Technical note: an R package for fitting generalized linear mixed models in animal breeding

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10-13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual's genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual's predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden)

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

Conversational production and comprehension: fMRI-evidence reminiscent of but deviant from the classical Broca-Wernicke model

A key question in research on the neurobiology of language is to which extent the language production and comprehension systems share neural infrastructure, but this question has not been addressed in the context of conversation. We utilized a public fMRI dataset where 24 participants engaged in unscripted conversations with a confederate outside the scanner, via an audio-video link. We provide evidence indicating that the two systems share neural infrastructure in the left-lateralized perisylvian language network, but diverge regarding the level of activation in regions within the network. Activity in the left inferior frontal gyrus was stronger in production compared to comprehension, while comprehension showed stronger recruitment of the left anterior middle temporal gyrus and superior temporal sulcus, compared to production. Although our results are reminiscent of the classical Broca-Wernicke model, the anterior (rather than posterior) temporal activation is a notable difference from that model. This is one of the findings that may be a consequence of the conversational setting, another being that conversational production activated what we interpret as higher-level socio-pragmatic processes. In conclusion, we present evidence for partial overlap and functional asymmetry of the neural infrastructure of production and comprehension, in the above-mentioned frontal vs temporal regions during conversation.

Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken

Genotype imputation is a powerful technique employed by next-generation sequencing (NGS) and genotyping arrays, which can significantly enhance the cost-effectiveness and efficiency of genomic selection. The accuracy of imputation is largely determined by the choice of reference panel, with previous studies generally demonstrating that a closely related population as a reference panel leads to greater accuracy than a more distantly related population. Various strategies have been proposed for selecting desirable individuals via targeted resequencing, but their efficiencies need further improvement. In this study, we present a practical broiler selection methodology for a local Chinese chicken line that integrates established methods based on pedigree, genomics, and random sampling, and leverages genotype and pedigree information from the yellow-plumage dwarf chicken line. The efficacy of these selection strategies was assessed by evaluating their ability to accurately impute masked genotypes from data obtained using a 600K chip. Our findings reveal that the pedigree-based method yields superior accuracy in genotype imputation, whereas the haplotype-based method exhibits greater stability. Nonetheless, the impact of these targeted methods for selecting key individuals is slightly different when initiating a new sequencing project in a production context. Overall, this study highlights the advantages of using the pedigree-based approach as the preferred method for optimizing genotype imputation in broiler chickens.

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

The wrong horse was bet on: the effects of argument structure versus argument adjacency on the processing of idiomatic sentences

Introduction

Psycholinguistic research remains puzzled about the circumstances under which syntactically transformed idioms keep their figurative meaning. There is an abundance of linguistic and psycholinguistic studies that have examined which factors may determine why some idioms are more syntactically fixed than others, including transparency, compositionality, and syntactic frozenness; however, they have returned inconclusive, sometimes even conflicting, results. This is the first study to examine argument structure (i.e., the number of arguments a verb takes) and argument adjacency (i.e., the position of the critical arguments relative to the verb) and their effects on the processing of idiomatic and literal sentences in German. Our results suggest that neither the traditional models of idiom processing (according to which idioms are stored as fixed entries) nor more recent hybrid theories (which concede some compositional handling in addition to a fixed entry) adequately account for the effects of argument structure or argument adjacency. Therefore, this study challenges existing models of idiom processing.

Methods

In two sentence-completion experiments, participants listened to idiomatic and literal sentences in both active and passive voice without the sentence-final verb. They indicated which of three visually-presented verbs best completed the sentence. We manipulated the factor argument structure within experiments and argument adjacency across experiments. In Experiment 1, passivized three-argument sentences had the critical argument adjacent to the verb while two-argument sentences had the critical argument non-adjacent to the verb, and vice versa in Experiment 2.

Results

In both experiments, voice interacted with argument structure. Active sentences-both literal and idiomatic-showed equivalent processing of two- and three-argument sentences. However, passive sentences returned contrasting effects. In Experiment 1, three-argument sentences were processed faster than two-argument sentences and vice versa in Experiment 2. This pattern corresponds to faster processing when critical arguments are adjacent than non-adjacent.

Discussion

The results point to the dominant role of argument adjacency over the number of arguments in the processing of syntactically transformed sentences. Regarding idiom processing, we conclude that the adjacency of the verb to its critical arguments determines whether passivized idioms keep their figurative meaning and present the implications of this finding for relevant models of idiom processing.

Runs of homozygosity and cross-generational inbreeding of Iranian fat-tailed sheep

The Lori-Bakhtiari fat-tailed sheep is one of the most important heavyweight native breeds of Iran. The breed is robust and well-adapted to semi-arid regions and an important resource for smallholder farms. An established nucleus-based breeding scheme is used to improve their production traits but there is an indication of inbreeding depression and loss of genetic diversity due to selection. Here, we estimated the inbreeding levels and the distribution of runs of homozygosity (ROH) islands in 122 multi-generational female Lori-Bakhtiari from different half-sib families selected from a breeding station that were genotyped on the 50k array. A total of 2404 ROH islands were identified. On average, there were 19.70 ± 1.4 ROH per individual ranging between 6 and 41. The mean length of the ROH was 4.1 ± 0.14 Mb. There were 1999 short ROH of length 1-6 Mb and another 300 in the 6-12 Mb range. Additionally long ROH indicative of inbreeding were found in the ranges of 12-24 Mb (95) and 24-48 Mb (10). The average inbreeding coefficient (F_ROH) was 0.031 ± 0.003 with estimates varying from 0.006 to 0.083. Across generations, F_ROH increased from 0.019 ± 0.012 to 0.036 ± 0.007. Signatures of selection were identified on chromosomes 2, 6, and 10, encompassing 55 genes and 23 QTL associated with production traits. Inbreeding coefficients are currently within acceptable levels but across generations, inbreeding is increasing due to selection. The breeding program needs to actively monitor future inbreeding rates and ensure that the breed maintains or improves on its current levels of environmental adaptation.

