Common vs. rare allele hypotheses for complex diseases

Rare MED12L Variants Are Associated with Susceptibility to Guttate Psoriasis in the Han Chinese Population

INTRODUCTION

According to the common disease/rare variant hypothesis, it is important to study the role of rare variants in complex diseases. The association of rare variants with psoriasis has been demonstrated, but the association between rare variants and specific clinical subtypes of psoriasis has not been investigated.

METHODS

Gene-based and gene-level meta-analyses were performed on data extracted from our previous study data sets (2,483 patients with guttate psoriasis and 8,292 patients with non-guttate psoriasis) for genotyping. Then, haplotype analysis was performed for rare loss-of-function variants located in MED12L, and protein function prediction was performed for MED12L. Gene-based analysis at each stage had a moderate significance threshold (p < 0.05). A χ2 test was then conducted on the three potential genes, and the merged gene-based analysis was used to confirm the results. We also conducted association analysis and meta-analysis for functional variants located on the identified gene.

RESULTS

Through these gene-level analyses, we determined that MED12L is a guttate psoriasis susceptibility gene (p = 9.99 × 10-5), and the single-nucleotide polymorphism with the strongest association was rs199780529 (p_combine = 1 × 10-3, p_meta = 2 × 10-3).

CONCLUSIONS

In our study, a guttate psoriasis-specific subtype-associated susceptibility gene was confirmed in a Chinese Han population. These findings contribute to a better genetic understanding of different subtypes of psoriasis.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

PTCH1 Gene Variants, mRNA Expression, and Bioinformatics Insights in Mexican Cutaneous Squamous Cell Carcinoma Patients

BACKGROUND

Skin cancer is one of the most frequent types of cancer, and cutaneous squamous cell carcinoma (cSCC) constitutes 20% of non-melanoma skin cancer (NMSC) cases. PTCH1, a tumor suppressor gene involved in the Sonic hedgehog signaling pathway, plays a crucial role in neoplastic processes.

METHODS

An analytical cross-sectional study, encompassing 211 cSCC patients and 290 individuals in a control group (CG), was performed. A subgroup of samples was considered for the relative expression analysis, and the results were obtained using quantitative real-time PCR (qPCR) with TaqMan® probes. The functional, splicing, and disease-causing effects of the proposed variants were explored via bioinformatics.

RESULTS

cSCC was predominant in men, especially in sun-exposed areas such as the head and neck. No statistically significant differences were found regarding the rs357564, rs2236405, rs2297086, and rs41313327 variants of PTCH1, or in the risk of cSCC, nor in the mRNA expression between the cSCC group and CG. A functional effect of rs357564 and a disease-causing relation to rs41313327 was identified.

CONCLUSION

The proposed variants were not associated with cSCC risk in this Mexican population, but we recognize the need for analyzing larger population groups to elucidate the disease-causing role of rare variants.

Altered pubertal timing in 7q11.23 copy number variations and associated genetic mechanisms

Pubertal timing, including age at menarche (AAM), is a heritable trait linked to lifetime health outcomes. Here, we investigate genetic mechanisms underlying AAM by combining genome-wide association study (GWAS) data with investigations of two rare genetic conditions clinically associated with altered AAM: Williams syndrome (WS), a 7q11.23 hemideletion characterized by early puberty; and duplication of the same genes (7q11.23 Duplication syndrome [Dup7]) characterized by delayed puberty. First, we confirm that AAM-derived polygenic scores in typically developing children (TD) explain a modest amount of variance in AAM (R2 = 0.09; p = 0.04). Next, we demonstrate that 7q11.23 copy number impacts AAM (WS < TD < Dup7; p = 1.2x10-8, η2 = 0.45) and pituitary volume (WS < TD < Dup7; p = 3x10-5, ηp2 = 0.2) with greater effect sizes. Finally, we relate an AAM-GWAS signal in 7q11.23 to altered expression in postmortem brains of STAG3L2 (p = 1.7x10-17), a gene we also find differentially expressed with 7q11.23 copy number (p = 0.03). Collectively, these data explicate the role of 7q11.23 in pubertal onset, with STAG3L2 and pituitary development as potential mediators.

Evaluating the regulatory function of non-coding autism-associated single nucleotide polymorphisms on gene expression in human brain tissue

Common variants account for most of the estimated heritability associated with autism spectrum disorder (autism). Although several replicable single nucleotide polymorphisms (SNPs) for the condition have been detected using genome-wide association study (GWAS) methodologies, their pathophysiological relevance remains elusive. Examining this is complicated, however, as all detected loci are situated within non-coding regions of the genome. It is therefore likely that they possess roles of regulatory function as opposed to directly affecting gene coding sequences. To bridge the gap between SNP discovery and mechanistic insight, we applied a comprehensive bioinformatic pipeline to functionally annotate autism-associated polymorphisms and their non-coding linkage disequilibrium (i.e., non-randomly associated) partners. We identified 82 DNA variants of probable regulatory function that may contribute to autism pathogenesis. To validate these predictions, we measured the impact of 11 high-confidence candidates and their GWAS linkage disequilibrium partners on gene expression in human brain tissue from Autistic and non-Autistic donors. Although a small number of the surveyed variants exhibited measurable influence on gene expression as determined via quantitative polymerase chain reaction, these did not survive correction for multiple comparisons. Additionally, no significant genotype-by-diagnosis effects were observed for any of the SNP-gene associations. We contend that this may reflect an inability to effectively capture the modest, neurodevelopmental-specific impact of individual variants on biological dysregulation in available post-mortem tissue samples, as well as limitations in the existing autism GWAS data.

