The future of genetic studies of complex human diseases

The relationship between the minor allele content and Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease. The genetic risk factors of AD remain better understood. Using previously published dataset of common single nucleotide polymorphisms (SNPs), we studied the association between the minor allele content (MAC) in an individual and AD. We found that AD patients have higher average MAC values than matched controls. We identified a risk prediction model that could predict 2.19% of AD cases. We also identified 49 genes whose expression levels correlated with both MAC and AD. By pathway and process enrichment analyses, these genes were found in pathways or processes closely related to AD. Our study suggests that AD may be linked with too many genetic variations over a threshold. The method of correlations with both MAC and traits appears to be effective in high efficiency identification of target genes for complex traits.

Genome-wide Association Study and Genomic Prediction for Fusarium graminearum Resistance Traits in Nordic Oat (Avena sativa L.)

Fusarium head blight (FHB) and the accumulation of deoxynivalenol (DON) mycotoxin induced by Fusarium graminearum and other Fusarium fungi cause serious problems for oat production in the Nordic region (Scandinavia, Fennoscandia). Besides toxin accumulation, FHB causes reduction in grain yield and in germination capacity. Here, genomic approaches for accelerating breeding efforts against FHB and DON accumulation were studied. Resistance-related traits included DON content, F. graminearum DNA (relative to oat DNA) content (qFUSG) measured with real-time quantitative polymerase chain reaction (PCR), Fusarium-infected kernels (FIKs) and germination capacity (GC). Plant germplasm used in the study consisted of mostly breeding lines, and additionally, a few cultivars and exotic accessions. Genome-wide association study (GWAS) and genomic prediction, enabling genomic selection (GS) on the resistance-related and collected agronomic traits, were performed. Considerable genetic correlations between resistance-related traits were observed: DON content had a positive correlation (0.60) with qFUSG and a negative correlation (−0.63) with germination capacity. With the material in hand, we were not able to find any significant associations between markers and resistance-related traits. On the other hand, in genomic prediction, some resistance-related traits showed favorable accuracy in fivefold cross-validation (GC = 0.57). Genomic prediction is a promising method and genomic estimated breeding values (GEBVs) generated for germination capacity are applicable in oat breeding programs.

Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene

Single Nucleotide Polymorphisms (SNPs) are the most common candidate mutations in human beings that play a vital role in the genetic basis of certain diseases. Previous studies revealed that Solute Carrier Family 26 Member 4 (SLC26A4) being an essential gene of the multi-faceted transporter family SLC26 facilitates reflexive movement of Iodide into follicular lumen through apical membrane of thyrocyte. SLC26A4 gene encodes Pendred protein, a membrane glycoprotein, highly hydrophobic in nature, present at the apical membrane of thyrocyte functioning as transporter of iodide for thyroid cells. A minor genetic variation in SLC26A4 can cause Pendred syndrome, a syndrome associated with thyroid glands and deafness. In this study, we performed in-silico analysis of 674 missense SNPs of SLC26A4 using different computational platforms. The bunch of tools including SNPNEXUS, SNAP-2, PhD-SNP, SNPs&GO, I-Mutant, ConSurf, and ModPred were used to predict 23 highly confident damaging and disease causing nsSNPs (G209V, G197R, L458P, S427P, Q101P, W472R, N392Y, V359E, R409C, Q235R, R409P, G139V, G497S, H723R, D87G, Y127H, F667C, G334A, G95R, S427C, R291W, Q383H and E384G) that could potentially alter the SLC26A4 gene. Moreover, protein structure prediction, protein-ligand docking and Molecular Dynamics simulation were performed to confirm the impact of two evident alterations (Y127H and G334A) on the protein structure and function.