Educational Attainment Polygenic Scores: Examining Evidence for Gene-Environment Interplay with Adolescent Alcohol, Tobacco and Cannabis Use

Genes associated with educational attainment may be related to or interact with adolescent alcohol, tobacco and cannabis use. Potential gene-environment interplay between educational attainment polygenic scores (EA-PGS) and adolescent alcohol, tobacco, and cannabis use was evaluated with a series of regression models fitted to data from a sample of 1871 adult Australian twins. All models controlled for age, age², cohort, sex and genetic ancestry as fixed effects, and a genetic relatedness matrix was included as a random effect. Although there was no evidence that adolescent alcohol, tobacco or cannabis use interacted with EA-PGS to influence educational attainment, there was a significant, positive gene-environment correlation with adolescent alcohol use at all PGS thresholds (ps <.02). Higher EA-PGS were associated with an increased likelihood of using alcohol as an adolescent (ΔR² ranged from 0.5% to 1.1%). The positive gene-environment correlation suggests a complex relationship between educational attainment and alcohol use that is due to common genetic factors.

Longitudinal change in blood DNA epigenetic signature after smoking cessation

Cigarette smoking is associated with epigenetic changes that may be reversible following smoking cessation. Whole blood DNA methylation was evaluated in Framingham Heart Study Offspring (n = 169) and Third Generation (n = 30) cohort participants at two study visits 6 years apart and in Atherosclerosis Risk in Communities (ARIC) study (n = 222) participants at two study visits 20 years apart. Changes in DNA methylation (delta β values) at 483,565 cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs) were compared between participants who were current, former, or never smokers at both visits (current-current, former-former, never-never, respectively), versus those who quit in the interim (current-former). Interim quitters had more hypermethylation at four CpGs annotated to AHRR, one CpG annotated to F2RL3, and one intergenic CpG (cg21566642) compared with current-current smokers (FDR < 0.02 for all), and two significant DMRs were identified. While there were no significant differentially methylated CpGs in the comparison of interim quitters and former-former smokers, 106 DMRs overlapping with small nucleolar RNA were identified. As compared with all non-smokers, current-current smokers additionally had more hypermethylation at two CpG sites annotated to HIVEP3 and TMEM126A, respectively, and another intergenic CpG (cg14339116). Gene transcripts associated with smoking cessation were implicated in immune responses, cell homoeostasis, and apoptosis. Smoking cessation is associated with early reversion of blood DNA methylation changes at CpG sites annotated to AHRR and F2RL3 towards those of never smokers. Associated gene expression suggests a role of longitudinal smoking-related DNA methylation changes in immune response processes.

Genetic architecture of end-use quality traits in soft white winter wheat

Background

Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.

Results

This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.

Conclusions

In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08676-5.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Bos taurus haplotypes segregating in Nellore (Bos indicus) cattle

Brazil is the largest exporter of beef in the world, and most of that beef derives from Nellore cattle. Although considered a zebu breed (Bos indicus), the history of Nellore cattle in Brazil is marked by the importation of bulls from India, the use of a Creole taurine (Bos taurus) maternal lineage to quickly expand the herds and backcrossing to Nellore bulls to recover zebu ancestry. As a consequence, the current Brazilian Nellore population carries an average taurine ancestry of approximately 1%. Although that percentage seems small, some taurine variants deviate substantially from that average, with the better-known cases being the PLAG1-Q haplotype involved with body size variation and the Guarani (P_G ) polled variant producing hornless animals. Here, we report taurine haplotypes in 9074 Nellore animals genotyped for 539 657 imputed SNP markers. Apart from PLAG1-Q and P_G , our analysis further revealed common taurine haplotypes (>3%) spanning genes related to immunity, growth, reproduction and hair and skin phenotypes. Using data from 22 economically important traits, we showed that many of the major QTL previously reported in the breed are at least partially driven by taurine haplotypes. As B. taurus and B. indicus haplotypes are highly divergent, presenting widely different sets of functional variants, our results provide promising targets for future scrutiny in Nellore cattle.

Genome-Wide Association Study of Root Mealiness and Other Texture-Associated Traits in Cassava

Cassava breeders have made significant progress in developing new genotypes with improved agronomic characteristics such as improved root yield and resistance against biotic and abiotic stresses. However, these new and improved cassava (Manihot esculenta Crantz) varieties in cultivation in Nigeria have undergone little or no improvement in their culinary qualities; hence, there is a paucity of genetic information regarding the texture of boiled cassava, particularly with respect to its mealiness, the principal sensory quality attribute of boiled cassava roots. The current study aimed at identifying genomic regions and polymorphisms associated with natural variation for root mealiness and other texture-related attributes of boiled cassava roots, which includes fibre, adhesiveness (ADH), taste, aroma, colour, and firmness. We performed a genome-wide association (GWAS) analysis using phenotypic data from a panel of 142 accessions obtained from the National Root Crops Research Institute (NRCRI), Umudike, Nigeria, and a set of 59,792 high-quality single nucleotide polymorphisms (SNPs) distributed across the cassava genome. Through genome-wide association mapping, we identified 80 SNPs that were significantly associated with root mealiness, fibre, adhesiveness, taste, aroma, colour and firmness on chromosomes 1, 4, 5, 6, 10, 13, 17 and 18. We also identified relevant candidate genes that are co-located with peak SNPs linked to these traits in M. esculenta. A survey of the cassava reference genome v6.1 positioned the SNPs on chromosome 13 in the vicinity of Manes.13G026900, a gene recognized as being responsible for cell adhesion and for the mealiness or crispness of vegetables and fruits, and also known to play an important role in cooked potato texture. This study provides the first insights into understanding the underlying genetic basis of boiled cassava root texture. After validation, the markers and candidate genes identified in this novel work could provide important genomic resources for use in marker-assisted selection (MAS) and genomic selection (GS) to accelerate genetic improvement of root mealiness and other culinary qualities in cassava breeding programmes in West Africa, especially in Nigeria, where the consumption of boiled and pounded cassava is low.

Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat

The novel super-soft kernel phenotype has the potential to improve wheat processing and flour quality. We identified genomic regions associated with this kernel texture in white winter wheat. Grain hardness is a key determinant of wheat milling and baking quality. The recently discovered 'super-soft' kernel phenotype has the potential to improve wheat processing and flour quality. However, the genetic basis underlying the super-soft trait in wheat is not yet well understood. In this study, we investigated the phenotypic and genotypic structure of the super-soft trait in a collection of 172 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the USA. This collection had a continuous distribution for grain hardness index (single-kernel characterization system). Ten super-soft genotypes showed hardness index ≤ 12 including the cultivar Jasper. Over 98,000 SNP markers from genotyping-by-sequencing were used for association mapping (GWAS). The GWAS identified 20 significant markers associated with grain hardness. These significant SNPs corresponded to seven QTL on chromosomes 2B, 3A, 3B, 5A, 6B,7A, and one unaligned chromosome. Two of these QTL, QSKhard.wql-3A and QSKhard.wql-5A, had large effects and distinguished between the normal soft and the super-soft classes. QSKhard.wql-3A and QSKhard.wql-5A reduced the hardness index by 11.7 and 13.1 on average, respectively. The remaining QTL had small effects and reduced grain hardness within the normal soft range. QSKhard.wql-2B, QSKhard.wql-3A, QSKhard.wql-3B, and QSKhard.wql-6B were not previously reported to be in genomic regions of grain hardness-related genes/QTL. The identified super-soft genotypes as well as the SNPs associated with lower grain hardness will be useful to assist breeding for this grain texture trait.

Alternative Ways of Computing the Numerator Relationship Matrix

Pedigree relationships between every pair of individuals forms the elements of the additive genetic relationship matrix (A). Calculation of A⁻¹ does not require forming and inverting A, and it is faster and easier than the calculation of A. Although A⁻¹ is used in best linear unbiased prediction of genetic merit, A is used in population studies and post-evaluation procedures, such as breeding programs and controlling the rate of inbreeding. Three pedigrees with 20,000 animals (20K) and different (1, 2, 4) litter sizes, and a pedigree with 180,000 animals (180K) and litter size 2 were simulated. Aiming to reduce the computation time for calculating A, new methods [Array-Tabular method, (^T−1)−1 instead of T in Thompson's method, iterative updating of D in Thompson's method, and iteration by generation] were developed and compared with some existing methods. The methods were coded in the R programming language to demonstrate the algorithms, aiming for minimizing the computational time. Among 20K, computational time decreased with increasing litter size for most of the methods. Methods deriving A from A⁻¹ were relatively slow. The other methods were either using only pedigree information or both the pedigree and inbreeding coefficients. Calculating inbreeding coefficients was extremely fast (<0.2 s for 180K). Parallel computing (15 cores) was adopted for methods that were based on solving A⁻¹ for columns of A, as those methods allowed implicit parallelism. Optimizing the code for one of the earliest methods enabled A to be built in 13 s (faster than the 31 s for calculating A⁻¹) for 20K and 17 min 3 s for 180K. Memory is a bottleneck for large pedigrees but attempts to reduce the memory usage increased the computational time. To reduce disk space usage, memory usage, and computational time, relationship coefficients of old animals in the pedigree can be archived and relationship coefficients for parents of the next generation can be saved in an external file for successive updates to the pedigree and the A matrix.

lme4GS: An R-Package for Genomic Selection

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

High-resolution characterization of male ornamentation and re-evaluation of sex linkage in guppies

Coloration plays a key role in the ecology of many species, influencing how an organism interacts with its environment, other species and conspecifics. Guppies are sexually dimorphic, with males displaying sexually selected coloration resulting from female preference. Previous work has suggested that much of guppy colour pattern variation is Y-linked. However, it remains unclear how many individual colour patterns are Y-linked in natural populations as much of the previous work has focused on phenotypes either not found in the wild, or aggregate measures such as total colour area. Moreover, ornaments have traditionally been identified and delineated by hand, and computational methods now make it possible to extract pixels and identify ornaments with automated methods, reducing the potential for human bias. Here we developed a pipeline for semi-automated ornament identification and high-resolution image analysis of male guppy colour patterns and applied it to a multigenerational pedigree. Our results show that loci controlling the presence or the absence of individual male ornaments in our population are not predominantly Y-linked. However, we find that ornaments of similar colour are not independent of each other, and modifier loci that affect whole animal coloration appear to be at least partially Y-linked. Considering these results, Y-linkage of individual ornaments may not be important in driving colour changes in natural populations of guppies, or in expansions of the non-recombining Y region, while Y-linked modifier loci that affect aggregate traits may well play an important role.

Proximate Causes of Infertility and Embryo Mortality in Captive Zebra Finches

AbstractSome species show high rates of reproductive failure, which is puzzling because natural selection works against such failure in every generation. Hatching failure is common in both captive and wild zebra finches (Taeniopygia guttata), yet little is known about its proximate causes. Here we analyze data on reproductive performance (the fate of >23,000 eggs) based on up to 14 years of breeding of four captive zebra finch populations. We find that virtually all aspects of reproductive performance are negatively affected by inbreeding (mean r=-0.117); by an early-starting, age-related decline (mean r=-0.132); and by poor early-life nutrition (mean r=-0.058). However, these effects together explain only about 3% of the variance in infertility, offspring mortality, fecundity, and fitness. In contrast, individual repeatability of different fitness components varied between 15% and 50%. As expected, we found relatively low heritability in fitness components (median: 7% of phenotypic variation and 29% of individually repeatable variation). Yet some of the heritable variation in fitness appears to be maintained by antagonistic pleiotropy (negative genetic correlations) between male fitness traits and female and offspring fitness traits. The large amount of unexplained variation suggests a potentially important role of local dominance and epistasis, including the possibility of segregating genetic incompatibilities.

