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Background We previously performed targeted sequencing of autism risk genes in probands from the Autism Clinical and Genetic Resources in China (ACGC) (phase I). Here, we expand this analysis to a larger cohort of patients (ACGC phase II) to better understand the prevalence, inheritance, and genotype–phenotype correlations of likely gene-disrupting (LGD) mutations for autism candidate genes originally identified in cohorts of European descent. Methods We sequenced 187 autism candidate genes in an additional 784 probands and 85 genes in 599 probands using single-molecule molecular inversion probes. We tested the inheritance of potentially pathogenic mutations, performed a meta-analysis of phase I and phase II data and combined our results with existing exome sequence data to investigate the phenotypes of carrier parents and patients with multiple hits in different autism risk genes. Results We validated recurrent, LGD, de novo mutations (DNMs) in 13 genes. We identified a potential novel risk gene ( ZNF292 ), one novel gene with recurrent LGD DNMs ( RALGAPB ), as well as genes associated with macrocephaly ( GIGYF2 and WDFY3 ). We identified the transmission of private LGD mutations in genes predominantly associated with DNMs and showed that parental carriers tended to share milder autism-related phenotypes. Patients that carried DNMs in two or more candidate genes show more severe phenotypes. Conclusions We identify new risk genes and transmission of deleterious mutations in genes primarily associated with DNMs. The fact that parental carriers show milder phenotypes and patients with multiple hits are more severe supports a multifactorial model of risk.

Mucociliary clearance (MCC) is one of the most important defence mechanisms of the human respiratory system. Its failure is implicated in many chronic and debilitating airway diseases. However, due to the complexity of lung organization, we currently lack full understanding on the relationship between these regional differences in anatomy and biology and MCC functioning. For example, it is unknown whether the regional variability of airway geometry, cell biology and ciliary mechanics play a functional role in MCC. It therefore remains unclear whether the regional preference seen in some airway diseases could originate from local MCC dysfunction. Though great insights have been gained into the genetic basis of cilia ultrastructural defects in airway ciliopathies, the scaling to regional MCC function and subsequent clinical phenotype remains unpredictable. Understanding the multiscale mechanics of MCC would help elucidate genotype–phenotype relationships and enable better diagnostic tools and treatment options. Here, we review the hierarchical and variable organization of ciliated airway epithelium in human lungs and discuss how this organization relates to MCC function. We then discuss the relevancy of these structure–function relationships to current topics in lung disease research. Finally, we examine how state-of-the-art computational approaches can help address existing open questions. This article is part of the Theo Murphy meeting issue ‘Unity and diversity of cilia in locomotion and transport’.

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype‐phenotype relationships remains elusive. Here, we report the large‐scale measurement of the genotype‐phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli , LacI. Using a method that combines long‐read and short‐read DNA sequencing, we quantitatively measure the dose‐response curves for nearly 10 5 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence‐structure‐function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural‐network models, but unpredictable changes also occur. For example, we were surprised to discover a new band‐stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions. SYNOPSIS A large‐scale approach is used to measure the dose‐response curves of > 60,000 variants of the lac repressor. The results reveal systematic sequence‐structure‐function relationships underlying allostery, as well as a surprising diversity of allosteric phenotypes. Long‐read and short‐read DNA sequencing is used to measure the genotype and dose‐response phenotype of 62,000 variants of the lac repressor. The effects of single amino acid substitutions reveal systematic sequence‐structure‐function relationships underlying LacI allostery that may apply to allosteric proteins more generally. Novel phenotypes emerge with few substitutions, including variants with inverted dose‐response curves and variants with biphasic dose‐response curves. Graphical Abstract A large‐scale approach is used to measure the dose‐response curves of > 60,000 variants of the lac repressor. The results reveal systematic sequence‐structure‐function relationships underlying allostery, as well as a surprising diversity of allosteric phenotypes.

Summary Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome‐wide association approach. We provide re‐sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.

Understanding genotype–phenotype relationship is fundamental in biology. With the benefit from next-generation sequencing and high-throughput phenotyping methodologies, there have been generated much genome and phenome data for Saccharomyces cerevisiae. This makes it an excellent model system to understand the genotype–phenotype relationship. In this paper, we presented the reconstruction and application of the yeast pan-genome in resolving genotype–phenotype relationship by a machine learning-assisted approach.

IntroductionSarcoidosis is a highly variable disease in terms of organ involvement, type of onset and course. Associations of genetic polymorphisms with sarcoidosis phenotypes have been observed and suggest genetic signatures.MethodsAfter obtaining a positive vote of the competent ethics committee we genotyped 1909 patients of the deeply phenotyped Genetic-Phenotype Relationship in Sarcoidosis (GenPhenReSa) cohort of 31 European centers in 12 countries with 116 potentially disease-relevant single-nucleotide polymorphisms (SNPs). Using a meta-analysis, we investigated the association of relevant phenotypes (acute vs. sub-acute onset, phenotypes of organ involvement, specific organ involvements, and specific symptoms) with genetic markers. Subgroups were built on the basis of geographical, clinical and hospital provision considerations.ResultsIn the meta-analysis of the full cohort, there was no significant genetic association with any considered phenotype after correcting for multiple testing. In the largest sub-cohort (Serbia), we confirmed the known association of acute onset with TNF and reported a new association of acute onset an HLA polymorphism. Multi-locus models with sets of three SNPs in different genes showed strong associations with the acute onset phenotype in Serbia and Lublin (Poland) demonstrating potential region-specific genetic links with clinical features, including recently described phenotypes of organ involvement.DiscussionThe observed associations between genetic variants and sarcoidosis phenotypes in subgroups suggest that gene–environment-interactions may influence the clinical phenotype. In addition, we show that two different sets of genetic variants are permissive for the same phenotype of acute disease only in two geographic subcohorts pointing to interactions of genetic signatures with different local environmental factors. Our results represent an important step towards understanding the genetic architecture of sarcoidosis.