Comparing rare variants versus common in the pathogenesis of nonalcoholic fatty liver disease: a whole exome sequencing approach

BACKGROUND/PURPOSE

Genome-wide association studies have reported the association of common variants with nonalcoholic fatty liver disease in genes, namely, PNPLA3/TM6SF2/MBOAT7/HSD17B13, across ethnicities. However, the approach does not identify rarer variants with a higher effect size. We therefore sequenced the complete exonic regions of patients with nonalcoholic steatohepatitis and controls to compare rare and common variants with a role in the pathogenesis.

METHODS

This is a prospective study that recruited 54 individuals with/without fatty infiltration. Patients with biopsy-proven nonalcoholic steatohepatitis and persistently elevated liver enzymes were included. Controls were with normal CT/MR fat fraction. DNA was isolated from whole blood, amplified (SureSelectXT Human All Exon V5 + UTR kit) and sequenced (Illumina). Data were filtered for quality, aligned (hg19), and annotated (OpenCRAVAT). Pathogenic (Polyphen-2/SIFT/ClinVar) variants and variants reported to be associated with NAFLD based on published literature were extracted from our data and compared between patients and controls.

RESULTS

The mean age of controls (N = 17) and patients (N = 37) was 46.88 ± 6.94 and 37.46 ± 13.34 years, respectively. A total of 251 missense variants out of 89 286 were classified as pathogenic. Of these, 106 (42.23%) were unique to the patients and remaining (n = 145; 57.77%) were found in both patients and controls. Majority (25/37; 67.57%) patients had a minimum of one or more rare pathogenic variant(s) related to liver pathology that was not seen in the controls.

CONCLUSION

Elucidating the contribution of rare pathogenic variants would enhance our understanding of the pathogenesis. Including the rarer genes in the polygenic risk scores would enhance prediction power.

Genetics of diabetes

Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.

Rare penetrant mutations confer severe risk of common diseases

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

Rare penetrant mutations confer severe risk of common diseases

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

One sentence summary

Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

The contribution of copy number variants to psychiatric symptoms and cognitive ability

Copy number variants (CNVs) are deletions and duplications of DNA sequence. The most frequently studied CNVs, which are described in this review, are recurrent CNVs that occur in the same locations on the genome. These CNVs have been strongly implicated in neurodevelopmental disorders, namely autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD), but also in schizophrenia. More recent work has also shown that CNVs increase risk for other psychiatric disorders, namely, depression, bipolar disorder, and post-traumatic stress disorder. Many of the same CNVs are implicated across all of these disorders, and these neuropsychiatric CNVs are also associated with cognitive ability in the general population, as well as with structural and functional brain alterations. Neuropsychiatric CNVs also show incomplete penetrance, such that carriers do not always develop any psychiatric disorder, and may show only mild symptoms, if any. Variable expressivity, whereby the same CNVs are associated with many different phenotypes of varied severity, also points to highly complex mechanisms underlying disease risk in CNV carriers. Comprehensive and longitudinal phenotyping studies of individual CNVs have provided initial insights into these mechanisms. However, more work is needed to estimate and predict the effect of non-recurrent, ultra-rare CNVs, which also contribute to psychiatric and cognitive outcomes. Moreover, delineating the broader phenotypic landscape of neuropsychiatric CNVs in both clinical and general population cohorts may also offer important mechanistic insights.

Association of Single-Nucleotide Polymorphisms Rs2779249 (chr17:26128581 C>A) and Rs rs2297518 (chr17: chr17:27769571 G>A) of the NOS2 Gene with Tension-Type Headache and Arterial Hypertension Overlap Syndrome in Eastern Siberia

Inducible nitric oxide (NO) synthase (iNOS), encoded by the NOS2 gene, promotes the generation of high levels of NO to combat harmful environmental influences in a wide range of cells. iNOS can cause adverse effects, such as falling blood pressure, if overexpressed. Thus, according to some data, this enzyme is an important precursor of arterial hypertension (AH) and tension-type headache (TTH), which are the most common multifactorial diseases in adults. The purpose of this study was to investigate the association of rs2779249 (chr17:26128581 C>A) and rs2297518 (chr17: chr17:27769571 G>A) of the NOS2 gene with TTH and AH overlap syndrome (OS) in Caucasians in Eastern Siberia. The sample size was 91 participants: the first group-30 patients with OS; the second group-30 patients AH; and the third group-31 healthy volunteers. RT-PCR was used for the determination of alleles and genotypes of the SNPs rs2779249 and rs2297518 of the NOS2 gene in all groups of participants. We showed that the frequency of allele A was significantly higher among patients with AH compared with healthy volunteers (p-value < 0.05). The frequency of the heterozygous genotype CA of rs2779249 was higher in the first group vs. the control (p-value = 0.03), and in the second group vs. the control (p-value = 0.045). The frequency of the heterozygous genotype GA of rs2297518 was higher in the first group vs. the control (p-value = 0.035), and in the second group vs. the control (p-value = 0.001). The allele A of rs2779249 was associated with OS (OR = 3.17 [95% CI: 1.31-7.67], p-value = 0.009) and AH (OR = 2.94 [95% CI: 1.21-7.15], p-value = 0.015) risks compared with the control. The minor allele A of rs2297518 was associated with OS (OR = 4.0 [95% CI: 0.96-16.61], p-value = 0.035) and AH (OR = 8.17 [95% CI: 2.03-32.79], p-value = 0.001) risks compared with the control. Therefore, our pilot study demonstrated that the SNPs rs2779249 and rs229718 of the NOS2 gene could be promising genetic biomarkers for this OS risk in Caucasians from Eastern Siberia.

