the 1,000 Genomes Project. The final model gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in is available at http://sparks-lab.org/ddig.