Linkage Disequilibrium Research Articles

Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations

BackgroundGenome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows.ResultsWe used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05).ConclusionsWe identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study.

Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.

Single nucleotide polymorphism profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions.

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around quantitative trait nucleotides (QTNs) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTNs are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

Further screening of SNP loci of eggshell translucency related genes and evaluation of genetic effects

Eggshell translucency is a widespread issue in the field of egg quality. Previous research has established that the heritability of eggshell translucency is relatively low or moderate. Scientists have also successfully identified SNP loci related to eggshell translucency on different chromosomes by using gene chips and single-variant GWAS. However, the specific impact of single or multiple genes on the trait of eggshell translucency remains unknown. In an effort to investigate this, we examined 170 SNPs associated with eggshell translucency obtained by our research group. We selected 966 half-sibling laying hens from 2 generations in 3 pure lines: Dwarf Layer-White, Rhode Island Red-White Strain, and Rhode Island Red. Eggs were collected from each hen over a period of 5 consecutive days, and eggshell translucency was measured using a grading method in which the hens were divided into 2 groups: an opaque group and a translucent group. We collected blood samples from the laying hens and extracted DNA. Time of flight mass spectrometry (TOF-MS) was used for genotyping to identify SNP loci that influence the trait of eggshell translucency. The results of our analysis revealed that using TOF-MS in 3 chicken strains, we were able to eliminate loci with low gene polymorphism, genetic effect contribution less than 1%, and deviation from Hardy-Weinberg equilibrium. Ultimately, 5 SNPs (Affx-50362599, rs15050262, rs312943734, rs316121113, and rs317389181) were identified on chromosomes 1, 5, and 19. Additionally, nine candidate genes (DCN, BTG1, ZFP92, POU2F1, NUCB2, FTL, GGNBP2, ACACA, and TADA2A) were found to be associated with these SNPs. No linkage disequilibrium relationship was observed between the 2 pairs of SNP loci on chromosomes 1 and 19. Based on previous studies on the formation mechanism of eggshell translucency, we hypothesize that NUCB2, FTL, and ACACA genes may be affecting the eggshell structure through different mechanisms, such as increase the water permeability or make thin of eggshell membrane, which promote moisture or part of other egg contents and ultimately lead to the formation of eggshell translucency. These findings validate and identify five SNP loci that regulate the translucency trait, and provide molecular markers for breeding non-translucent populations. Furthermore, this study serves as a reference for further investigation of the genetic regulatory mechanisms underlying eggshell translucency.

Molecular genetic identification of Wuzhishan ant chicken, a newly discovered resource in China.

The Wuzhishan ant (MY) chicken exhibits significant differences from other chicken breeds. However, the molecular genetic relationship between the MY breed and other chicken breeds has not been assessed. Whole-genome resequencing was used to compare genetic diversity, nucleotide diversity, the fixation index, the linkage disequilibrium coefficient, and phylogenetic tree relationships between the MY breed and the Wenchang (WC), Danzhou (DZ), Bawangling (BW), and Longsheng Feng (LF) breeds. A total of 21,586,378 singlenucleotide polymorphisms and 4,253,341 insertions/deletions were screened out among the five breeds. The MY breed had the second highest genomic genetic diversity and nucleotide diversity and the lowest LD coefficient among the five breeds. Moreover, the phylogenetic tree analysis showed that individual birds of each breed clustered together with those of their respective breeds. Our data indicated that the MY breed is distinct from the other breeds and can be considered a new genetic resource.

Structural variants contribute to phenotypic variation in maize.

