High-density Single Nucleotide Polymorphism Array Research Articles

Natural selection on feralization genes contributed to the invasive spread of wild pigs throughout the United States.

Despite a long presence in the contiguous United States (US), the distribution of invasive wild pigs (Sus scrofa × domesticus) has expanded rapidly since the 1980s, suggesting a more recent evolutionary shift towards greater invasiveness. Contemporary populations of wild pigs represent exoferal hybrid descendants of domestic pigs and European wild boar, with such hybridization expected to enrich genetic diversity and increase the adaptive potential of populations. Our objective was to characterize how genetic enrichment through hybridization increases the invasiveness of populations by identifying signals of selection and the ancestral origins of selected loci. Our study focused on invasive wild pigs within Great Smoky Mountains National Park, which represents a hybrid population descendent from the admixture of established populations of feral pigs and an introduction of European wild boar to North America. Accordingly, we genotyped 881 wild pigs with multiple high-density single-nucleotide polymorphism (SNP) arrays. We found 233 markers under putative selection spread over 79 regions across 16 out of 18 autosomes, which contained genes involved in traits affecting feralization. Among these, genes were found to be related to skull formation and neurogenesis, with two genes, TYRP1 and TYR, also encoding for crucial melanogenesis enzymes. The most common haplotypes associated with regions under selection for the Great Smoky Mountains population were also common among other populations throughout the region, indicating a key role of putatively selective variants in the fitness of invasive populations. Interestingly, many of these haplotypes were absent among European wild boar reference genotypes, indicating feralization through genetic adaptation.

Genome-wide detection of positive and balancing signatures of selection shared by four domesticated rainbow trout populations (Oncorhynchus mykiss).

Evolutionary processes leave footprints along the genome over time. Highly homozygous regions may correspond to positive selection of favorable alleles, while maintenance of heterozygous regions may be due to balancing selection phenomena. We analyzed data from 176 fish from four disconnected domestic rainbow trout populations that were genotyped using a high-density Axiom Trout genotyping 665K single nucleotide polymorphism array, including 20 from the US and 156 from three French lines. Using methods based on runs of homozygosity and extended haplotype homozygosity, we detected signatures of selection in these four populations. Nine genomic regions that included 253 genes were identified as being under positive selection in all four populations Most were located on chromosome 2 but also on chromosomes 12, 15, 16, and 20. In addition, four heterozygous regions that contain 29 genes that are putatively under balancing selection were also shared by the four populations. These were located on chromosomes 10, 13, and 19. Regardless of the homozygous or heterozygous nature of the regions, in each region, we detected several genes that are highly conserved among vertebrates due to their critical roles in cellular and nuclear organization, embryonic development, or immunity. We identified new candidate genes involved in rainbow trout fitness, as well as 17 genes that were previously identified to be under positive selection, 10 of which in other fishes (auts2, atp1b3, zp4, znf135, igf-1α, brd2, col9a2, mrap2, pbx1, and emilin-3). Using material from disconnected populations of different origins allowed us to draw a genome-wide map of signatures of positive selection that are shared between these rainbow trout populations, and to identify several regions that are putatively under balancing selection. These results provide a valuable resource for future investigations of the dynamics of genetic diversity and genome evolution during domestication.

Genomic prediction and genome-wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high-density SNP genotypes in Yunong-black pigs.

Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.

Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss)

BackgroundFlavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels.ResultsThe accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions.ConclusionsCompared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.

Design and validation of high-density SNP array of goats and population stratification of Indian goat breeds

Goats are the supporting pillars of rural economy contributing significantly to meat and milk production in India. It is a species targeted for fulfilling the interdependent goals of poverty reduction and creation of employment for supporting the rural income. The increased demand for goat products necessitates their genetic characterization and improvement to augment the production of native breeds. Bi-allelic, genome wide, densely placed single nucleotide polymorphism (SNP) markers are most suitable for this purpose. This paper describes the design and validation of an Affymetrix Axiom-based high-density (HD) SNP chip for goats. The array was designed using a panel of 225 samples from 15 diverse goat breeds of India. In total, more than 38 million high quality SNPs were subjected to stringent filtering and 626,975 SNPs were finally tiled on the array. The average coverage of SNPs in our chip is one SNP per four kilobase (kb), providing a denser coverage of the goat genome than previously available arrays. The HD chip (Axiom_Cahi) was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. The results revealed 95.83% markers to be highly informative and polymorphic in Indian goats. Multivariate analysis indicated population structuring, as 15 breeds could be segregated using the designed array. Phylogenetic analysis suggested stratification of breeds by geographic proximity. This HD SNP chip for goats is a valuable resource for genomic selection, genome wide association as well as population genetic studies in goats.