A brief history of human disease genetics

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

275 years of forestry meets genomics in Pinus sylvestris

Pinus sylvestris has a long history of basic and applied research that is relevant for both forestry and evolutionary studies. Its patterns of adaptive variation and role in forest economic and ecological systems have been studied extensively for nearly 275 years, detailed demography for a 100 years and mating system more than 50 years. However, its reference genome sequence is not yet available and genomic studies have been lagging compared to, for example, Pinus taeda and Picea abies, two other economically important conifers. Despite the lack of reference genome, many modern genomic methods are applicable for a more detailed look at its biological characteristics. For example, RNA-seq has revealed a complex transcriptional landscape and targeted DNA sequencing displays an excess of rare variants and geographically homogenously distributed molecular genetic diversity. Current DNA and RNA resources can be used as a reference for gene expression studies, SNP discovery, and further targeted sequencing. In the future, specific consequences of the large genome size, such as functional effects of regulatory open chromatin regions and transposable elements, should be investigated more carefully. For forest breeding and long-term management purposes, genomic data can help in assessing the genetic basis of inbreeding depression and the application of genomic tools for genomic prediction and relatedness estimates. Given the challenges of breeding (long generation time, no easy vegetative propagation) and the economic importance, application of genomic tools has a potential to have a considerable impact. Here, we explore how genomic characteristics of P. sylvestris, such as rare alleles and the low extent of linkage disequilibrium, impact the applicability and power of the tools.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Genome-Wide Composite Interval Mapping (GCIM) of Expressional Quantitative Trait Loci in Backcross Population

One of the most remarkable findings in expressional quantitative trait locus (eQTL) mapping is that trans (distal) eQTL has small effect. The widely used approaches have a low power in the detection of small-effect eQTL. To overcome this issue, we integrate polygenic background control with multi-locus genetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid populations. We describe the genetic model, parameter estimation, multi-locus genetic model, hypothesis tests, and software. Finally, some issues related to the GCIM method are discussed.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

General regression model: A "model-free" association test for quantitative traits allowing to test for the underlying genetic model

Most genome-wide association studies used genetic-model-based tests assuming an additive mode of inheritance, leading to underpowered association tests in case of departure from additivity. The general regression model (GRM) association test proposed by Fisher and Wilson in 1980 makes no assumption on the genetic model. Interestingly, it also allows formal testing of the underlying genetic model. We conducted a simulation study of quantitative traits to compare the power of the GRM test to the classical linear regression tests, the maximum of the three statistics (MAX), and the allele-based (allelic) tests. Simulations were performed on two samples sizes, using a large panel of genetic models, varying genetic models, minor allele frequencies, and the percentage of explained variance. In case of departure from additivity, the GRM was more powerful than the additive regression tests (power gain reaching 80%) and had similar power when the true model is additive. GRM was also as or more powerful than the MAX or allelic tests. The true simulated model was mostly retained by the GRM test. Application of GRM to HbA1c illustrates its gain in power. To conclude, GRM increases power to detect association for quantitative traits, allows determining the genetic model and is easily applicable.

Genomics and metagenomics of colorectal cancer

Colorectal cancer (CRC) is a common cancer globally. It is a complex disease influenced by genetic and environmental factors. Early studies on familial cases have identified major genes involved in CRC, such as proto-oncogenes KRAS, PIK3CA and BRAF, and tumour-suppressor genes APC and TP53. These genes have provided valuable insight into the molecular pathogenesis of CRC, and some have made ways to clinical utility to help diagnose cancer syndromes, prognosticate oncological outcomes and predict treatment responses. While these genetic factors are important, recent studies have suggested contribution of microorganisms to colorectal carcinogenesis. Observational studies, animal experiments and translational works have identified several microorganisms as potential carcinogenic bacteria, such as Fusobacterium nucleatum and Peptostreptococcus anaerobius. With the advent of sequencing technology and bioinformatics, more genomic and metagenomic factors are being uncovered as important players in CRC carcinogenesis. This article aims to review recent genomic and metagenomic discoveries relating to CRC.

Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis

Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.