Comprehensively identifying the loci shaping trait variation has been challenging, in part because standard approaches often miss many types of genetic variants. Structural variants, especially transposable elements are likely to affect phenotypic variation but we need better methods in maize for detecting polymorphic structural variants and TEs using short-read sequencing data. Here, we used a whole genome alignment between two maize genotypes to identify polymorphic structural variants and then genotyped a large maize diversity panel for these variants using short-read sequencing data. We characterized variation of SVs within the panel and identified SV polymorphisms that are associated with life history traits and genotype-by-environment interactions. While most of the SVs associated with traits contained TEs, only one of the SV's boundaries clearly matched TE breakpoints indicative of a TE insertion, whereas the other polymorphisms were likely caused by deletions. All of the SVs associated with traits were in linkage disequilibrium with nearby single nucleotide polymorphisms (SNPs), suggesting that this method did not identify variants that would have been missed in a SNP association study.

A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding

Genomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values.

Identification of candidate genes associated with primary feathers of tianfu nonghua ducks based on Genome-wide association studies

The primary feathers of ducks have important economic value in the poultry industry. This study quantified the primary feather phenotype of Nonghua ducks, including the primary feathers' length, area, distribution of black spots, and feather symmetry. And genome-wide association analysis was used to screen candidate genes that affect the primary feather traits. The genome-wide association study (GWAS) results identified the genetic region related to feather length (FL) on chromosome 2. Through Linkage disequilibrium (LD) analysis, candidate regions (chr2: 115,246,393-116,501,448 bp) were identified and were further annotated to 5 genes: MRS2, GPLD1, ALDH5A1, KIAA0319, and ATP9B. Secondly, candidate regions related to feather black spots were identified on chromosome 21. Through LD analysis, the candidate regions (chr21: 163,552-2,183,853 bp) were screened and further annotated to 47 genes. Among them, STK4, CCN5, and YWHAB genes were related to melanin-related pathways or pigment deposition, which may be key genes affecting the distribution of black spots on feathers. In addition, we also screened 125 genes on multiple chromosomes that may be related to feather symmetry. Among them, significant SNPs on chromosome 1 were further identified as candidate regions (chr1: 142,118,209-142,223,605 bp) through LD analysis and annotated into 2 genes, TGFBRAP1 and LOC113839965. These results reported the genetic basis of the primary feather from multiple phenotypes, and offered valuable insights into the genetic basis for the growth and development of duck feathers and feather color pattern.

SLRfinder: A method to detect candidate sex-linked regions with linkage disequilibrium clustering.

Despite their critical roles in genetic sex determination, sex chromosomes remain unknown in many non-model organisms, especially those having recently evolved sex-linked regions (SLRs). These evolutionarily young and labile sex chromosomes are important for understanding early sex chromosome evolution but are difficult to identify due to the lack of Y/W degeneration and SLRs limited to small genomic regions. Here, we present SLRfinder, a method to identify candidate SLRs using linkage disequilibrium (LD) clustering, heterozygosity and genetic divergence. SLRfinder does not rely on specific sequencing methods or a specific type of reference genome (e.g., from the homomorphic sex). In addition, the input of SLRfinder does not require phenotypic sexes, which may be unknown from population sampling, but sex information can be incorporated and is necessary to validate candidate SLRs. We tested SLRfinder using various published datasets and compared it to the local principal component analysis (PCA) method and the depth-based method Sex Assignment Through Coverage (SATC). As expected, the local PCA method could not be used to identify unknown SLRs. SATC works better on conserved sex chromosomes, whereas SLRfinder outperforms SATC in analysing labile sex chromosomes, especially when SLRs harbour inversions. Power analyses showed that SLRfinder worked better when sampling more populations that share the same SLR. If analysing one population, a relatively larger sample size (around 50) is needed for sufficient statistical power to detect significant SLR candidates, although true SLRs are likely always top-ranked. SLRfinder provides a novel and complementary approach for identifying SLRs and uncovering additional sex chromosome diversity in nature.

Association between BCL11B gene polymorphisms and age-related hearing loss in the elderly: A case-control study in Qingdao, China.