Predicting Environmental and Ecological Drivers of Human Population Structure.

Landscape, climate, and culture can all structure human populations, but few existing methods are designed to simultaneously disentangle among a large number of variables in explaining genetic patterns. We developed a machine learning method for identifying the variables which best explain migration rates, as measured by the coalescent-based program MAPS that uses shared identical by descent tracts to infer spatial migration across a region of interest. We applied our method to 30 human populations in eastern Africa with high-density single nucleotide polymorphism array data. The remarkable diversity of ethnicities, languages, and environments in this region offers a unique opportunity to explore the variables that shape migration and genetic structure. We explored more than 20 spatial variables relating to landscape, climate, and presence of tsetse flies. The full model explained ∼40% of the variance in migration rate over the past 56 generations. Precipitation, minimum temperature of the coldest month, and elevation were the variables with the highest impact. Among the three groups of tsetse flies, the most impactful was fusca which transmits livestock trypanosomiasis. We also tested for adaptation to high elevation among Ethiopian populations. We did not identify well-known genes related to high elevation, but we did find signatures of positive selection related to metabolism and disease. We conclude that the environment has influenced the migration and adaptation of human populations in eastern Africa; the remaining variance in structure is likely due in part to cultural or other factors not captured in our model.

Genome-wide association study of alkylresorcinols content in 161 wheat cultivars

Alkylresorcinols (ARs), a group of phenolic lipids exclusively found in wheat, rye and barley among daily foods, are considered to be biomarkers for whole wheat grain intake. Recent studies have gradually revealed their functions and benefits for human health. However, the natural variation of ARs contents (ARC) in bread wheat cultivars in China has not been revealed. This study assayed grain ARC in a panel of 161 wheat cultivars grown in five environments in China and reports significant genotypic and environmental variation. Grain ARC ranged from 321 to 972 μg/g. Cultivars such as Yumai 18 and Yumai 2 had high ARC and thousand kernel weight (TKW) and can be utilized for production of ARs-fortified foods and as parents for breeding. Genome-wide association analysis with high-density single nucleotide polymorphism (SNP) arrays identified 24 and 32 stable loci associated with total ARC and thousand kernel ARC (TKAC), respectively. Loci on chromosome arms 3BL and 7BL, were significant for both ARC and TKAC. This study firstly revealed the extensive variation and genetic architecture of ARC in Chinese bread wheat, providing a theoretical basis for future high-ARC genetic improvement.

Design and validation of a high-density single nucleotide polymorphism array for the Eastern oyster (Crassostrea virginica).

Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster (Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r2 ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.

Development and Evaluation of High-Density SNP Arrays for the Eastern Oyster Crassostrea virginica.

The eastern oyster Crassostrea virginica is a major aquaculture species for the USA. The sustainable development of eastern oyster aquaculture depends upon the continued improvement of cultured stocks through advanced breeding technologies. The Eastern Oyster Breeding Consortium (EOBC) was formed to advance the genetics and breeding of the eastern oyster. To facilitate efficient genotyping needed for genomic studies and selection, the consortium developed two single-nucleotide polymorphism (SNP) arrays for the eastern oyster: one screening array with 566K SNPs and one breeders' array with 66K SNPs. The 566K screening array was developed based on whole-genome resequencing data from 292 oysters from Atlantic and Gulf of Mexico populations; it contains 566,262 SNPs including 47K from protein-coding genes with a marker conversion rate of 48.34%. The 66K array was developed using best-performing SNPs from the screening array, which contained 65,893 oyster SNPs including 22,984 genic markers with a calling rate of 99.34%, a concordance rate of 99.81%, and a much-improved marker conversion rate of 92.04%. Null alleles attributable to large indels were found in 13.1% of the SNPs, suggesting that copy number variation is pervasive. Both arrays provided easy identification and separation of selected stocks from wild progenitor populations. The arrays contain 31 mitochondrial SNPs that allowed unambiguous identification of Gulf mitochondrial genotypes in some Atlantic populations. The arrays also contain 756 probes from 13 oyster and human pathogens for possible detection. Our results show that marker conversion rate is low in high polymorphism species and that the two-step process of array development can greatly improve array performance. The two arrays will advance genomic research and accelerate genetic improvement of the eastern oyster by delineating genetic architecture of production traits and enabling genomic selection. The arrays also may be used to monitor pedigree and inbreeding, identify selected stocks and their introgression into wild populations, and assess the success of oyster restoration.