Sequence variations of ACVRL1 play a critical role in hepatic vascular malformations in hereditary hemorrhagic telangiectasia

Background

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant disorder characterized by multiple telangiectases and caused by germline disease-causing variants in the ENG (HHT1), ACVRL1 (HHT2) and, to a lesser extent MADH4 and GDF2, which encode proteins involved in the TGF-β/BMP9 signaling pathway. Common visceral complications of HHT are caused by pulmonary, cerebral, or hepatic arteriovenous malformations (HAVMs). There is large intrafamilial variability in the severity of visceral involvement, suggesting a role for modifier genes. The objective of the present study was to investigate the potential role of ENG, ACVRL1, and of other candidate genes belonging to the same biological pathway in the development of HAVMs.

Methods

We selected 354 patients from the French HHT patient database who had one disease causing variant in either ENG or ACVRL1 and who underwent hepatic exploration. We first compared the distribution of the different types of variants with the occurrence of HAVMs. Then, we genotyped 51 Tag-SNPs from the Hap Map database located in 8 genes that encode proteins belonging to the TGF-β/BMP9 pathway (ACVRL1, ENG, GDF2, MADH4, SMAD1, SMAD5, TGFB1, TGFBR1), as well as in two additional candidate genes (PTPN14 and ADAM17). We addressed the question of a possible genetic association with the occurrence of HAVMs.

Results

The proportion of patients with germline ACVRL1 variants and the proportion of women were significantly higher in HHT patients with HAVMs. In the HHT2 group, HAVMs were more frequent in patients with truncating variants. Six SNPs (3 in ACVRL1, 1 in ENG, 1 in SMAD5, and 1 in ADAM17) were significantly associated with HAVMs. After correction for multiple testing, only one remained significantly associated (rs2277383).

Conclusions

In this large association study, we confirmed the strong relationship between ACVRL1 and the development of HAVMs. Common polymorphisms of ACVRL1 may also play a role in the development of HAVMs, as a modifying factor, independently of the disease-causing variants.

Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Offspring performance is well buffered against stress experienced by ancestors

Evolution should render individuals resistant to stress and particularly to stress experienced by ancestors. However, many studies report negative effects of stress experienced by one generation on the performance of subsequent generations. To assess the strength of such transgenerational effects we propose a strategy aimed at overcoming the problem of type I errors when testing multiple proxies of stress in multiple ancestors against multiple offspring performance traits, and we apply it to a large observational dataset on captive zebra finches (Taeniopygia guttata). We combine clear one-tailed hypotheses with steps of validation, meta-analytic summary of mean effect sizes, and independent confirmatory testing. We find that drastic differences in early growth conditions (nestling body mass 8 days after hatching varied sevenfold between 1.7 and 12.4 g) had only moderate direct effects on adult morphology (95% confidence interval [CI]: r = 0.19-0.27) and small direct effects on adult fitness traits (r = 0.02-0.12). In contrast, we found no indirect effects of parental or grandparental condition (r = -0.017 to 0.002; meta-analytic summary of 138 effect sizes), and mixed evidence for small benefits of matching environments between parents and offspring, as the latter was not robust to confirmatory testing in independent datasets. This study shows that evolution has led to a remarkable robustness of zebra finches against undernourishment. Our study suggests that transgenerational effects are absent in this species, because CIs exclude all biologically relevant effect sizes.

ggroups: an R package for pedigree and genetic groups data

BACKGROUND

R is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article (https://cran.r-project.org/web/packages, accessed in October 2019). The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package.

RESULTS

Using sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package.

CONCLUSIONS

The presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data.

Estimation of inbreeding and identification of regions under heavy selection based on runs of homozygosity in a Large White pig population

Background

Runs of homozygosity (ROHs) are homozygous segments of the genome where the two haplotypes inherited from the parents are identical. The current availability of genotypes for a very large number of single nucleotide polymorphisms (SNPs) is leading to more accurate characterization of ROHs in the whole genome. Here, we investigated the occurrence and distribution of ROHs in 3,692 Large White pigs and compared estimates of inbreeding coefficients calculated based on ROHs (F_ROH), homozygosity (F_HOM), genomic relationship matrix (F_GRM) and pedigree (F_PED). Furthermore, we identified genomic regions with high ROH frequencies and annotated their candidate genes.

Results

In total, 176,182 ROHs were identified from 3,569 animals, and all individuals displayed at least one ROH longer than 1 Mb. The ROHs identified were unevenly distributed on the autosomes. The highest and lowest coverages of Sus scrofa chromosomes (SSC) by ROH were on SSC14 and SSC13, respectively. The highest pairwise correlation among the different inbreeding coefficient estimates was 0.95 between F_{ROH_total} and F_HOM, while the lowest was − 0.083 between F_GRM and F_PED. The correlations between F_PED and F_ROH using four classes of ROH lengths ranged from 0.18 to 0.37 and increased with increasing ROH length, except for ROH > 10 Mb. Twelve ROH islands were located on four chromosomes (SSC1, 4, 6 and 14). These ROH islands harboured genes associated with reproduction, muscular development, fat deposition and adaptation, such as SIRT1, MYPN, SETDB1 and PSMD4.

Conclusion

F_ROH can be used to accurately assess individual inbreeding levels compared to other inbreeding coefficient estimators. In the absence of pedigree records, F_ROH can provide an alternative to inbreeding estimates. Our findings can be used not only to effectively increase the response to selection by appropriately managing the rate of inbreeding and minimizing the negative effects of inbreeding depression but also to help detect genomic regions with an effect on traits under selection.