Age-related hearing loss is a complex disease caused by a combination of genetic and environmental factors, and a study have conducted animal experiments to explore the association between BCL11B heterozygosity and age-related hearing loss. The present study used established genetic models to examine the association between BCL11B gene polymorphisms and age-related hearing loss. A total of 410 older adults from two communities in Qingdao, China, participated in this study. The case group comprised individuals aged ≥ 60 years with age-related hearing loss, and the control group comprised individuals without age-related hearing loss from the same communities. The groups were matched 1:1 for age and sex. The individual characteristics of the participants were analyzed descriptively using the Mann-Whitney U test and the chi-square test. To explore the association between BCL11B gene polymorphisms and age-related hearing loss, conditional logistic regression was performed to construct genetic models for two single-nucleotide-polymorphisms (SNPs) of BCL11B, and haplotype analysis was conducted to construct their haplotype domains. Two SNP sites of the BCL11B gene, four genetic models of rs1152781 (additive, dominant, recessive, and codominant), and five genetic models of rs1152783 (additive, dominant, recessive, codominant, and over dominant) were significantly associated with age-related hearing loss in the models both unadjusted and adjusted for all covariates (P < 0.05). Additionally, a linkage disequilibrium between rs1152781 and rs1152783 was revealed through haplotype analysis. Our study revealed that BCL11B gene polymorphisms were significantly associated with age-related hearing loss.

Computing linkage disequilibrium aware genome embeddings using autoencoders.

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L0 +L1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

A novel tomato interspecific (Solanum lycopersicum var. cerasiforme and Solanum pimpinellifolium) MAGIC population facilitates trait association and candidate gene discovery in untapped exotic germplasm.

We developed a novel eight-way tomato multiparental advanced generation intercross (MAGIC) population to improve the accessibility of tomato relatives genetic resources to geneticists and breeders. The interspecific tomato MAGIC population (ToMAGIC) was obtained by intercrossing four accessions each of Solanum lycopersicum var. cerasiforme and Solanum pimpinellifolium, which are the weedy relative and the ancestor of cultivated tomato, respectively. The eight exotic ToMAGIC founders were selected based on a representation of the genetic diversity and geographical distribution of the two taxa. The resulting MAGIC population comprises 354 lines, which were genotyped using a new 12k tomato single primer enrichment technology panel and yielded 6488 high-quality single-nucleotide polymorphism (SNPs). The genotyping data revealed a high degree of homozygosity, an absence of genetic structure, and a balanced representation of the founder genomes. To evaluate the potential of the ToMAGIC population, a proof of concept was conducted by phenotyping it for fruit size, plant pigmentation, leaf morphology, and earliness. Genome-wide association studies identified strong associations for the studied traits, pinpointing both previously identified and novel candidate genes near or within the linkage disequilibrium blocks. Domesticated alleles for fruit size were recessive and were found, at low frequencies, in wild/ancestral populations. Our findings demonstrate that the newly developed ToMAGIC population is a valuable resource for genetic research in tomato, offering significant potential for identifying new genes that govern key traits in tomato. ToMAGIC lines displaying a pyramiding of traits of interest could have direct applicability for integration into breeding pipelines providing untapped variation for tomato breeding.

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil.

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility

Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.

Genome-wide linkage and association of novel genes and pathways with type 2 diabetes in Italian families

BackgroundType 2 diabetes mellitus (T2D) stands as one of the most prevalent chronic diseases globally, posing substantial health and economic burdens on society. Within the spectrum of T2D, familial cases emerge as a distinct entity characterized by a strong familial clustering of the disease. This phenomenon has long suggested that genetics contributes substantially to T2D susceptibility, motivating extensive research into the genetic determinants of familial T2D. MethodsWe recruited 212 multigenerational Italian families with multiple cases of T2D. The families were genotyped using genomic array (≥ 600k) derived from the UK Biobank Axiom Array platform. Informative markers were tested via Pseudomarker for linkage to and linkage disequilibrium (i.e., linkage joint to association) with T2D across the following models: dominant with complete penetrance (D1), dominant with incomplete penetrance (D2), recessive with complete penetrance (R1), and recessive with incomplete penetrance (R2). ResultsWe identified a total of 566 variants reaching genome-wide significant (P < 0.00005) linkage and/or association to/with the risk of T2D in Italian families. Of the 355 genes identified in our study, 341 (96%) are novel and have not been reported with T2D or any of its related phenotypes (i.e., obesity, metabolic syndrome, insulin resistance, polycystic ovary syndrome, and hyperglycemia). ConclusionOur study constitutes the first familial T2D-linkage and association study in the Italian population. However, the functional relevance of the novel variants and genes reported in our study remains to be explored.