Identification of rare missense mutations in the glutamate ionotropic receptor AMPA type subunit genes in schizophrenia

OBJECTIVE

The alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors significantly regulate the synaptic transmission and functions of various synaptic receptors. This study aimed to identify single nucleotide mutations in the glutamate receptor, ionotropic, AMPA type (GRIA) gene family, which is associated with schizophrenia.

METHODS

The exon regions of four genes (GRIA1, GRIA2, GRIA3, and GRIA4) encoding glutamate ionotropic receptor AMPA type proteins were resequenced in 516 patients with schizophrenia. We analyzed the protein function of the identified rare mutants via immunoblotting.

RESULTS

A total of 24 coding variants were detected in the GRIA gene family, including six missense mutations, 17 synonymous mutations, and one frameshift insertion. Notably, three ultra-rare missense mutations (GRIA1p.V182A, GRIA2p.P123Q, and GRIA4p.Y491H) were not documented in the single nucleotide polymorphism database, gnomAD genomes, and 1517 healthy controls available from Taiwan BioBank. Immunoblotting revealed GRIA4p.Y491H mutant with altered protein expressions in cultured cells compared with the wild type.

CONCLUSION

Our findings suggest that, in some patients affected by schizophrenia, the GRIA gene family harbors rare functional mutations, which support rare coding variants that could contribute to the genetic architecture of this illness. The in-vitro impacts of these rare pathological mutations on the pathophysiology of schizophrenia are worthy of future investigation.

Genome-Wide Analysis of Rare Haplotypes Associated with Breast Cancer Risk

Numerous common genetic variants have been linked to breast cancer risk, but they only partially explain the total breast cancer heritability. Inference from Nordic population-based twin data indicates rare high-risk loci as the chief determinant of breast cancer risk. Here, we use haplotypes, rather than single variants, to identify rare high-risk loci for breast cancer. With computationally phased genotypes from 181,034 white British women in the UK Biobank, a genome-wide haplotype-breast cancer association analysis was conducted using sliding windows of 5 to 500 consecutive array-genotyped variants. In the discovery stage, haplotype-breast cancer associations were evaluated retrospectively in the prestudy-enrollment data including 5,487 breast cancer cases. Breast cancer hazard ratios (HR) for additive haplotypic effects were estimated using Cox regression. The replication analysis included a prospective cohort of women free of breast cancer at enrollment, of whom 3,524 later developed breast cancer. This two-stage analysis detected 13 rare loci (frequency <1%), each associated with an appreciable breast cancer-risk increase (discovery: HRs = 2.84-6.10, P < 5 × 10-8; replication: HRs = 2.08-5.61, P < 0.01). In contrast, the variants that formed these rare haplotypes individually exhibited much smaller effects. Functional annotation revealed extensive cis-regulatory DNA elements in breast cancer-related cells underlying the replicated rare haplotypes. Using phased, imputed genotypes from 30,064 cases and 25,282 controls in the DRIVE OncoArray case-control study, 6 of the 13 rare-loci associations were found generalizable (odds ratio estimates: 1.48-7.67, P < 0.05). This study demonstrates the complementary advantage of utilizing rare haplotypes to capture novel risk loci and suggests the potential for the discovery of more genetic elements contributing to cancer heritability as large data sets of germline whole-genome sequencing become available.

SIGNIFICANCE

A genome-wide two-stage haplotype analysis identifies rare haplotypes associated with breast cancer risk and suggests that the rare risk haplotypes represent long-range interactions with regulatory consequences influencing cancer risk.

A fast and efficient approach for gene-based association studies of ordinal phenotypes

Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.