Development and Evaluation of an AxiomTM 60K SNP Array for Almond (Prunus dulcis).

A high-density single nucleotide polymorphism (SNP) array is essential to enable faster progress in plant breeding for new cultivar development. In this regard, we have developed an Axiom 60K almond SNP array by resequencing 81 almond accessions. For the validation of the array, a set of 210 accessions were genotyped and 82.8% of the SNPs were classified in the best recommended SNPs. The rate of missing data was between 0.4% and 2.7% for the almond accessions and less than 15.5% for the few peach and wild accessions, suggesting that this array can be used for peach and interspecific peach × almond genetic studies. The values of the two SNPs linked to the RMja (nematode resistance) and SK (bitterness) genes were consistent. We also genotyped 49 hybrids from an almond F2 progeny and could build a genetic map with a set of 1159 SNPs. Error rates, less than 1%, were evaluated by comparing replicates and by detection of departures from Mendelian inheritance in the F2 progeny. This almond array is commercially available and should be a cost-effective genotyping tool useful in the search for new genes and quantitative traits loci (QTL) involved in the control of agronomic traits.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays.

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. $\textrm{LmTag}$ is available at: https://github.com/datngu/LmTag.

Genome‐wide association study for desirable traits in the Pacific oysterCrassostrea gigas(Thunberg)

The Pacific oyster, Crassostrea gigas (Thunberg), from the northwest Pacific Ocean has been introduced for aquaculture in many areas. Despite its economic importance, genetic dissection for growth, shell shape and yield traits have not been reported thoroughly. To explore single nucleotide polymorphisms (SNPs) associated with desired traits in Pacific oyster, a genome-wide association study (GWAS) was conducted based on the 190 K high-density SNP array. Growth, shell shape and yield traits, including shell height, condition index and dressing percentage, were studied. Moderate to high phenotypic correlations between shell height and other growth-related traits in the studied wild population were observed (r = 0.38–0.77, p < 0.05), and the dressing percentage was correlated with the condition index (r = 0.6, p < 0.05). The condition index had a moderate positive correlation with glycogen and fat content (r = 0.22–0.35, p < 0.05). The results of GWAS indicated that 11 SNPs were significantly associated with the studied traits. Shell height, body weight and soft body weight were all associated with a significant nonsynonymous SNP (scaffold731_92759), phenotypic variation explanations associated with this single SNP are 8.59%, 8.12% and 8.13%, respectively. This SNP is located in the protein-coding region of the serine protease inhibitor dipetalogastin, which is thought to be involved in food digestion and absorption. This polymorphism may affect the ability of dipetalogastin to inhibit serine proteases, thereby affecting the oyster's growth. This study identified new candidate genes and SNPs related to oyster growth, which is useful for marker-assisted selection.

Rationale and design of the PROspective ATHletic Heart (Pro@Heart) study: long-term assessment of the determinants of cardiac remodelling and its clinical consequences in endurance athletes

BackgroundExercise-induced cardiac remodelling (EICR) results from the structural, functional and electrical adaptations to exercise. Despite similar sports participation, EICR varies and some athletes develop phenotypic features that overlap with cardiomyopathies....

First-degree relationships and genotyping errors deciphered by a high-density SNP array in a Duroc\u2009\xd7\u2009Iberian pig cross

BackgroundTwo individuals with a first-degree relationship share about 50 percent of their alleles. Parent–offspring relationships cannot be homozygous for alternative alleles (genetic exclusion).MethodsApplying the concept of genetic exclusion to HD arrays typed in animals for experimental purposes or genomic selection allows estimation of the rate of rejection of first-degree relationships as the rate at which two individuals typed for a large number of Single Nucleotide Polymorphisms (SNPs) do not share at least one allele. An Expectation–Maximization algorithm is applied to estimate parentage. In addition, genotyping errors are estimated in true parent–offspring relationships. Samples from nine candidate Duroc sires and 55 Iberian dams producing 214 Duroc × Iberian barrows were typed for the HD porcine Affymetrix array.ResultsWe were able to establish paternity and maternity of 75 and 85 piglets, respectively. Rate of rejection in true parent–offspring relationships was estimated as 0.000735. This is a lower bound of the genotyping error since rate of rejection depends on allele frequencies. After accounting for allele frequencies, our estimate of the genotyping error is 0.6%. A total of 7,744 SNPs were rejected in five or more true parent–offspring relationships facilitating identification of “problematic” SNPs with inconsistent inheritance in multiple parent–offspring relationships.ConclusionsThis study shows that animal experiments and routine genotyping in genomic selection allow to establish or to verify first-degree relationships as well as to estimate genotyping errors for each batch of animals or experiment.