Genome-wide association study of hippocampal atrophy rate in non-demented elders

Hippocampal atrophy rate has been correlated with cognitive decline and its genetic modifiers are still unclear. Here we firstly performed a genome-wide association study (GWAS) to identify genetic loci that regulate hippocampal atrophy rate. Six hundred and two non-Hispanic Caucasian elders without dementia were included from the Alzheimer's Disease Neuroimaging Initiative cohort. Three single nucleotide polymorphisms (SNPs) (rs4420638, rs56131196, rs157582) in the TOMM40-APOC1 region were associated with hippocampal atrophy rate at genome-wide significance and 3 additional SNPs (in TOMM40 and near MIR302F gene) reached a suggestive level of significance. Strong linkage disequilibrium between rs4420638 and rs56131196 was found. The minor allele of rs4420638 (G) and the minor allele of rs157582 (T) showed associations with lower Mini-mental State Examination score, higher Alzheimer Disease Assessment Scale-cognitive subscale 11 score and smaller entorhinal volume using both baseline and longitudinal measurements, as well as with accelerated cognitive decline. Moreover, rs56131196 (P = 1.96 × 10-454) and rs157582 (P = 9.70 × 10-434) were risk loci for Alzheimer's disease. Collectively, rs4420638, rs56131196 and rs157582 were found to be associated with hippocampal atrophy rate. Besides, they were also identified as genetic loci for cognitive decline.

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits

In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early- to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk; however, the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If the preventive heritable gene therapies were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.

Era of Genomic Medicine: A Narrative Review on CRISPR Technology as a Potential Therapeutic Tool for Human Diseases

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) provides acquired immunity in microorganisms against exogenous DNA that may hinder the survival of the organism. Pioneering work by Doudna and Charpentier in 2012 resulted in the creation of the CRISPR/Cas9 genome editing tool on the basis of this concept. The aim of this was to create a rapid, efficient, and versatile genome-editing tool to facilitate genetic manipulation. The mechanism relies on two components: the RNA guide which acts as a sentinel and a Cas protein complex which functions as a highly precise molecular knife. The guide RNA can be modified to match a DNA sequence of interest in the cell and accordingly be used to rectify mutations that may otherwise cause disease. Within a few years following the development of the CRISPR/Cas9 tool, its usage has become ubiquitous. Its influence extends into many fields of biological sciences from biotechnology and biochemistry to molecular biology and biomedical sciences. The following review aims at shedding some light on to the applications of the CRISPR/Cas9 tool in the field of biomedical sciences, particularly gene therapy. An insight with relation to a few of the many diseases that are being tackled with the aid of the CRISPR/Cas9 mechanism and the trends, successes, and challenges of this application as a gene therapy are discussed in this review.

Deconstructing the sources of genotype-phenotype associations in humans

Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably-in particular for phenotypic prediction-a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.

Association Mapping Between Candidate Gene SNP and Production and Oil Quality Traits in Interspecific Oil Palm Hybrids

Oil palm production is gaining importance in Central and South America. However, the main species Elaeis guineensis (Eg) is suffering severely from bud rod disease, restricting the potential cultivation areas. Therefore, breeding companies have started to work with interspecific Elaeis oleifera × Eg (Eo × Eg) hybrids which are tolerant to this disease. We performed association studies between candidate gene (CG) single nucleotide polymorphisms (SNP) and six production and 19 oil quality traits in 198 accessions of interspecific oil palm hybrids from five different origins. For this purpose, barcoded amplicons of initially 167 CG were produced from each genotype and sequenced with Ion Torrent. After sequence cleaning 115 SNP remained targeting 62 CG. The influence of the origins on the different traits was analyzed and a genetic diversity study was performed. Two generalized linear models (GLM) with principle component analysis (PCA) or structure (Q) matrixes as covariates and two mixed linear models (MLM) which included in addition a Kinship (K) matrix were applied for association mapping using GAPIT. False discovery rate (FDR) multiple testing corrections were applied in order to avoid Type I errors. However, with FDR adjusted p values no significant associations between SNP and traits were detected. If using unadjusted p values below 0.05, seven of the studied CG showed potential associations with production traits, while 23 CG may influence different quality traits. Under these conditions the current approach and the detected candidate genes could be exploited for selecting genotypes with superior CG alleles in Marker Assisted Selection systems.

Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces

Motivation

Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association.

Results

We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function.

Availability and implementation

The dataset used in this paper is available as Supplementary Material. The data can be found at http://prism.ccbb.ku.edu.tr/data/sav/ as well.

Supplementary information

Supplementary data are available at Bioinformatics online.