Exploiting family history in aggregation unit-based genetic association tests

The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

The NESHIE and CP Genetics Resource (NCGR): A database of genes and variants reported in neonatal encephalopathy with suspected hypoxic ischemic encephalopathy (NESHIE) and consequential cerebral palsy (CP)

Neonatal encephalopathy (NE) with suspected hypoxic ischaemic encephalopathy (HIE) (NESHIE) is a complex syndrome occurring in newborns, characterised by altered neurological function. It has been suggested that genetic variants may influence NESHIE susceptibility and outcomes. Unlike NESHIE, for which a limited number of genetic studies have been performed, many studies have identified genetic variants associated with cerebral palsy (CP), which can develop from severe NESHIE. Identifying variants in patients with CP, as a consequence of NESHIE, may provide a starting point for the identification of genetic variants associated with NESHIE outcomes. We have constructed NCGR (NESHIE and CP Genetics Resource), a database of genes and variants reported in patients with NESHIE and CP (where relevant to NESHIE), for the purpose of collating and comparing genetic findings between the two conditions. In this paper we describe the construction and functionality of NCGR. Furthermore, we demonstrate how NCGR can be used to prioritise genes and variants of potential clinical relevance that may underlie a genetic predisposition to NESHIE and contribute to an understanding of its pathogenesis.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Mitochondrial DNA variation in Parkinson’s disease: Analysis of “out-of-place” population variants as a risk factor

Mitochondrial DNA (mtDNA), a potential source of mitochondrial dysfunction, has been implicated in Parkinson’s disease (PD). However, many previous studies investigating associations between mtDNA population variation and PD reported inconsistent or contradictory findings. Here, we investigated an alternative hypothesis to determine whether mtDNA variation could play a significant role in PD risk. Emerging evidence suggests that haplogroup-defining mtDNA variants may have pathogenic potential if they occur “out-of-place” on a different maternal lineage. We hypothesized that the mtDNA of PD cases would be enriched for out-of-place variation in genes encoding components of the oxidative phosphorylation complexes. We tested this hypothesis with a unique dataset comprising whole mitochondrial genomes of 70 African ancestry PD cases, two African ancestry control groups (n = 78 and n = 53) and a replication group of 281 European ancestry PD cases and 140 controls from the Parkinson’s Progression Markers Initiative cohort. Significantly more African ancestry PD cases had out-of-place variants than controls from the second control group (P < 0.0125), although this association was not observed in the first control group nor the replication group. As the first mtDNA study to include African ancestry PD cases and to explore out-of-place variation in a PD context, we found evidence that such variation might be significant in this context, thereby warranting further replication in larger cohorts.

Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes

Background

Polygenic risk scores (PRSs) are indices of genetic liability for illness, but their clinical utility for predicting risk for a specific psychiatric disorder is limited. Genetic overlap among disorders and their effects on allied phenotypes may be a possible explanation, but this has been difficult to quantify given focus on singular disorders and/or allied phenotypes.

Methods

We constructed PRSs for 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder) and 3 nonpsychiatric control traits (height, type II diabetes, irritable bowel disease) in the UK Biobank (N = 31,616) and quantified associations between PRSs and phenotypes allied with mental illness: behavioral (symptoms, cognition, trauma) and brain measures from magnetic resonance imaging. We then evaluated the extent of specificity among PRSs and their effects on these allied phenotypes.

Results

Correlations among psychiatric PRSs replicated previous work, with overlap between schizophrenia and bipolar disorder, which was distinct from overlap between autism spectrum disorder and attention-deficit/hyperactivity disorder; overlap between psychiatric and control PRSs was minimal. There was, however, substantial overlap of PRS effects on allied phenotypes among psychiatric disorders and among psychiatric disorders and control traits, where the extent and pattern of overlap was phenotype specific.

Conclusions

Results show that genetic distinctions between psychiatric disorders and between psychiatric disorders and control traits exist, but this does not extend to their effects on allied phenotypes. Although overlap can be informative, work is needed to construct PRSs that will function at the level of specificity needed for clinical application.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

Gene-Based Methods for Estimating the Degree of the Skewness of X Chromosome Inactivation

Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases, and currently several methods have been proposed to estimate the degree of the XCI-S (denoted as γ) for a single locus. However, no method has been available to estimate γ for genes. Therefore, in this paper, we first propose the point estimate and the penalized point estimate of γ for genes, and then derive its confidence intervals based on the Fieller’s and penalized Fieller’s methods, respectively. Further, we consider the constraint condition of γ∈[0, 2] and propose the Bayesian methods to obtain the point estimates and the credible intervals of γ, where a truncated normal prior and a uniform prior are respectively used (denoted as GBN and GBU). The simulation results show that the Bayesian methods can avoid the extreme point estimates (0 or 2), the empty sets, the noninformative intervals ([0, 2]) and the discontinuous intervals to occur. GBN performs best in both the point estimation and the interval estimation. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use. In summary, in practical applications, we recommend using GBN to estimate γ of genes.

MMP9 SNP and MMP SNP-SNP interactions increase the risk for ischemic stroke in the Han Hakka population

OBJECTIVES

To investigate the association of eight variants of four matrix metalloproteinase (MMP) genes with ischemic stroke (IS) and whether interactions among these single nucleotide polymorphisms (SNPs) increases the risk of IS.

METHODS

Among 547 patients with ischemic stroke and 350 controls, matrix-assisted laser desorption/ionization time of flight mass spectrometry was used to examine eight variants arising from four different genes, including MMP-1 (rs1799750), MMP-2 (rs243865, rs2285053, rs2241145), MMP-9 (rs17576), and MMP-12 (rs660599, rs2276109, and rs652438). Gene-gene interactions were employed using generalized multifactor dimensionality reduction (GMDR) methods.

