Genome-wide investigation to assess copy number variants in the Italian local chicken population

Background Copy number variants(CNV)hold significant functional and evolutionary importance.Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock.High-density chips have enabled the detection of CNV with increased resolution,leading to the identification of even small CNV.This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome.Results Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV.The majority of CNV in the investigated breeds were of types 0 and 1,and the minimum length of CNV was significantly larger than that reported in previous studies.Interestingly,a high proportion of the length of chromosome 16 was covered by copy number variation regions(CNVR),with the major histocompatibility complex being the likely cause.Among the genes identified within CNVR,only those present in at least five animals across breeds(n=95)were discussed to reduce the focus on redundant CNV.Some of these genes have been associated to functional traits in chickens.Notably,several CNVR on different chromosomes harbor genes related to muscle development,tissue-specific biological processes,heat stress resistance,and immune response.Quantitative trait loci(QTL)were also analyzed to investigate potential overlapping with the identified CNVR:54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size,carcass characteristics,egg production,egg components,fat deposition,and feed intake.Conclusions The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

Exploring correlations among copy number variants

There have been a great many recent studies investigating the extent of Copy Number Variation in the genomes of various species such as human, cattle, dogs and many others. The results from these studies indicate that the extent of the Copy Number Variation in the genome is considerable, and that in humans and in cattle, frequencies of different Copy Number Variants may differ in different breeds/ethnicities. This is not entirely unexpected as allele frequencies of certain loci vary with different breeds/ ethnicities/species and many known Copy Number Variants behave similarly to ordinary markers as regards Mendelian segregation. It is also well known in many instances, species/breeds/ethnicities show variation not only in marker allele frequencies, but also in the extent of Linkage Disequilibrium between markers. Thus it is worth investigating the extent of association between Copy Number Variants in different populations. In this paper we will investigate the extent of correlations between selected Copy Number Variants in different human populations and show that statistically significant correlations exist and are strongly population dependent.

Effectiveness of machine learning at modeling the relationship between Hi-C data and copy number variation

Copy number variation(CNV)refers to the number of copies of a specific sequence in a genome and is a type of chromatin structural variation.The development of the Hi-C technique has empowered research on the spatial structure of chromatins by capturing interactions between DNA fragments.We utilized machine-learning methods including the linear transformation model and graph convolutional network(GCN)to detect CNV events from Hi-C data and reveal how CNV is related to three-dimensional interactions between genomic fragments in terms of the one-dimensional read count signal and features of the chromatin structure.The experimental results demonstrated a specific linear relation between the Hi-C read count and CNV for each chromosome that can be well qualified by the linear transformation model.In addition,the GCN-based model could accurately extract features of the spatial structure from Hi-C data and infer the corresponding CNV across different chromosomes in a cancer cell line.We performed a series of experiments including dimension reduction,transfer learning,and Hi-C data perturbation to comprehensively evaluate the utility and robustness of the GCN-based model.This work can provide a benchmark for using machine learning to infer CNV from Hi-C data and serves as a necessary foundation for deeper understanding of the relationship between Hi-C data and CNV.

Fertility,genome stability,and homozygosity in a diverse set of resynthesized rapeseed lines

Rapeseed(Brassica napus,AACC)was formed by hybridization between progenitor species Brassica rapa(AA)and Brassica oleracea(CC).As a result of a limited number of hybridization events between specific progenitor genotypes and strong breeding selection for oil quality traits,rapeseed has limited genetic diversity.The production of resynthesized B.napus lines via interspecific hybridization of the diploid progenitor species B.rapa and B.oleracea is one possible way to increase genetic variation in rapeseed.However,most resynthesized lines produced so far have been reported to be meiotically unstable and infertile,in contrast to established B.napus cultivars.This hinders both maintenance and use of this germplasm in breeding programs.We characterized a large set of 140 resynthesized lines produced by crosses between B.rapa and B.oleracea,as well as between B.rapa and wild C genome species(B.incana,B.hilarionis,B.montana,B.Bourgeaui,B.villosa and B.cretica)for purity(homozygosity),fertility,and genome stability.Self-pollinated seed set,seeds per ten pods as well as percentage pollen viability were used to estimate fertility.SNP genotyping was performed using the Illumina Infinium Brassica 60K array for 116 genotypes,with at least three individuals per line.Most of the material which had been advanced through multiple generations was no longer pure,with heterozygosity detected corresponding to unknown parental contributions via outcrossing.Fertility and genome stability were both genotypedependent.Most lines had high numbers of copy number variants(CNVs),indicative of meiotic instability,and high numbers of CNVs were significantly associated with reduced fertility.Eight putatively stable resynthesized B.napus lines were observed.Further investigation of these lines may reveal the mechanisms underlying this effect.Our results suggest that selection of stable resynthesized lines for breeding purposes is possible.

Insights into the genetic architecture of congenital heart disease from animal modeling

Congenital heart disease(CHD)is observed in up to 1%of live births and is one of the leading causes of mortality from birth defects.While hundreds of genes have been implicated in the genetic etiology of CHD,their role in CHD pathogenesis is still poorly understood.This is largely a reflection of the sporadic nature of CHD,as well as its variable expressivity and incomplete penetrance.We reviewed the monogenic causes and evidence for oligogenic etiology of CHD,as well as the role of de novo mutations,common variants,and genetic modifiers.For further mechanistic insight,we leveraged single-cell data across species to investigate the cellular expression characteristics of genes implicated in CHD in developing human and mouse embryonic hearts.Understanding the genetic etiology of CHD may enable the application of precision medicine and prenatal diagnosis,thereby facilitating early intervention to improve outcomes for patients with CHD.