Identification of additional /novel QTL associated with resistance to cassava green mite in a biparental mapping population

Cassava green mite [CGM, Mononychellus tanajoa (Bondar)] is the most destructive dry-season pest in most cassava production areas. The pest is responsible for cassava fresh root yield losses of over 80%. Deployment of CGM resistant cultivars is the most cost-effective and sustainable approach of alleviating such production losses. The purposes of this study were to validate the stability of CGM resistance genes found in previously published results, to identify new genes for CGM resistance in bi-parental mapping population and estimate the heritability of the trait. A total of 109 F1 progeny derived from a cross between CGM resistant parent, TMEB778 and a very susceptible parent, TMEB419 were evaluated under CGM hotspot areas in Nigeria for two cropping seasons. A total of 42,204 SNP markers with MAF ≥ 0.05 were used for single-marker analysis. The most significant QTL (S12_7962234) was identified on the left arm on chromosome 12 which explained high phenotypic variance and harboured significant single nucleotide polymorphism (SNP) markers conferring resistance to CGM and leaf pubescence (LP). Colocalization of the most significant SNP associated with resistance to CGM and LP on chromosome 12 is possibly an indication of a beneficial pleiotropic effect or are physically linked. These significant SNPs markers were intersected with the gene annotations and 33 unique genes were identified within SNPs at 4 - 8MB on chromosome 12. Among these genes, nine novel candidate genes namely; Manes.12077600, Manes.12G086200, Manes.12G061200, Manes.12G083100, Manes.12G082000, Manes.12G094100, Manes.12G075600, Manes.12G091400 and Manes.12G069300 highly expressed direct link to cassava green mite resistance. Pyramiding the new QTL/genes identified on chromosome 12 in this study with previously discovered loci, such on chromosome 8, will facilitate breeding varieties that are highly resistant CGM.

No genetic contribution to variation in human offspring sex ratio: a total population study of 4.7 million births

The ratio of males to females among an individual's offspring at birth (offspring sex ratio) has long been of great interest to evolutionary biologists. The human offspring sex ratio is around 1 : 1 and is understood primarily in terms of Fisher's principle (R. A. Fisher, The genetical theory of natural selection, 1930), which is based on the insight that in a population with an unequal sex ratio, each individual of the rarer sex will on average have greater reproductive value than each individual of the more common sex. Accordingly, individuals genetically predisposed to produce the rarer sex will tend to have greater fitness and thus genes predisposing to bearing that sex will increase in frequency until the population sex ratio approaches 1 : 1. An assumption of this perspective is that individuals' offspring sex ratio is heritable. However, the heritability in humans remains remarkably uncertain, with inconsistent findings and important power limitations of existing studies. To address this persistent uncertainty, we used data from the entire Swedish-born population born 1932 or later, including 3 543 243 individuals and their 4 753 269 children. To investigate whether offspring sex ratio is influenced by genetic variation, we tested the association between individuals' offspring's sex and their siblings' offspring's sex (n pairs = 14 015 421). We estimated that the heritability for offspring sex ratio was zero, with an upper 95% confidence interval of 0.002, rendering Fisher's principle and several other existing hypotheses untenable as frameworks for understanding human offspring sex ratio.

Performance of pedigree and various forms of marker-derived relationship coefficients in genomic prediction and their correlations

In recent years, with development and validation of different genotyping panels, several methods have been proposed to build efficient similarity matrices among individuals to be used for genomic selection. Consequently, the estimated genetic parameters from such information may deviate from their counterpart using traditional family information. In this study, we used a pedigree-based numerator relationship matrix (A) and three types of marker-based relationship matrices ( G ) including two identical by descent, that is G K and G M and one identical by state, G V as well as four Gaussian kernel ( GK ) similarity kernels with different smoothing parameters to predict yet to be observed phenotypes. Also, we used different kinship matrices that are a linear combination of marker-derived IBD or IBS matrices with A, constructed as K = λ G + 1 - λ A , where the weight ( λ ) assigned to each source of information varied over a grid of values. A Bayesian multiple-trait Gaussian model was fitted to estimate the genetic parameters and compare the prediction accuracy in terms of predictive correlation, mean square error and unbiasedness. Results show that the estimated genetic parameters (heritability and correlations) are affected by the source of the information used to create kinship or the weight placed on the sources of genomic and pedigree information. The superiority of GK-based model depends on the smoothing parameters (θ) so that with an optimum θ value, the GK-based model statistically yielded better performance (higher predictive correlation, lowest MSE and unbiased estimates) and more stable correlations and heritability than the model with IBD, IBS or A kinship matrices or any of the linear combinations.

Fat mass compared to four body condition scoring systems in the Asian elephant (Elephas maximus)

Captive elephant populations are not self-sustaining due to health concerns possibly related to obesity. Categorizing obesity relies on qualitative analyses like body condition scores (BCS). However, elephant indices have not been validated against measured body composition. The objective was to compare BCS systems to body composition determined by deuterium dilution in 28 zoo-kept Asian elephants. Elephants were weighed and given deuterated water orally (0.05 ml/kg). Blood was collected at ~0, 24, 120, 240, 360, and 480 hr after dosing. Photographs were taken to score the elephant based on four BCS systems (BCSWemmer [0 to 11 scoring], BCSMorfeld [1 to 5 scoring], BCSFernando [0 to 10 scoring], BCSWijeyamohan [1 to 10 scoring]). Based on regression analysis, relative fat ranged from -305 kg to 515 kg, where negative values indicate less and positive values indicate more fat than expected for the elephant's mass in this population. BCSFernando was associated with relative fat (p = .020, R2  = 0.194). Relative fat, adjusted for sex and age in the statistical model, was associated with BCSWemmer (p = .027, R2  = 0.389), BCSFernando (p = .002, R2  = 0.502), and BCSWijeyamohan (p = .011, R2  = 0.426). Inclusion of zoo and familial relatedness resulted in all BCS systems associated with relative fat (p ≤ .015). Only BCSFernando predicted relative fat, unadjusted, suggesting it is the most capable system for practical use. Compared to absolute fat, relative fat may be more biologically relevant as greater fat relative to body mass is more likely to lead to health issues.

Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease

Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.