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

Detection of First Marker Trait Associations for Resistance Against Sclerotinia sclerotiorum in Brassica juncea-Erucastrum cardaminoides Introgression Lines

A set of 96 Brassica juncea-Erucastrum cardaminoides introgression lines (ILs) were developed with genomic regions associated with Sclerotinia stem rot (Sclerotinia sclerotiorum) resistance from a wild Brassicaceous species E. cardaminoides. ILs were assessed for their resistance responses to stem inoculation with S. sclerotiorum, over three crop seasons (season I, 2011/2012; II, 2014/2015; III, 2016-2017). Initially, ILs were genotyped with transferable SSR markers and subsequently through genotyping by sequencing. SSR based association mapping identified six marker loci associated to resistance in both A and B genomes. Subsequent genome-wide association analysis (GWAS) of 84 ILs recognized a large number of SNPs associated to resistance, in chromosomes A03, A06, and B03. Chromosomes A03 and A06 harbored the maximum number of resistance related SNPs. Annotation of linked genomic regions highlighted an array of resistance mechanisms in terms of signal transduction pathways, hypersensitive responses and production of anti-fungal proteins and metabolites. Of major importance was the clustering of SNPs, encoding multiple resistance genes on small regions spanning approximately 885 kb region on chromosome A03 and 74 kb on B03. Five SNPs on chromosome A03 (6,390,210-381) were associated with LRR-RLK (receptor like kinases) genes that encode LRR-protein kinase family proteins. Genetic factors associated with pathogen-associated molecular patterns (PAMPs) and effector-triggered immunity (ETI) were predicted on chromosome A03, exhibiting 11 SNPs (6,274,763-994). These belonged to three R-Genes encoding TIR-NBS-LRR proteins. Marker trait associations (MTAs) identified will facilitate marker assisted introgression of these critical resistances, into new cultivars of B. juncea initially and, subsequently, into other crop Brassica species.

Rapid and Cost-Effective Genotyping Protocol for Angiotensin-Converting Enzyme Insertion/Deletion (Ins/Del) Polymorphism from Saliva

DNA extraction and purification have been generally considered to be required for PCR assay. We demonstrated a new protocol using biological specimens directly as templates for real-time PCR with melting curve analysis. We confirmed the melting curve analysis was particularly suitable for the identification of the insertion/deletion (Ins/Del) polymorphism of the angiotensin-converting enzyme (ACE) gene. The new protocol we developed can be set up using simple and complete PCR analysis including data interpretation in under four hours with additional advantages of application for large-scale clinical research, diagnostics, and epidemiological studies at low cost.

Next-Generation Sequencing in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a common disorder that causes substantial distress. Heritability studies consistently show a strong genetic contribution, raising the hope that identifying ASD-associated genetic variants will offer insights into neurobiology and ultimately therapeutics. Next-generation sequencing (NGS) enabled the identification of disruptive variants throughout protein-coding regions of the genome. Alongside large cohorts and novel statistical methods, these NGS methods revolutionized ASD gene discovery. NGS methods have also contributed substantially to functional genetic data, such as gene expression, used to understand the neurobiological consequences of disrupting these ASD-associated genes. These functional data are also critical for annotating the noncoding genome as whole-genome sequencing (WGS) begins to provide initial insights outside of protein-coding regions. NGS methods still have a major role to play, as do similarly transformative advances in stem cell and gene-editing methods, in translating genetic discoveries into a first generation of ASD therapeutics.

Social communication deficits and restricted repetitive behavior symptoms in Tourette syndrome

Background: Autism spectrum disorders (ASD) have been found to occur more frequently in individuals with Tourette syndrome (TS) than in the general population. Similarities exist between ASD and TS clinically, which suggests a potential relationship between the two conditions. Purpose: The purpose of this study was to explore the occurrence of autism-related features in ASD and TS, focusing on areas of overlap and difference. Patients and methods: This study examined the nature and extent of autistic traits as measured by the Social Communication Questionnaire (SCQ) in a sample with a diagnosis of TS, a sample diagnosed to have ASD, and a normative general population sample. Results: The TS sample had significantly higher mean SCQ scores than the general population, but generally lower scores than the ASD sample. The group differences in mean SCQ scores between the TS and ASD sample were significant except in the domain of restricted repetitive behaviours (RRB). Conclusion: This suggests that ASD traits occur commonly in the TS population, with a significant overlap in certain clinical features. This was especially the case for complex movements or repetitive behaviours, which may represent either: i) a shared phenotype which is subclinical, ii) a phenocopy where some clinical symptoms mimic each other, or iii) a co-morbidity. Awareness of this association can be useful in identifying these symptoms as part of the comprehensive assessment of TS and addressing these to improve the overall clinical outcomes in these patients.