Deletions in the genomes of fifteen inbred mouse lines and their possible implications for fat accumulation

Copy number variants (CNVs) are pieces of genomic DNA of 1000 base pairs or longer which occur in a given genome at a different frequency than in a reference genome. Their importance as a source for phenotypic variability has been recognized only in the last couple of years. Chromosomal deletions can be seen as a special case of CNVs where stretches of DNA are missing in certain lines when compared to the reference genome of the mouse line C57BL/6, for example. Based upon more than 8 million single nucleotide polymorphisms (SNPs) in the fifteen inbred mouse lines which were determined in a whole genome chip based resequencing project by Perlegen Sciences, we detected 20166 such long chromosomal deletions. They cover altogether between 4.4 million and 8.8 million base pairs, depending on the mouse line. Thus, their extent is comparable to that of SNPs. The chromosomal deletions were found by searching for clusters of missing values in the genotyping data by applying bioinformatics and biostatistical methods. In contrast to isolated missing values, clusters are likely the consequence of missing DNA probe rather than of a failed hybridization or deficient oligos. We analyzed these deletion sites in various ways. Twenty-two percent of these deletion sites overlap with exons; they could therefore affect a gene's functioning. The corresponding genes seem to exist in alternative forms, a phenomenon that reminds of the alternative forms of mRNA generated during gene splicing. We furthermore detected statistically significant association between hundreds of deletion sites and fat weight at the age of eight weeks.

JAX-CNV:A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level

We aimed to develop a whole-genome sequencing(WGS)-based copy number variant(CNV)calling algorithm with the potential of replacing chromosomal microarray assay(CMA)for clinical diagnosis.JAX-CNV is thus developed for CNV detection from WGS data.The performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 samples.The result showed that JAX-CNV recalled 100%of these CNVs.Besides,JAX-CNV identified an average of 30 CNVs per individual,representing an approximately seven-fold increase compared to calls of clinically validated CMAs.Experimental validation of 24 randomly selected CNVs showed one false positive,i.e.,a false discovery rate(FDR)of 4.17%.A robustness test on lowercoverage data revealed a 100%sensitivity for CNVs larger than 300 kb(the current threshold for College of American Pathologists)down to 10×coverage.For CNVs larger than 50 kb,sensitivities were 100%for coverages deeper than 20×,97%for 15×,and 95%for 10×.We developed a WGS-based CNV pipeline,including this newly developed CNV caller JAX-CNV,and found it capable of detecting CMA-reported CNVs at a sensitivity of 100%with about a FDR of 4%.We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS.JAX-CNV is available at https://github.com/TheJacksonLaboratory/JAX-CNV.

The genetic architecture of autism spectrum disorders(ASDs) and the potential importance of common regulatory genetic variants

Currently, there is great interest in identifying genetic variants that contribute to the risk of developing autism spectrum disor- ders （ASDs）, due in part to recent increases in the frequency of diagnosis of these disorders worldwide. While there is nearly universal agreement that ASDs are complex diseases, with multiple genetic and environmental contributing factors, there is less agreement concerning the relative importance of common vs rare genetic variants in ASD liability. Recent observations that rare mutations and copy number variants （CNVs） are frequently associated with ASDs, combined with reduced fecundity of individuals with these disorders, has led to the hypothesis that ASDs are caused primarily by de novo or rare genetic muta- tions. Based on this model, large-scale whole-genome DNA sequencing has been proposed as the most appropriate method for discovering ASD liability genes. While this approach will undoubtedly identity many novel candidate genes and produce important new insights concerning the genetic causes of these disorders, a full accounting of the genetics of ASDs will be incomplete absent an understanding of the contributions of common regulatory variants, which are likely to influence ASD liability by modifying the effects of rare variants or, by assuming unfavorable combinations, directly produce these disorders. Because it is not yet possible to identify regulatory genetic variants by examination of DNA sequences alone, their identitication will require experimentation. In this essay, I discuss these issues and describe the advantages of measurements of allelic expression imbalance （AEI） of mRNA expression for identifying cis-acting regulatory variants that contribute to ASDs.

Genetic architecture, epigenetic influence and environment exposure in the pathogenesis of Autism

Autism spectrum disorder(ASD) is a spectral neurodevelopment disorder affecting approximately 1% of the population. ASD is characterized by impairments in reciprocal social interaction, communication deficits and restricted patterns of behavior. Multiple factors, including genetic/genomic, epigenetic/epigenomic and environmental, are thought to be necessary for autism development. Recent reviews have provided further insight into the genetic/genomic basis of ASD. It has long been suspected that epigenetic mechanisms, including DNA methylation, chromatin structures and long non-coding RNAs may play important roles in the pathology of ASD. In addition to genetic/genomic alterations and epigenetic/epigenomic influences, environmental exposures have been widely accepted as an important role in autism etiology, among which immune dysregulation and gastrointestinal microbiota are two prominent ones.

The complex genetics in autism spectrum disorders

Autism spectrum disorders(ASD) are a pervasive neurodevelopmental disease characterized by deficits in social interaction and nonverbal communication, as well as restricted interests and stereotypical behavior. Genetic changes/heritability is one of the major contributing factors, and hundreds to thousands of causative and susceptible genes, copy number variants(CNVs), linkage regions, and micro RNAs have been associated with ASD which clearly indicates that ASD is a complex genetic disorder. Here, we will briefly summarize some of the high-confidence genetic changes in ASD and their possible roles in their pathogenesis.