RESULTS

The frequency of rs17576 was significantly higher in IS patients than in controls (p = .033). Logistic regression analysis revealed the AG and GG genotypes of rs17576 to be associated with a higher risk for IS, with the odds ratio and 95% confidence interval being 2.490 (1.251-4.959) and 2.494 (1.274-4.886), respectively. GMDR analysis showed a significant SNP-SNP interaction between rs17576 and rs660599 (the testing balanced accuracy was 53.70% and cross-validation consistency was 8/10, p = .0107). Logistic regression analysis showed the interaction between rs17576 and rs660599 to be an independent risk factor for IS with an odds ratio of 1.568 and a 95% confidence interval of 1.152-2.135.

CONCLUSION

An MMP-9 rs17576 polymorphism is associated with increased IS risk in the Han Hakka population and interaction between MMP-9 rs17576 and MMP-12 rs660599 is associated with increased IS risk as well.

How Genetics Can Improve Clinical Practice in Chronic Kidney Disease: From Bench to Bedside

Chronic kidney disease (CKD) is considered a major global health problem with high socio-economic costs: the risk of CKD in individuals with an affected first degree relative has been found to be three times higher than in the general population. Genetic factors are known to be involved in CKD pathogenesis, both due to the possible presence of monogenic pathologies as causes of CKD, and to the role of numerous gene variants in determining susceptibility to the development of CKD. The genetic study of CKD patients can represent a useful tool in the hands of the clinician; not only in the diagnostic and prognostic field, but potentially also in guiding therapeutic choices and in designing clinical trials. In this review we discuss the various aspects of the role of genetic analysis on clinical management of patients with CKD with a focus on clinical applications. Several topics are discussed in an effort to provide useful information for daily clinical practice: definition of susceptibility to the development of CKD, identification of unrecognized monogenic diseases, reclassification of the etiological diagnosis, role of pharmacogenetics.

Challenge accepted: uncovering the role of rare genetic variants in Alzheimer's disease

The search for rare variants in Alzheimer's disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.

Clinical validation of genomic functional screen data: analysis of observed BRCA1 variants in an unselected population cohort

Functional assessment of genomic variants provides a promising approach to systematically examine the potential pathogenicity of variants independent of associated clinical data. However, making such conclusions requires validation with appropriate clinical findings. To this end, here, we use variant calls from exome data and BRCA1-related cancer diagnoses from electronic health records to demonstrate an association between published laboratory-based functional designations of BRCA1 variants and BRCA1-related cancer diagnoses in an unselected cohort of patient-participants. These findings validate and support further exploration of functional assay data to better understand the pathogenicity of rare variants. This information may be valuable in the context of healthy population genomic screening, where many rare, potentially pathogenic variants may not have sufficient associated clinical data to inform their interpretation directly.

Computational study of the impact of nucleotide variations on highly conserved proteins: In the case of actin

Sequencing of individual human genomes enables studying relationship among nucleotide variations, amino acid substitutions, effect on protein structures and diseases. Many studies have found general tendencies, for instance, that pathogenic variations tend to be found in the buried regions of the protein structures, that benign variations tend to be found on the surface of the proteins, and that variations on evolutionary conserved residues tend to be pathogenic. These tendencies were deduced from globular proteins with standard evolutionary changes in amino acid sequences. In this study, we investigated the variation distribution on actin, one of the highly conserved proteins. Many nucleotide variations and three-dimensional structures of actin have been registered in databases. By combining those data, we found that variations buried inside the protein were rather benign and variations on the surface of the protein were pathogenic. This idiosyncratic distribution of the variation impact is likely ascribed to the extensive use of the surface of the protein for protein-protein interactions in actin.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

DNA Methylation and Type 2 Diabetes: Novel Biomarkers for Risk Assessment?

Diabetes is a severe threat to global health. Almost 500 million people live with diabetes worldwide. Most of them have type 2 diabetes (T2D). T2D patients are at risk of developing severe and life-threatening complications, leading to an increased need for medical care and reduced quality of life. Improved care for people with T2D is essential. Actions aiming at identifying undiagnosed diabetes and at preventing diabetes in those at high risk are needed as well. To this end, biomarker discovery and validation of risk assessment for T2D are critical. Alterations of DNA methylation have recently helped to better understand T2D pathophysiology by explaining differences among endophenotypes of diabetic patients in tissues. Recent evidence further suggests that variations of DNA methylation might contribute to the risk of T2D even more significantly than genetic variability and might represent a valuable tool to predict T2D risk. In this review, we focus on recent information on the contribution of DNA methylation to the risk and the pathogenesis of T2D. We discuss the limitations of these studies and provide evidence supporting the potential for clinical application of DNA methylation marks to predict the risk and progression of T2D.