A literature review and systematic meta-analysis on XRCC3 Thr241Met polymorphism associating with susceptibility of oral cancer

Oral cancer is very common, occurring on head as well as neck region with poor prognosis. The X-ray repair cross-complementing group 3 (XRCC3) gene contained in DNA repairing pathway has been investigated for its functional role in oral cancer. Nevertheless, the corresponding results are inconclusive. This study investigated the association of XRCC3 Thr241Met polymorphism regarding oral cancer risk. Article and literature searches were performed using Embase, Medline, PubMed, Wanfang and China National Knowledge Infrastructure (CNKI) databases with a manual search. The keywords of ‘XRCC3 or X-ray repair cross complementing protein 3’, ‘polymorphism or SNP’, ‘oral cancer or oral squamous cell carcinoma’ and their combinations were used to search literature. In accordance with the criteria of inclusion, we focused on only case-and-control studies with the distribution of genotypes and alleles being available to be extracted. Systematic meta-analysis was conducted via the STATA software (version 11.0). After a comprehensive literature collection and review, 1,615 oral cancer cases and 1,897 matched controls extracted from 7 articles were included for this meta-analysis. Our results show that only Met/Met (TT) genotype with the recessive model was associated with high risk of oral cancer (CC + CT vs. TT, OR=1.81, P=0.001, 95% CI=1.28–2.567). A significant relationship was identified under both homozygous and recessive model in Asians (CC vs. TT: OR=2.15, 95% CI=1.107–4.170, P=0.024; CT + CC vs. TT: OR=2.140, 95% CI=1.105–4.144, P=0.024), but not among Caucasians (P>0.05). The results indicate that XRCC3 241Met allele might be a potential factor for oral cancer risk, particularly among Asian population. A further study using a larger population and more ethnicities should be performed to confirm the findings.

Genome-wide association study identifies CD1A associated with rate of increase in plasma neurofilament light in non-demented elders

As a marker of neuroaxonal injury, neurofilament light (NFL) in blood is robustly elevated in many neurodegenerative conditions. We aimed to discover single nucleotide polymorphisms (SNPs) associated with longitudinal changes in plasma NFL levels that affect the risk of developing neurodegenerative disease and clinical disease progression. 545 eligible non-Hispanic white participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with longitudinal plasma NFL data were included. Three SNPs (rs16840041, p=4.50×10-8; rs2269714, p=4.50×10-8; rs2269715, p=4.83×10-8) in CD1A were in high linkage disequilibrium (LD) and significantly associated with the increase in plasma NFL levels. We demonstrate a promoting effect of rs16840041-A on clinical disease progression (p = 0.006). Moreover, the minor allele (A) of rs16840041 was significantly associated with accelerated decline in [18F] Fluorodeoxyglucose (FDG) (estimate -1.6% per year [95% CI -0.6 to -2.6], p=0.0024). CD1A is a gene involved in longitudinal changes in plasma NFL levels and AD-related phenotypes among non-demented elders. Given the potential effects of these variants, CD1A should be further investigated as a gene of interest in neurodegenerative diseases and as a potential target for monitoring disease trajectories and treating disease.

Genome-wide association study and quantitative trait loci mapping of seed dormancy in common wheat (Triticum aestivum L.)