Differentially methylated CpGs can inform on disease mechanisms and be useful as biomarkers. Here, the authors perform GWAS for DNA methylation in whole blood, cis- and trans-meQTL mapping, followed by Mendelian randomization analysis that links meQTLs with cardiovascular diseases.

Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load

Inbreeding depression, the deterioration in mean trait value in progeny of related parents, is a fundamental quantity in genetics, evolutionary biology, animal and plant breeding, and conservation biology. The magnitude of inbreeding depression can be quantified by the inbreeding load, typically measured in numbers of lethal equivalents, a population genetic quantity that allows for comparisons between environments, populations or species. However, there is as yet no quantitative assessment of which combinations of statistical models and metrics of inbreeding can yield such estimates. Here, we review statistical models that have been used to estimate inbreeding load and use population genetic simulations to investigate how unbiased estimates can be obtained using genomic and pedigree-based metrics of inbreeding. We use simulated binary viability data (i.e., dead versus alive) as our example, but the concepts apply to any trait that exhibits inbreeding depression. We show that the increasingly popular generalized linear models with logit link do not provide comparable and unbiased population genetic measures of inbreeding load, independent of the metric of inbreeding used. Runs of homozygosity result in unbiased estimates of inbreeding load, whereas inbreeding measured from pedigrees results in slight overestimates. Due to widespread use of models that do not yield unbiased measures of the inbreeding load, some estimates in the literature cannot be compared meaningfully. We surveyed the literature for reliable estimates of the mean inbreeding load from wild vertebrate populations and found an average of 3.5 haploid lethal equivalents for survival to sexual maturity. To obtain comparable estimates, we encourage researchers to use generalized linear models with logarithmic links or maximum-likelihood estimation of the exponential equation, and inbreeding coefficients calculated from runs of homozygosity, provided an assembled reference genome of sufficient quality and enough genetic marker data are available.

Direct and indirect genetic effects on reproductive investment in a grasshopper

A fundamental part of the quantitative genetic theory deals with the partitioning of the phenotypic variance into additive genetic and environmental components. During interaction with conspecifics, the interaction partner becomes a part of the environment from the perspective of the focal individual. If the interaction effects have a genetic basis, they are called indirect genetic effects (IGEs) and can evolve along with direct genetic effects. Sexual reproduction is a classic context where potential conflict between males and females can arise from trade-offs between current and future investments. We studied five female fecundity traits, egg length and number, egg pod length and number and latency to first egg pod, and estimated the direct and IGEs using a half-sib breeding design in the grasshopper Chorthippus biguttulus. We found that the male IGEs were an order of magnitude lower than the direct genetic effects and were not significantly different from zero. However, there was some indication that IGEs were larger shortly after mating, consistent with the idea that IGEs fade with time after interaction. Female direct heritabilities were moderate to low. Simulation shows that the variance component estimates can appear larger with less data, calling for care when interpreting variance components estimated with low power. Our results illustrate that the contribution of male IGEs is overall low on the phenotypic variance of female fecundity traits. Thus, even in the relevant context of sexual conflict, the influence of male IGEs on the evolutionary trajectory of female reproductive traits is likely to be small.

A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows

Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm^-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm^-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm^-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm^-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.

DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysis

Many hemostatic factors are associated with age and age-related diseases; however, much remains unknown about the biological mechanisms linking aging and hemostatic factors. DNA methylation is a novel means by which to assess epigenetic aging, which is a measure of age and the aging processes as determined by altered epigenetic states. We used a meta-analysis approach to examine the association between measures of epigenetic aging and hemostatic factors, as well as a clotting time measure. For fibrinogen, we performed European and African ancestry-specific meta-analyses which were then combined via a random effects meta-analysis. For all other measures we could not estimate ancestry-specific effects and used a single fixed effects meta-analysis. We found that 1-year higher extrinsic epigenetic age as compared with chronological age was associated with higher fibrinogen (0.004 g/L/y; 95% confidence interval, 0.001-0.007; P = .01) and plasminogen activator inhibitor 1 (PAI-1; 0.13 U/mL/y; 95% confidence interval, 0.07-0.20; P = 6.6 × 10-5) concentrations, as well as lower activated partial thromboplastin time, a measure of clotting time. We replicated PAI-1 associations using an independent cohort. To further elucidate potential functional mechanisms, we associated epigenetic aging with expression levels of the PAI-1 protein encoding gene (SERPINE1) and the 3 fibrinogen subunit-encoding genes (FGA, FGG, and FGB) in both peripheral blood and aorta intima-media samples. We observed associations between accelerated epigenetic aging and transcription of FGG in both tissues. Collectively, our results indicate that accelerated epigenetic aging is associated with a procoagulation hemostatic profile, and that epigenetic aging may regulate hemostasis in part via gene transcription.

Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree

Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual's risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10^-4). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10^-4), diabetes (p = 1 × 10^-3), and lipid traits such as triglyceride levels (p = 3 × 10^-4) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

BACKGROUND

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.

RESULTS

To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.

CONCLUSIONS

Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking

Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.

Pedigree-based inbreeding coefficient explains more variation in fitness than heterozygosity at 160 microsatellites in a wild bird population

Although the pedigree-based inbreeding coefficient F predicts the expected proportion of an individual's genome that is identical-by-descent (IBD), heterozygosity at genetic markers captures Mendelian sampling variation and thereby provides an estimate of realized IBD. Realized IBD should hence explain more variation in fitness than their pedigree-based expectations, but how many markers are required to achieve this in practice remains poorly understood. We use extensive pedigree and life-history data from an island population of song sparrows (Melospiza melodia) to show that the number of genetic markers and pedigree depth affected the explanatory power of heterozygosity and F, respectively, but that heterozygosity measured at 160 microsatellites did not explain more variation in fitness than F This is in contrast with other studies that found heterozygosity based on far fewer markers to explain more variation in fitness than F Thus, the relative performance of marker- and pedigree-based estimates of IBD depends on the quality of the pedigree, the number, variability and location of the markers employed, and the species-specific recombination landscape, and expectations based on detailed and deep pedigrees remain valuable until we can routinely afford genotyping hundreds of phenotyped wild individuals of genetic non-model species for thousands of genetic markers.