An Investigation of the Role of Common and Rare Variants in a Large Italian Multiplex Family of Multiple Sclerosis Patients

Known multiple sclerosis (MS) susceptibility variants can only explain half of the disease's estimated heritability, whereas low-frequency and rare variants may partly account for the missing heritability. Thus, here we sought to determine the occurrence of rare functional variants in a large Italian MS multiplex family with five affected members. For this purpose, we combined linkage analysis and next-generation sequencing (NGS)-based whole exome and whole genome sequencing (WES and WGS, respectively). The genetic burden attributable to known common MS variants was also assessed by weighted genetic risk score (wGRS). We found a significantly higher burden of common variants in the affected family members compared to that observed among sporadic MS patients and healthy controls (HCs). We also identified 34 genes containing at least one low-frequency functional variant shared among all affected family members, showing a significant enrichment in genes involved in specific biological processes-particularly mRNA transport-or neurodegenerative diseases. Altogether, our findings point to a possible pathogenic role of different low-frequency functional MS variants belonging to shared pathways. We propose that these rare variants, together with other known common MS variants, may account for the high number of affected family members within this MS multiplex family.

Genetic susceptibility to multiple sclerosis: interactions between conserved extended haplotypes of the MHC and other susceptibility regions

Background

To study the accumulation of MS-risk resulting from different combinations of MS-associated conserved-extended-haplotypes (CEHs) of the MHC and three non-MHC “risk-haplotypes” nearby genes EOMES, ZFP36L1, and CLEC16A. Many haplotypes are MS-associated despite having population-frequencies exceeding the percentage of genetically-susceptible individuals. The basis of this frequency-disparity requires explanation.

Methods

The SNP-data from the WTCCC was phased at the MHC and three non-MHC susceptibility-regions. CEHs at the MHC were classified into five haplotype-groups: (HLA-DRB1*15:01 ~ DQB1*06:02 ~ a1)-containing (H +); extended-risk (ER); all-protective (AP); neutral (0); and the single-CEH (c1). MS-associations for different “risk-combinations” at the MHC and other non-MHC “risk-loci” and the appropriateness of additive and multiplicative risk-accumulation models were assessed.

Results

Different combinations of “risk-haplotypes” produce a final MS-risk closer to additive rather than multiplicative risk-models but neither model was consistent. Thus, (H +)-haplotypes had greater impact when combined with (0)-haplotypes than with (H +)-haplotypes, whereas, (H +)-haplotypes had greater impact when combined with a (c1)-haplotypes than with (0)-haplotypes. Similarly, risk-genotypes (0,H +), (c1,H +), (H + ,H +) and (0,c1) were additive with risks from non-MHC risk-loci, whereas risk-genotypes (ER,H +) and (AP,c1) were unaffected.

Conclusions

Genetic-susceptibility to MS is essential for MS to develop but actually developing MS depends heavily upon both an individual’s particular combination of “risk-haplotypes” and how these loci interact.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01018-6.

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank

BACKGROUND

Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer.

METHODS

We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings.

RESULTS

We identified 133 significant pathway-cancer pairs (85 replicated) and 90 significant gene-cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05).

CONCLUSIONS

In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers.

IMPACT

This work adds to the knowledge base and progress being made in precision medicine.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

An analysis of the demographic history of the risk allele R4810K in RNF213 of moyamoya disease

BACKGROUND

Ring finger protein 213 (RNF213) is a susceptibility gene of moyamoya disease (MMD). A previous case-control study and a family analysis demonstrated a strong association of the East Asian-specific variant, R4810K (rs112735431), with MMD. Our aim is to uncover evolutionary history of R4810K in East Asian populations.

METHODS

The RNF213 locus of 24 MMD patients in Japan were sequenced using targeted-capture sequencing. Based on the sequence data, we conducted population genetic analysis and estimated the age of R4810K using coalescent simulation.

RESULTS

The diversity of the RNF213 gene was higher in Africans than non-Africans, which can be explained by bottleneck effect of the out-of-Africa migration. Coalescent simulation showed that the risk variant was born in East Asia 14,500-5100 years ago and came to the Japanese archipelago afterward, probably in the period when the known migration based on archaeological evidences occurred.

CONCLUSIONS

Although clinical data show that the symptoms varies, all sequences harboring the risk allele are almost identical with a small number of exceptions, suggesting the MMD phenotypes are unaffected by the variants of this gene and rather would be more affected by environmental factors.

Coronavirus: Pure Infectious Disease or Genetic Predisposition

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes novel coronavirus disease (COVID-19), is the seventh pathogenic coronavirus recently discovered in December 2019 in Wuhan, China. To date, our knowledge about its effect on the human host remains limited. It is well known that host genetic factors account for the individual differences in the susceptibility to infectious diseases. The genetic susceptibility factors to COVID-19 and its severity are associated with several unanswered questions. However, the experience gained from an earlier strain of coronavirus, SARS-CoV-1, which shows 78% genetic similarity to SARS-CoV-2 and uses the same receptor to bind to host cells, could provide some clues. It, therefore, seems possible to assemble new evidence in order to solve a potential genetic predisposition puzzle for COVID-19. In this chapter, the puzzle pieces, including virus entry receptors, immune response, and inflammation-related genes, as well as the probable genetic predisposition models to COVID-19, are discussed.