Totally, 23 and 26 loci for the first count germination ratio and the final germination ratio were detected by quantitative trait loci (QTL) mapping and association mapping, respectively, which could be used to facilitate wheat pre-harvest sprouting breeding. Weak dormancy can cause pre-harvest sprouting in seeds of common wheat which significantly reduces grain yield. In this study, both quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) were used to identify loci controlling seed dormancy. The analyses were based on a recombinant inbred line population derived from Zhou 8425B/Chinese Spring cross and 166 common wheat accessions. Inclusive composite interval mapping detected 8 QTL, while 45 loci were identified in the 166 wheat accessions by GWAS. Among these, four loci (Q_bifcgr.cas-3AS/Qfcgr.cas-3AS, Q_bifcgr.cas-6AL.1/Qfcgr.cas-6AL.1, Q_bifcgr.cas-7BL.2/Qfcgr.cas-7BL.2, and Q_bigr.cas-3DL/Qgr.cas-3DL) were detected in both QTL mapping and GWAS. In addition, 41 loci co-located with QTL reported previously, whereas 8 loci (Qfcgr.cas-5AL, Qfcgr.cas-6DS, Qfcgr.cas-7AS, Qgr.cas-3DS.1, Qgr.cas-3DS.2, Q_bigr.cas-3DL/Qgr.cas-3DL, Qgr.cas-4B, and Qgr.cas-5A) were likely to be new. Linear regression showed the first count germination ratio or the final germination ratio reduced while multiple favorable alleles increased. It is suggested that QTL pyramiding was effective to reduce pre-harvest sprouting risk. This study could enrich the research on pre-harvest sprouting and provide valuable information of marker exploration for wheat breeding programs.

Heritability and genetic correlations of heart rate variability at rest and during stress in the Oman Family Study

Supplemental Digital Content is available in the text

Introduction:

Individual differences in heart rate variability (HRV) can be partly attributed to genetic factors that may be more pronounced during stress. Using data from the Oman Family Study (OFS), we aimed to estimate and quantify the relative contribution of genes and environment to the variance of HRV at rest and during stress; calculate the overlap in genetic and environmental influences on HRV at rest and under stress using bivariate analyses of HRV parameters and heart rate (HR).

Methods:

Time and frequency domain HRV variables and average HR were measured from beat-to-beat HR obtained from electrocardiogram recordings at rest and during two stress tests [mental: Word Conflict Test (WCT) and physical: Cold Pressor Test (CPT)] in the OFS – a multigenerational pedigree consisting of five large Arab families with a total of 1326 participants. SOLAR software was used to perform quantitative genetic modelling.

Results:

Heritability estimates for HRV and HR ranged from 0.11 to 0.31 for rest, 0.09–0.43 for WCT, and 0.07–0.36 for CPT. A large part of the genetic influences during rest and stress conditions were shared with genetic correlations ranging between 0.52 and 0.86 for rest-WCT and 0.60–0.92 for rest-CPT. Nonetheless, genetic rest–stress correlations for most traits were significantly smaller than 1 indicating some stress-specific genetic effects.

Conclusion:

Genetic factors significantly influence HRV and HR at rest and under stress. Most of the genetic factors that influence HRV at rest also influence HRV during stress tests, although some unique genetic variance emerges during these challenging conditions.

Identifying genetic determinants of complex phenotypes from whole genome sequence data

BACKGROUND

A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known.

RESULTS

To better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB.

CONCLUSIONS

Altogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.

Genome-wide association study identifies Alzheimer's risk variant in MS4A6A influencing cerebrospinal fluid sTREM2 levels

The triggering receptor expressed on myeloid cells 2 (TREM2) gene has been reported to increase the risk of Alzheimer's disease (AD). The soluble TREM2 protein (sTREM2) in cerebrospinal fluid (CSF) was also associated with AD. However, the role of sTREM2 in AD and its genetic modifiers remain unclear. We carried out a genome-wide association study for CSF sTREM2 levels using participants from the Alzheimer's Disease Neuroimaging Initiative and validated the significant association in an independent cohort from Chinese Alzheimer's Biomarker and LifestylE study. rs7232 in membrane spanning 4-domains A6A (MS4A6A) gene was associated with CSF sTREM2 levels at genome-wide significance (p = 1.42 × 10^-15). The locus influences CSF sTREM2 levels especially in nondemented individuals. And the association was replicable in the validation cohort from Chinese Alzheimer's Biomarker and LifestylE study (p = 0.0106). Besides, the expressions of MS4A6A and TREM2 were correlated in brain regions (p < 2 × 10^-16). The findings of our study suggest that the AD risk variant in the MS4A6A gene participates in the regulation of sTREM2.