Epigenetic Patterns in Blood Associated With Lipid Traits Predict Incident Coronary Heart Disease Events and Are Enriched for Results From Genome-Wide Association Studies

Supplemental Digital Content is available in the text.

Background—

Genome-wide association studies have identified loci influencing circulating lipid concentrations in humans; further information on novel contributing genes, pathways, and biology may be gained through studies of epigenetic modifications.

Methods and Results—

To identify epigenetic changes associated with lipid concentrations, we assayed genome-wide DNA methylation at cytosine–guanine dinucleotides (CpGs) in whole blood from 2306 individuals from 2 population-based cohorts, with replication of findings in 2025 additional individuals. We identified 193 CpGs associated with lipid levels in the discovery stage (P<1.08E-07) and replicated 33 (at Bonferroni-corrected P<0.05), including 25 novel CpGs not previously associated with lipids. Genes at lipid-associated CpGs were enriched in lipid and amino acid metabolism processes. A differentially methylated locus associated with triglycerides and high-density lipoprotein cholesterol (HDL-C; cg27243685; P=8.1E-26 and 9.3E-19) was associated with cis-expression of a reverse cholesterol transporter (ABCG1; P=7.2E-28) and incident cardiovascular disease events (hazard ratio per SD increment, 1.38; 95% confidence interval, 1.15–1.66; P=0.0007). We found significant cis-methylation quantitative trait loci at 64% of the 193 CpGs with an enrichment of signals from genome-wide association studies of lipid levels (P_TC=0.004, P_HDL-C=0.008 and P_{triglycerides}=0.00003) and coronary heart disease (P=0.0007). For example, genome-wide significant variants associated with low-density lipoprotein cholesterol and coronary heart disease at APOB were cis-methylation quantitative trait loci for a low-density lipoprotein cholesterol–related differentially methylated locus.

Conclusions—

We report novel associations of DNA methylation with lipid levels, describe epigenetic mechanisms related to previous genome-wide association studies discoveries, and provide evidence implicating epigenetic regulation of reverse cholesterol transport in blood in relation to occurrence of cardiovascular disease events.

Neuregulin signaling pathway in smoking behavior

Understanding molecular processes that link comorbid traits such as addictions and mental disorders can provide novel therapeutic targets. Neuregulin signaling pathway (NSP) has previously been implicated in schizophrenia, a neurodevelopmental disorder with high comorbidity to smoking. Using a Finnish twin family sample, we have previously detected association between nicotine dependence and ERBB4 (a neuregulin receptor), and linkage for smoking initiation at the ERBB4 locus on 2q33. Further, Neuregulin3 has recently been shown to associate with nicotine withdrawal in a behavioral mouse model. In this study, we scrutinized association and linkage between 15 036 common, low frequency and rare genetic variants in 10 NSP genes and phenotypes encompassing smoking and alcohol use. Using the Finnish twin family sample (N=1998 from 740 families), we detected 66 variants (representing 23 LD blocks) significantly associated (false discovery rate P<0.05) with smoking initiation, nicotine dependence and nicotine withdrawal. We comprehensively annotated the associated variants using expression (eQTL) and methylation quantitative trait loci (meQTL) analyses in a Finnish population sample. Among the 66 variants, we identified 25 eQTLs (in NRG1 and ERBB4), 22 meQTLs (in NRG3, ERBB4 and PSENEN), a missense variant in NRG1 (rs113317778) and a splicing disruption variant in ERBB4 (rs13385826). Majority of the QTLs in blood were replicated in silico using publicly available databases, with additional QTLs observed in brain. In conclusion, our results support the involvement of NSP in smoking behavior but not in alcohol use and abuse, and disclose functional potential for 56 of the 66 associated single-nucleotide polymorphism.

Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)

Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint (Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint.

Independent Neural Computation of Value from Other People's Confidence

Expectation of reward can be shaped by the observation of actions and expressions of other people in one's environment. A person's apparent confidence in the likely reward of an action, for instance, makes qualities of their evidence, not observed directly, socially accessible. This strategy is computationally distinguished from associative learning methods that rely on direct observation, by its use of inference from indirect evidence. In twenty-three healthy human subjects, we isolated effects of first-hand experience, other people's choices, and the mediating effect of their confidence, on decision-making and neural correlates of value within ventromedial prefrontal cortex (vmPFC). Value derived from first-hand experience and other people's choices (regardless of confidence) were indiscriminately represented across vmPFC. However, value computed from agent choices weighted by their associated confidence was represented with specificity for ventromedial area 10. This pattern corresponds to shifts of connectivity and overlapping cognitive processes along a posterior-anterior vmPFC axis. Task behavior and self-reported self-reliance for decision-making in other social contexts correlated. The tendency to conform in other social contexts corresponded to increased activation in cortical regions previously shown to respond to social conflict in proportion to subsequent conformity (Campbell-Meiklejohn et al., 2010). The tendency to self-monitor predicted a selectively enhanced response to accordance with others in the right temporoparietal junction (rTPJ). The findings anatomically decompose vmPFC value representations according to computational requirements and provide biological insight into the social transmission of preference and reassurance gained from the confidence of others.

SIGNIFICANCE STATEMENT

Decades of research have provided evidence that the ventromedial prefrontal cortex (vmPFC) signals the satisfaction we expect from imminent actions. However, we have a surprisingly modest understanding of the organization of value across this substantial and varied region. This study finds that using cues of the reliability of other peoples' knowledge to enhance expectation of personal success generates value correlates that are anatomically distinct from those concurrently computed from direct, personal experience. This suggests that representation of decision values in vmPFC is suborganized according to the underlying computation, consistent with what we know about the anatomical heterogeneity of the region. These results also provide insight into the observational learning process by which someone else's confidence can sway and reassure our choices.