The impact of rare and low-frequency genetic variants in common variable immunodeficiency (CVID)

Next Generation Sequencing (NGS) has uncovered hundreds of common and rare genetic variants involved in complex and rare diseases including immune deficiencies in both an autosomal recessive and autosomal dominant pattern. These rare variants however, cannot be classified clinically, and common variants only marginally contribute to disease susceptibility. In this study, we evaluated the multi-gene panel results of Common Variable Immunodeficiency (CVID) patients and argue that rare variants located in different genes play a more prominent role in disease susceptibility and/or etiology. We performed NGS on DNA extracted from the peripheral blood leukocytes from 103 patients using a panel of 19 CVID-related genes: CARD11, CD19, CD81, ICOS, CTLA4, CXCR4, GATA2, CR2, IRF2BP2, MOGS, MS4A1, NFKB1, NFKB2, PLCG2, TNFRSF13B, TNFRSF13C, TNFSF12, TRNT1 and TTC37. Detected variants were evaluated and classified based on their impact, pathogenicity classification and population frequency as well as the frequency within our study group. NGS revealed 112 different (a total of 227) variants with under 10% population frequency in 103 patients of which 22(19.6%) were classified as benign, 29(25.9%) were classified as likely benign, 4(3.6%) were classified as likely pathogenic and 2(1.8%) were classified as pathogenic. Moreover, 55(49.1%) of the variants were classified as variants of uncertain significance. We also observed different variant frequencies when compared to population frequency databases. Case-control data is not sufficient to unravel the genetic etiology of immune deficiencies. Thus, it is important to understand the incidence of co-occurrence of two or more rare variants to aid in illuminating their potential roles in the pathogenesis of immune deficiencies.

Genetic Analysis Reveals Rare Variants in T-Cell Response Gene MR1 Associated with Poor Overall Survival after Urothelial Cancer Diagnosis

Urothelial carcinoma of the bladder (UC) is the fifth most common cancer in the United States. Germline variants, especially rare germline variants, may account for a portion of the disparity seen among patients in terms of UC incidence, presentation, and outcomes. The objectives of this study were to identify rare germline variant associations in UC incidence and to determine its association with clinical outcomes. Using exome sequencing data from the DiscovEHR UC cohort (n = 446), a European-ancestry, North American population, the complex influence of germline variants on known clinical phenotypes were analyzed using dispersion and burden metrics with regression tests. Outcomes measured were derived from the electronic health record (EHR) and included UC incidence, age at diagnosis, and overall survival (OS). Consequently, key rare variant association genes were implicated in MR1 and ADGRL2. The Kaplan-Meier survival analysis reveals that individuals with MR1 germline variants had significantly worse OS than those without any (log-rank p-value = 3.46 × 10^-7). Those with ADGRL2 variants were found to be slightly more likely to have UC compared to a matched control cohort (FDR q-value = 0.116). These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.

Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

After the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

Background

Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders.

Methods

Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed.

Results

The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a “long-with-young” and “short-with-all” basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10⁻¹⁰), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs.

Conclusions

In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential “Common Disease-Hotspot Variant” approach to the missing heritability problem.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00318-3.

Evolutionary demographic models reveal the strength of purifying selection on susceptibility alleles to late-onset diseases

Assessing the role played by purifying selection on a susceptibility allele to late-onset disease (SALOD) is crucial to understanding the puzzling allelic spectrum of a disease, because most alleles are recent and rare. This fact is surprising because it suggests that alleles are under purifying selection while those that are involved in post-menopause mortality are often considered neutral in the genetic literature. The aim of this article is to use an evolutionary demography model to assess the magnitude of selection on SALODs while accounting for epidemiological and sociocultural factors. We develop an age-structured population model allowing for the calculation of SALOD selection coefficients (1) for a large and realistic parameter space for disease onset, (2) in a two-sex model in which men can reproduce in old age and (3) for situations in which child survival depends on maternal, paternal and grandmaternal care. The results show that SALODs are under purifying selection for most known age-at-onset distributions of late-onset genetic diseases. Estimates regarding various genes involved in susceptibility to cancer or Huntington's disease demonstrate that negative selection largely overcomes the effects of drift in most human populations. This is also probably true for neurodegenerative or polycystic kidney diseases, although sociocultural factors modulate the effect of selection in these cases. We conclude that neutrality is probably the exception among alleles that have a deleterious effect in old age and that accounting for sociocultural factors is required to understand the full extent of the force of selection shaping senescence in humans.