Sickle cell disease: Clinical presentation and management of a global health challenge

Sickle cell disease is an autosomal recessive, multisystem disorder, characterised by chronic haemolytic anaemia, painful episodes of vaso-occlusion, progressive organ failure and a reduced life expectancy. Sickle cell disease is the most common monogenetic disease, with millions affected worldwide. In well-resourced countries, comprehensive care programs have increased life expectancy of sickle cell disease patients, with almost all infants surviving into adulthood. Therapeutic options for sickle cell disease patients are however, still scarce. Predictors of sickle cell disease severity and a better understanding of pathophysiology and (epi)genetic modifiers are warranted and could lead to more precise management and treatment. This review provides an extensive summary of the pathophysiology and management of sickle cell disease and encompasses the characteristics, complications and current and future treatment options of the disease.

De Novo Domestication: An Alternative Route toward New Crops for the Future

Current global agricultural production must feed over 7 billion people. However, productivity varies greatly across the globe and is under threat from both increased competitions for land and climate change and associated environmental deterioration. Moreover, the increase in human population size and dietary changes are putting an ever greater burden on agriculture. The majority of this burden is met by the cultivation of a very small number of species, largely in locations that differ from their origin of domestication. Recent technological advances have raised the possibility of de novo domestication of wild plants as a viable solution for designing ideal crops while maintaining food security and a more sustainable low-input agriculture. Here we discuss how the discovery of multiple key domestication genes alongside the development of technologies for accurate manipulation of several target genes simultaneously renders de novo domestication a route toward crops for the future.

Hair of the Dog: Identification of a Cis-Regulatory Module Predicted to Influence Canine Coat Composition

Each domestic dog breed is characterized by a strict set of physical and behavioral characteristics by which breed members are judged and rewarded in conformation shows. One defining feature of particular interest is the coat, which is comprised of either a double- or single-layer of hair. The top coat contains coarse guard hairs and a softer undercoat, similar to that observed in wolves and assumed to be the ancestral state. The undercoat is absent in single-coated breeds which is assumed to be the derived state. We leveraged single nucleotide polymorphism (SNP) array and whole genome sequence (WGS) data to perform genome-wide association studies (GWAS), identifying a locus on chromosome (CFA) 28 which is strongly associated with coat number. Using WGS data, we identified a locus of 18.4 kilobases containing 62 significant variants within the intron of a long noncoding ribonucleic acid (lncRNA) upstream of ADRB1. Multiple lines of evidence highlight the locus as a potential cis-regulatory module. Specifically, two variants are found at high frequency in single-coated dogs and are rare in wolves, and both are predicted to affect transcription factor (TF) binding. This report is among the first to exploit WGS data for both GWAS and variant mapping to identify a breed-defining trait.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Statistical Methods and Software for Substance Use and Dependence Genetic Research

BACKGROUND

Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

METHODS

Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

CONCLUSION

In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.

Genome-wide association analyses for yield and yield-related traits in bread wheat (Triticum aestivum L.) under pre-anthesis combined heat and drought stress in field conditions

Understanding the genetic basis of heat and drought stress tolerance in wheat is prerequisite for wheat breeding program. In the present study, a wheat panel comprising of 192 elite bread wheat genotypes was phenotyped in eight environments for yield and related traits in field conditions. Four stress environments were created by implying four different treatments differing in sowing date and water availability, panel was evaluated for two years in field conditions. The panel was genotyped with 15K Illumina chip and 9236 polymorphic markers concentrated on B genome were employed in GWAS analysis. Consistent, fast LD decay was observed on D genome and structure analysis germplasm divided panel into three major populations. GWAS was performed using BLUEs values of combined environment data in R package GAPIT using log10(P) = 3.96 as significance threshold. The significance of association was further checked using FDR<0.05 threshold. The GWAS identified 487 loci associated with the traits and were significant at log10(p) threshold out of these 350 loci were significant at FDR threshold. For two stress indices 108 associations were significant at FDR threshold. Nine genomic regions were shared among all treatment, while multiple pleiotropic regions were present on chromosome 7D followed by unmapped chromosome. The present study validated many marker trait associations for yield and other traits, MTAs significant under combined drought and heat stress were novel. These regions are important and can be used for fine mapping and marker assisted selection to discover new genes responsible for heat and drought tolerance in wheat.