Sporadic Pseudohypoparathyroidism Type 1B in Monozygotic Twins: Insights Into the Pathogenesis of Methylation Defects

ContextSporadic pseudohypoparathyroidism type 1B (sporPHP1B) is an imprinting disease without a defined genetic cause, characterized by broad methylation changes in differentially methylated regions (DMRs) of the GNAS gene.ObjectiveThis work aims to provide insights into the causative event leading to the GNAS methylation defects through comprehensive molecular genetic analyses of a pair of female monozygotic twins concordant for sporPHP1B who were conceived naturally, that is, without assisted reproductive techniques.MethodsUsing the leukocyte genome of the twins and family members, we performed targeted bisulfite sequencing, methylation-sensitive restriction enzyme (MSRE)-quantitative polymerase chain reaction (qPCR), whole-genome sequencing (WGS), high-density single-nucleotide polymorphism (SNP) array, and Sanger sequencing.ResultsMethylation analyses by targeted bisulfite sequencing and MSRE-qPCR revealed almost complete losses of methylation at the GNAS AS, XL, and A/B DMRs and a gain of methylation at the NESP55 DMR in the twins, but not in other family members. Except for the GNAS locus, we did not find apparent methylation defects at other imprinted genome loci of the twins. WGS, SNP array, and Sanger sequencing did not detect the previously described genetic defects associated with familial PHP1B. Sanger sequencing also ruled out any novel genetic alterations in the entire NESP55/AS region. However, the analysis of 28 consecutive SNPs could not exclude the possibility of paternal heterodisomy in a span of 22 kb comprising exon NESP55 and AS exon 5.ConclusionOur comprehensive analysis of a pair of monozygotic twins with sporPHP1B ruled out all previously described genetic causes. Twin concordance indicates that the causative event was an imprinting error earlier than the timing of monozygotic twinning.

The Aldehyde Dehydrogenase ALDH2*2 Allele, Associated with Alcohol Drinking Behavior, Dates Back to Prehistoric Times.

Human alcohol-consumption behavior is partly genetically encoded. The alcohol consumption of 987 residents in Keelung, Taiwan, was evaluated by using the Alcohol Use Disorder Identification Test (AUDIT). We assessed ~750,000 genomic variants of 71 residents who drank hazardously (AUDIT score ≥ 8) and 126 residents who did not drink in their daily lives (AUDIT score = 0), using high-density single nucleotide polymorphism (SNP) arrays. The rs671 G > A manifests the highest significance of the association with drinking behavior (Fisher’s exact P = 8.75 × 10−9). It is a pleiotropic, non-synonymous variant in the aldehyde dehydrogenase 2 (ALDH2) gene. The minor allele “A”, commonly known as ALDH2*2, is associated with non-drinkers. Intriguingly, identity-by-descent haplotypes encompassing genomic regions with a median length of 1.6 (0.6–2.0) million nucleotide bases were found in all study participants with either heterozygous or homozygous ALDH2*2 (n = 81 and 13, respectively). We also analyzed a public-domain dataset with genome-wide genotypes of 2000 participants in Guangzhou, a coastal city in Southern China. Among them, 175 participants have homozygous ALDH2*2 genotype, and again, long ALDH2*2-carrying haplotypes were found in all 175 participants without exceptions. The median length of the ALDH2*2-carrying haplotype is 1.7 (0.5–2.8) million nucleotide bases. The haplotype lengths in the Keelung and Guangzhou cohorts combined indicate that the origin of the ALDH2*2 allele dates back to 7935 (7014–9381) years ago. In conclusion, the rs671 G > A is the leading genomic variant associated with the long-term drinking behavior among residents of Keelung, Taiwan. The ALDH2*2 allele has been in Asian populations since prehistoric times.

The Bnapus50K array: a quick and versatile genotyping tool for Brassica napus genomic breeding and research.

Rapeseed is a globally cultivated commercial crop, primarily grown for its oil. High-density single nucleotide polymorphism (SNP) arrays are widely used as a standard genotyping tool for rapeseed research, including for gene mapping, genome-wide association studies, germplasm resource analysis, and cluster analysis. Although considerable rapeseed genome sequencing data have been released, DNA arrays are still an attractive choice for providing additional genetic data in an era of high-throughput whole-genome sequencing. Here, we integrated re-sequencing DNA array data (32,216, 304 SNPs) from 505 inbred rapeseed lines, allowing us to develop a sensitive and efficient genotyping DNA array, Bnapus50K, with a more consistent genetic and physical distribution of probes. A total of 42,090 high-quality probes were filtered and synthesized, with an average distance between adjacent SNPs of 8 kb. To improve the practical application potential of this array in rapeseed breeding, we also added 1,618 functional probes related to important agronomic traits such as oil content, disease resistance, male sterility, and flowering time. The additional probes also included those specifically for detecting genetically modified material. These probes show a good detection efficiency and are therefore useful for gene mapping, along with crop variety improvement and identification. The novel Bnapus50K DNA array developed in this study could prove to be a quick and versatile genotyping tool for B. napus genomic breeding and research.