Common and novel haplotype structures between different types of cancer.

Background: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with cancer risk. GWAS data are important for cancer prevention and understanding the underlying mechanisms of cancer. This study aimed to investigate the genetic association between different types of cancer using GWAS data and a bioinformatics approach. The significant GWAS variants associated with more than one cancer type were identified. Common linkage disequilibrium (LD) variants between different types of cancer were identified by 1000 genomes phase 3 LD data. Haplotype blocks were identified by analyzing 1000 Genomes phase 3 genotyping data in the GWAS populations. Subsequent analyses included functional SNP analyses and TCGA gene expression. The results associated with significant GWAS variants (P<5E-8) showed the following haplotype associations in European population: GT rs4808075-rs8170 haplotype on BABAM1 with breast and ovarian cancers, GC rs16857609-rs11693806 haplotype on DIRC3 with breast and thyroid cancers, GCG rs380286-rs401681-rs31487 haplotype on CLPTM1L with skin and lung cancers, GGG rs4430796-rs11651052-rs11263763 haplotype on HNF1B with prostate and endometrial cancers, and GT rs10505477-rs6983267 haplotype on CASC8 associated with colorectal and prostate cancers. All these genes had significantly different expressions in tumor tissues (P<1E-3). In addition, the rs11693806 variant is located in the hsa-miR-873-5p binding site and has an enhancing effect on the hsa-miR-873-5p:DIRC3 interaction. These novel haplotype structures and miRNA:lncRNA interactions are important for understanding the common genetic link between cancers. These results can potentially be used in genetic panels.

The impact of MICB mismatches in unrelated haematopoietic stem cell transplantation.

MICA polymorphisms have been associated with increased incidence of acute GvHD and adverse outcome in allogeneic haematopoietic stem cell transplantation (HSCT). MICB is another expressed member of MHC class I-related chain genes and its impact on HSCT outcome is yet to be fully defined. We typed a large cohort of patients and donors for MICB polymorphisms and investigated the impact of MICB matching on outcome after unrelated HSCT. 69.2% of the patients were 10/10 human leukocyte antigen (HLA) matched and 30.8% were 9/10 HLA matched. MICB typing was performed using a short amplicon-based NGS typing assay on the Illumina MiSeq platform. Differences in proteins were considered as mismatches. MICA polymorphisms were identified as possible confounder and were therefore included as parameter in the multivariate analyses. Due to the strong linkage disequilibrium with the classical HLA-genes, sub-stratification for HLA matching status was necessary, and no effect of MICB mismatches was seen in the 10/10 HLA matched group when compared to the MICB matched cases. However, in the 9/10 HLA matched group, MICB mismatched cases showed significantly worse disease free survival (DFS), GvHD and relapse free survival (GRFS) compared to the MICB matched cases (DFS: HR 1.24, p = 0.011; GRFS: HR 1.26, p = 0.002). MICA mismatches had no impact on any outcome parameter. According to our findings, effects previously attributed to MICA differences may have been confounded by MICB polymorphisms. We show that MICB differences contribute a small but relevant effect in 9/10 HLA-matched transplantations, which in turn highlights the possible usefulness of MICB typing in donor selection among similarly suitable 9/10 matched donors, especially when HLA-B mismatches have to be accepted.