Genome-wide Significance Thresholds for Admixture Mapping Studies

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women's Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10-5 and 4.5 × 10-6 for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.

Statistical Association Mapping of Population-Structured Genetic Data

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to $10-15$10-15 percent of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.

Immunomodulatory factors gene polymorphisms in chronic periodontitis: an overview

BACKGROUND

Chronic periodontitis (CP), defines as destruction of the supporting tissues of the teeth and resorption of the alveolar bone. It is widespread in human populations and represent an important problem for public health. CP results from inflammatory mechanisms created by the interaction between environmental and host genetic factors that confer the individual susceptibility to the disease.

AIM

The aim of the current study was to explore and summarize some functional biomarkers that are associated with CP susceptibility.

METHODS

CP is considered to be a multifactorial disease. The pathogenesis of multifactorial diseases is characterized by various biological pathways. The studies revealed that polymorphisms were associated with susceptibility to periodontal diseases. In other word, genetic variations can change the development of CP. However, there are some conflicting results, because there are different variations in frequency of some alleles in any populations. Therefore, we conducted the current review to completely understanding the special biomarkers for CP.

RESULTS

There is some evidence that SNPs in the IL-1α, IL-1β, IL1RN, IL-6, IL-10, TNF-α, TGF-β1, IFN-γ and VDR may be associated with CP susceptibility.

CONCLUSION

In conclusion, numerous studies have reported the host genetic factors associated with CP susceptibility and related traits. Therefore, it is prevail to study the multiple SNPs and their effects to find the useful diagnosis methods. The current study will investigate the relationship between polymorphisms in cytokine genes and the susceptibility to the chronic periodontitis.

Vitamin D status and vitamin D receptor gene polymorphism in Saudi children with acute lower respiratory tract infection

There is a significant association exists between vitamin D deficiencies, low respiratory tract infections, and certain types of VDR gene polymorphism. Various studies are being conducted to prove any such link between the different clinical conditions due to disturbed vitamin D regulation and VDR gene polymorphisms. The present study analyzed the presence of vitamin D receptor (VDR) gene polymorphisms (ApaI and TaqI) in Saudi pediatric patient suffering from acute lower respiratory tract infection (ALRTI) cases. Fifty children (50) with ALRTI admitted at King Saud University Medical City, Riyadh/Saudi Arabia were included in addition to seventy-three (73) apparently healthy children who were considered as the control group. Genomic DNA from whole blood was extracted and subjected to polymerase chain reaction (PCR) targeting TaqI and ApaI VDR polymorphisms. RFLP-PCR genotyping was performed to determine the allelic frequency within the VDR gene. In the whole sample, the allelic frequency of ApaI polymorphism in the VDR gene was 58.5%, 17.9%, and 23.6% for AA, Aa, and aa respectively (p = 0.11), while it was 48%, 19%, and 33% for TT, Tt, and tt respectively (p = 0.33) with regards to the frequency of TaqI polymorphism in the VDR gene. VDR ApaI Aa and aa genotypes and VDR TaqI Tt and tt genotypes were not associated with increased risk of ALRTI in children (OR 0.87, 95% CI 0.33-2.28, p = 0.77; OR 0.56, 95% CI 0.23-1.4, p = 0.21; OR 1.15, 95% CI 0.44-2.99, p = 0.77; OR 0.73, 95% CI 0.32-1.68, p = 0.46 respectively). To conclude, neither vitamin D status nor VDR gene polymorphisms such as ApaI and TaqI is associated with increased susceptibility to ALRTI. Linkage disequilibrium was not detected between ApaI and TaqI VDR gene polymorphisms as in the case of serum vitamin D status in ALRTI patients versus apparent healthy children.