High-Risk Clinicopathological and Genetic Features and Outcomes in Patients Receiving Neoadjuvant Radiochemotherapy for Locally Advanced Rectal Cancer.

Simple SummaryThe overall genomic copy number changes profile of three subgroups of locally advanced rectal carcinoma patients with significantly different response to neoadjuvant treatment with radiochemotherapy (ranging from complete to poor- or no-response) was analyzed and compared with a set of normal samples from healthy individuals with negative colonoscopies from the Castilla y León (Spain) region. We identified and validated a novel genetic signature, which combined with clinicopathological features, predicts response to neoadjuvant treatment and clinical outcome.Administering preoperative radiochemotherapy (RCT) in stage II-III tumors to locally advanced rectal carcinoma patients has proved to be effective in a high percentage of cases. Despite this, 20–30% of patients show no response or even disease progression. At present, preoperative response is assessed by a combination of imaging and tumor regression on histopathology, but recent studies suggest that various genetic abnormalities may be associated with the sensitivity or resistance of rectal cancer tumor cells to neoadjuvant therapy. In the present study we investigated the relationship between genetic lesions detected by high-density single-nucleotide polymorphisms (SNP) arrays 6.0 and response to neoadjuvant RCT, evaluated according to Dworak criteria in 39 rectal cancer tumors before treatment. The highest frequency of copy-number (CN) losses detected corresponded to chromosomes 18q (n = 27; 69%), 1p (n = 22; 56%), 15q (n = 19; 49%), 8p (n = 18; 48%), 4q (n = 17; 46%), and 22q (n = 17; 46%); in turn, CN gains more frequently involved chromosomes 20p (n = 22; 56%), 8p (n = 20; 51%), and 15q (n = 16; 41%). There was a significant association between alterations in the 1p, 3q, 7q, 12p, 17q, 20p, and 22q chromosomal regions and the degree of response to therapy prior to surgery. However, 4q, 15q11.1, and 15q14 chromosomal region alterations were identified as important by five prediction algorithms, i.e., those with the greatest influence on predicting the tumor response to treatment with preoperative RCT. Multivariate analysis of prognostic factors showed that gains on 15q11.1 and carcinoembryonic antigen (CEA) levels serum at diagnosis were the only independent variables predicting disease-free survival (DFS). Lymph node involvement also showed a prognostic impact on overall survival (OS) in the multivariate analysis. A deep-learning-based algorithm showed a 100% success rate in predicting both DFS and OS at 60 months after diagnosis of the disease. In summary, our results indicate the existence of an association between tumor genetic abnormalities at diagnosis, response to neoadjuvant therapy, and survival of patients with locally advanced rectal cancer. In addition to the clinical and biological characteristics of locally advanced rectal cancer patients, these could be used in the future as therapeutic and prognostic biomarkers, to identify patients sensitive or resistant to preoperative treatment, helping guide therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of the newly identified biomarkers.

Autozygosity Mapping by Genome-wide Single Nucleotide Polymorphism Array Identifies a Novel Homozygous HR Mutation in a Consanguineous Family with Universal Hereditary Hair Loss

AbstractObjective:Isolated hereditary hypotrichosis is caused by mutations in as many as 11 different genes. The conventional mutation detection strategy consists of sequencing of individual candidate genes separately, a time consuming and costly approach. In this study, we perform genome-wide single nucleotide polymorphism (SNP) array to identify candidate genes of hereditary hypotrichosis.Methods:A consanguineous family with two patients with hereditary hypotrichosis was enrolled, and autozygosity mapping by genome-wide SNP array was utilized to identify candidate genes.Results:Autozygosity mapping delineated runs of homozygosity, and alignment of the 11 genes identified the hairless (HR) gene as the candidate gene. Nucleotide sequencing revealed a novel homozygous mutation c.381delT, p.Ser127ArgfsTer40.Conclusion:This study illustrates how autozygosity mapping by a high-density SNP array streamlines mutation detection in heritable skin diseases.