Background Cornelia de Lange syndrome (CdLS) is a rare congenital developmental disorder with variable multisystem involvement and genetic heterogeneity. We aimed to analyze the clinical and genetic characteristics of Chinese individuals with CdLS. Methods We collected data regarding the neonatal period, maternal status, clinical manifestation, including facial dimorphisms and development, and follow‐up treatment for individuals diagnosed with CdLS. In individuals with suspected CdLS, high‐throughput sequencing, Sanger sequencing, and real‐time qualitative PCR were used to verify the diagnosis. Results Variants, including six that were novel, were concentrated in the NIPBL (70%), HDAC8 (20%), and SMC3 (10%) genes. We found two nonsense, three splicing, and two deletion variants in NIPBL; a missense variant and an absence variant in HDAC8; and a missense variant in SMC3. Eleven cardinal features of CdLS were present in more than 80% of Chinese individuals. Compared with non‐Chinese individuals of diverse ancestry, there were significant differences in the clinical characteristics of eight of these features. Conclusion Six novel pathological variants were identified; thus, the study expanded the gene variant spectrum. Furthermore, most cardinal features of CdLS found in Chinese individuals were also found in individuals from other countries. However, there were significant differences in eight clinical features. My article is the most number reports in China on accurate diagnosis of CdLS in the level of gene, including 6 novel gene mutations of new case reports. The successful case of growth hormone application was added to the exisiting literature, providing a new approach for the treatment of CdLS. The impact is expanding the gene spectrum of CdLS and the relationship between genotype and phenotype for CdLS.

Combining ecophysiological modelling and genetic mapping has increasingly received attention from researchers who wish to predict complex plant or crop traits under diverse environmental conditions. The potential for using this combined approach to predict flowering time of individual genotypes in a recombinant inbred line (RIL) population of spring barley (Hordeum vulgare L.) was examined. An ecophysiological phenology model predicts preflowering duration as affected by temperature and photoperiod, based on the following four input traits: fo (the minimum number of days to flowering at the optimum temperature and photoperiod), θ1 and θ2 (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and δ (the photoperiod sensitivity). The model-input trait values were obtained from a photoperiod-controlled greenhouse experiment. Assuming additivity of QTL effects, a multiple QTL model was fitted for the model-input traits using composite interval mapping. Four to seven QTL were identified for each trait. Each trait had at least one QTL specific to that trait alone. Other QTL were shared by two or all traits. Values of the model-input traits predicted for the RILs from the QTL model were fed back into the ecophysiological model. This QTL-based ecophysiological model was subsequently used to predict preflowering duration (d) for eight field trial environments. The model accounted for 72% of the observed variation among 94 RILs and 94% of the variation among the two parents across the eight environments, when observations in different environments were pooled. However, due to the low percentage (34–41%) of phenotypic variation accounted for by the identified QTL for three model-input traits (θ1, θ2 and δ), the QTL-based model accounted for somewhat less variation among the RILs than the model using original phenotypic input trait values. Nevertheless, days to flowering as predicted from the QTL-based ecophysiological model were highly correlated with days to flowering as predicted from QTL-models per environment for days to flowering per se. The ecophysiological phenology model was thus capable of extrapolating (QTL) information from one environment to another.

Background Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder caused by mutations in the arylsulfatase A gene ( ARSA ) and categorized into three subtypes according to age of onset. The functional effect of most ARSA mutants remains unknown; better understanding of the genotype–phenotype relationship is required to support newborn screening (NBS) and guide treatment. Results We collected a patient data set from the literature that relates disease severity to ARSA genotype in 489 individuals with MLD. Patient-based data were used to develop a phenotype matrix that predicts MLD phenotype given ARSA alleles in a patient’s genotype with 76% accuracy. We then employed a high-throughput enzyme activity assay using mass spectrometry to explore the function of ARSA variants from the curated patient data set and the Genome Aggregation Database (gnomAD). We observed evidence that 36% of variants of unknown significance (VUS) in ARSA may be pathogenic. By classifying functional effects for 251 VUS from gnomAD, we reduced the incidence of genotypes of unknown significance (GUS) by over 98.5% in the overall population. Conclusions These results provide an additional tool for clinicians to anticipate the disease course in MLD patients, identifying individuals at high risk of severe disease to support treatment access. Our results suggest that more than 1 in 3 VUS in ARSA may be pathogenic. We show that combining genetic and biochemical information increases diagnostic yield. Our strategy may apply to other recessive diseases, providing a tool to address the challenge of interpreting VUS within genotype–phenotype relationships and NBS.

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42–77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.