A sex-chromosome inversion causes strong overdominance for sperm traits that affect siring success

Male reproductive success depends on the competitive ability of sperm to fertilize the ova, which should lead to strong selection on sperm characteristics. This raises the question of how heritable variation in sperm traits is maintained. Here we show that in zebra finches (Taeniopygia guttata) nearly half of the variance in sperm morphology is explained by an inversion on the Z chromosome with a 40% allele frequency in the wild. The sperm of males that are heterozygous for the inversion had the longest midpieces and the highest velocity. Furthermore, such males achieved the highest fertility and the highest siring success, both within-pair and extra-pair. Males homozygous for the derived allele show detrimental sperm characteristics and the lowest siring success. Our results suggest heterozygote advantage as the mechanism that maintains the inversion polymorphism and hence variance in sperm design and in fitness.

Effects of Type 1 Diabetes Risk Alleles on Immune Cell Gene Expression

Genetic studies have identified 61 variants associated with the risk of developing Type 1 Diabetes (T1D). The functions of most of the non-HLA (Human Leukocyte Antigen) genetic variants remain unknown. We found that only 16 of these risk variants could potentially be linked to a protein-coding change. Therefore, we investigated whether these variants affected susceptibility by regulating changes in gene expression. To do so, we examined whole transcriptome profiles of 600 samples from the Type 1 Diabetes Genetics Consortium (T1DGC). These comprised four different immune cell types (Epstein-Barr virus (EBV)-transformed B cells, either basal or after stimulation; and cluster of differentiation (CD)4+ and CD8+ T cells). Many of the T1D-associated risk variants regulated expression of either neighboring (cis-) or distant (trans-) genes. In brief, 24 of the non-HLA T1D variants affected the expression of 31 nearby genes (cis) while 25 affected 38 distant genes (trans). The effects were highly significant (False Discovery Rate p < 0.001). In addition, we searched in public databases for expression effects of T1D single nucleotide polymorphisms (SNPs) in other immune cell types such as CD14+ monocytes, lipopolysaccharide (LPS) stimulated monocytes, and CD19+ B cells. In this paper, we review the (expression quantitative trait loci (eQTLs) associated with each of the 60 T1D variants and provide a summary of the genes impacted by T1D risk alleles in various immune cells. We then review the methodological steps involved in analyzing the function of genome wide association studies (GWAS)-identified variants, with emphasis on those affecting gene expression. We also discuss recent advancements in the methodologies and their advantages. We conclude by suggesting future study designs that will aid in the study of T1D risk variants.

Selecting cases and controls for DNA sequencing studies using family histories of disease

Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome-wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

Regional differences in mitochondrial DNA methylation in human post-mortem brain tissue

Background

DNA methylation is an important epigenetic mechanism involved in gene regulation, with alterations in DNA methylation in the nuclear genome being linked to numerous complex diseases. Mitochondrial DNA methylation is a phenomenon that is receiving ever-increasing interest, particularly in diseases characterized by mitochondrial dysfunction; however, most studies have been limited to the investigation of specific target regions. Analyses spanning the entire mitochondrial genome have been limited, potentially due to the amount of input DNA required. Further, mitochondrial genetic studies have been previously confounded by nuclear-mitochondrial pseudogenes. Methylated DNA Immunoprecipitation Sequencing is a technique widely used to profile DNA methylation across the nuclear genome; however, reads mapped to mitochondrial DNA are often discarded. Here, we have developed an approach to control for nuclear-mitochondrial pseudogenes within Methylated DNA Immunoprecipitation Sequencing data. We highlight the utility of this approach in identifying differences in mitochondrial DNA methylation across regions of the human brain and pre-mortem blood.

Results

We were able to correlate mitochondrial DNA methylation patterns between the cortex, cerebellum and blood. We identified 74 nominally significant differentially methylated regions (p < 0.05) in the mitochondrial genome, between anatomically separate cortical regions and the cerebellum in matched samples (N = 3 matched donors). Further analysis identified eight significant differentially methylated regions between the total cortex and cerebellum after correcting for multiple testing. Using unsupervised hierarchical clustering analysis of the mitochondrial DNA methylome, we were able to identify tissue-specific patterns of mitochondrial DNA methylation between blood, cerebellum and cortex.

Conclusions

Our study represents a comprehensive analysis of the mitochondrial methylome using pre-existing Methylated DNA Immunoprecipitation Sequencing data to identify brain region-specific patterns of mitochondrial DNA methylation.

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies

Background

Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2.

Results

We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes.

Conclusions

These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1142-6) contains supplementary material, which is available to authorized users.

Familial vs. sporadic sarcoidosis: BTNL2 polymorphisms, clinical presentations, and outcomes in a French cohort

Background

The occurrence of familial forms of sarcoidosis (OMIM 181100) suggests a genetic predisposition. The involvement of butyrophilin-like 2 (BTNL2) gene (rs2076530 variant) has to be investigated.

Results

The study performed independent analyses of BTNL2 polymorphism, clinical phenotypes, and outcomes in familial vs. sporadic presentations in 256 sporadic and 207 familial cases from 140 families. The logistic multivariate model showed that a young age at diagnosis and the combination of lung and skin involvement at diagnosis may distinguish sporadic from familial sarcoidosis (p = 0.016 and p = 0.041). We observed also that Sarcoid Clinical Activity Classification (SCAC) profiles were significantly different between familial and sporadic cases (p = 0.0497).

Variant rs2076530 was more frequent in patients than in controls (OR = 2.02; 95% CI: [1.32–3.09]) but showed no difference between sporadic and familial cases and no difference according to the clinical phenotype or the outcome.

Conclusion

Despite a significant difference in BTNL2 polymorphism between sarcoid patients and controls, there was no such difference between familial and sporadic sarcoidosis cases and no correlation between BTNL2 polymorphism and disease severity or outcome. Thus, BTNL2 difference cannot be considered as a key marker for disease classification or patient management.