Genetics of Alzheimer's disease

Alzheimer’s disease (AD) is the leading cause of neurodegeneration in the elderly and is clinically characterized by slowly progressing cognitive decline, which most commonly affects episodic memory function. This eventually leads to difficulties in activities of daily living. Biomarker studies show that the underlying pathology of AD begins 20 years before clinical symptoms. This results in the need to define specific targets and preclinical stages in order to address the problems of this disease at an earlier point in time. Genetic studies are indispensable for gaining insight into the etiology of neurodegenerative diseases and can play a major role in the early definition of the individual disease risk. This review provides an overview of the currently known genetic features of AD.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Examining the effect of mitochondrial DNA variants on blood pressure in two Finnish cohorts

High blood pressure (BP) is a major risk factor for many noncommunicable diseases. The effect of mitochondrial DNA single-nucleotide polymorphisms (mtSNPs) on BP is less known than that of nuclear SNPs. We investigated the mitochondrial genetic determinants of systolic, diastolic, and mean arterial BP. MtSNPs were determined from peripheral blood by sequencing or with genome-wide association study SNP arrays in two independent Finnish cohorts, the Young Finns Study and the Finnish Cardiovascular Study, respectively. In total, over 4200 individuals were included. The effects of individual common mtSNPs, with an additional focus on sex-specificity, and aggregates of rare mtSNPs grouped by mitochondrial genes were evaluated by meta-analysis of linear regression and a sequence kernel association test, respectively. We accounted for the predicted pathogenicity of the rare variants within protein-encoding and the tRNA regions. In the meta-analysis of 87 common mtSNPs, we did not observe significant associations with any of the BP traits. Sex-specific and rare-variant analyses did not pinpoint any significant associations either. Our results are in agreement with several previous studies suggesting that mtDNA variation does not have a significant role in the regulation of BP. Future studies might need to reconsider the mechanisms thought to link mtDNA with hypertension.

Genetic and Non-genetic Determinants of Cardiovascular Disease in South Asians

South Asians (SAs), people from the Indian subcontinent (e.g., India, Pakistan, Bangladesh, Sri Lanka, and Nepal) have a higher prevalence of cardiovascular disease (CVD) and suffer from a greater risk of CVD-associated mortality compared to other global populations. These problems are compounded by the alterations in lifestyles due to urbanization and changing cultural, social, economic, and political environments. Current methods of CV risk prediction are based on white populations that under-estimate the CVD risk in SAs. Prospective studies are required to obtain actual CVD morbidity/mortality rates so that comparisons between predicted CVD risk can be made with actual events. Overwhelming data support a strong influence of genetic factors. Genome-Wide Association Studies (GWAS) serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence CVD is still unclear. It is difficult to predict the potential implication of these findings in clinical settings. This review provides a systematic assessment of the risk factors, genetics, and environmental causes of CV health disparity in SAs, and highlights progress made in clinical and genomics discoveries in the rapidly evolving field, which has the potential to show clinical relevance in the near future.

Role of environmentally induced epigenetic transgenerational inheritance in evolutionary biology: Unified Evolution Theory

The current evolutionary biology theory primarily involves genetic alterations and random DNA sequence mutations to generate the phenotypic variation required for Darwinian natural selection to act. This neo-Darwinian evolution is termed the Modern Evolution Synthesis and has been the primary paradigm for nearly 100 years. Although environmental factors have a role in neo-Darwinian natural selection, Modern Evolution Synthesis does not consider environment to impact the basic molecular processes involved in evolution. An Extended Evolutionary Synthesis has recently developed that extends the modern synthesis to consider non-genetic processes. Over the past few decades, environmental epigenetics research has been demonstrated to regulate genetic processes and directly generate phenotypic variation independent of genetic sequence alterations. Therefore, the environment can on a molecular level through non-genetic (i.e. epigenetic) mechanisms directly influence phenotypic variation, genetic variation, inheritance and adaptation. This direct action of the environment to alter phenotype that is heritable is a neo-Lamarckian concept that can facilitate neo-Darwinian (i.e. Modern Synthesis) evolution. The integration of genetics, epigenetics, Darwinian theory, Lamarckian concepts, environment, and epigenetic inheritance provides a paradigm shift in evolution theory. The role of environmental-induced epigenetic transgenerational inheritance in evolution is presented to describe a more unified theory of evolutionary biology.

A Unifying Theory for Autism: The Pathogenetic Triad as a Theoretical Framework

This paper presents a unifying theory for autism by applying the framework of a pathogenetic triad to the scientific literature. It proposes a deconstruction of autism into three contributing features (an autistic personality dimension, cognitive compensation, and neuropathological risk factors), and delineates how they interact to cause a maladaptive behavioral phenotype that may require a clinical diagnosis. The autistic personality represents a common core condition, which induces a set of behavioral issues when pronounced. These issues are compensated for by cognitive mechanisms, allowing the individual to remain adaptive and functional. Risk factors, both exogenous and endogenous ones, show pathophysiological convergence through their negative effects on neurodevelopment. This secondarily affects cognitive compensation, which disinhibits a maladaptive behavioral phenotype. The triad is operationalized and methods for quantification are presented. With respect to the breadth of findings in the literature that it can incorporate, it is the most comprehensive model yet for autism. Its main implications are that (1) it presents the broader autism phenotype as a non-pathological core personality domain, which is shared across the population and uncoupled from associated features such as low cognitive ability and immune dysfunction, (2) it proposes that common genetic variants underly the personality domain, and that rare variants act as risk factors through negative effects on neurodevelopment, (3) it outlines a common pathophysiological mechanism, through inhibition of neurodevelopment and cognitive dysfunction, by which a wide range of endogenous and exogenous risk factors lead to autism, and (4) it suggests that contributing risk factors, and findings of immune and autonomic dysfunction are clinically ascertained rather than part of the core autism construct.