A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence

Psychiatric Genomics 2025: State of the Art and the Path Forward

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identified, functional interpretation of the associated loci becomes the critical next step toward translational applications. The article discusses achievements, challenges, and opportunities in psychiatric genomics associated with complexity and heterogeneity. Brain expression quantitative trait loci, single-cell ribonucleic acid-sequence, and functional genomics technologies are highlighted. It also covers newly developed techniques with improved spatiotemporal resolution, quality and sensitivity, coupled with advanced analytical methods and artificial intelligence. The power of collaborative research and inclusion of diverse populations will ensure a bright future for precision psychiatry.

Genome-wide fine-mapping improves identification of causal variants

Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.

Genome-wide fine-mapping improves identification of causal variants

Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 599 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.

Relative Contributions of g and Basic Domain-Specific Mathematics Skills to Complex Mathematics Competencies

Meta-analytic structural equation modeling was used to estimate the relative contributions of general cognitive ability or g (defined by executive functions, short-term memory, and intelligence) and basic domain-specific mathematical abilities to performance in more complex mathematics domains. The domain-specific abilities included mathematics fluency (e.g., speed of retrieving basic facts), computational skills (i.e., accuracy at solving multi-step arithmetic, algebra, or geometry problems), and word problems (i.e., mathematics problems presented in narrative form). The core analysis included 448 independent samples and 431,344 participants and revealed that g predicted performance in all three mathematics domains. Mathematics fluency contributed to the prediction of computational skills, and both mathematics fluency and computational skills predicted word problem performance, controlling g. The relative contribution of g was consistently larger than basic domain-specific abilities, although the latter may be underestimated. The patterns were similar across younger and older individuals, individuals with and without a disability (e.g., learning disability), concurrent and longitudinal assessments, and family socioeconomic status, and have implications for fostering mathematical development.

Identification and validation of a regulatory mutation upstream of the BMP2 gene associated with carcass length in pigs

Background

Carcass length is very important for body size and meat production for swine, thus understanding the genetic mechanisms that underly this trait is of great significance in genetic improvement programs for pigs. Although many quantitative trait loci (QTL) have been detected in pigs, very few have been fine-mapped to the level of the causal mutations. The aim of this study was to identify potential causal single nucleotide polymorphisms (SNPs) for carcass length by integrating a genome-wide association study (GWAS) and functional assays.

Results

Here, we present a GWAS in a commercial Duroc × (Landrace × Yorkshire) (DLY) population that reveals a prominent association signal (P = 4.49E−07) on pig chromosome 17 for carcass length, which was further validated in two other DLY populations. Within the detected 1 Mb region, the BMP2 gene stood out as the most likely causal candidate because of its functions in bone growth and development. Whole-genome gene expression studies showed that the BMP2 gene was differentially expressed in the cartilage tissues of pigs with extreme carcass length. Then, we genotyped an additional 267 SNPs in 500 selected DLY pigs, followed by further whole-genome SNP imputation, combined with deep genome resequencing data on multiple pig breeds. Reassociation analyses using genotyped and imputed SNP data revealed that the rs320706814 SNP, located approximately 123 kb upstream of the BMP2 gene, was the strongest candidate causal mutation, with a large association with carcass length, with a ~ 4.2 cm difference in length across all three DLY populations (N = 1501; P = 3.66E−29). This SNP segregated in all parental lines of the DLY (Duroc, Large White and Landrace) and was also associated with a significant effect on body length in 299 pure Yorkshire pigs (P = 9.2E−4), which indicates that it has a major value for commercial breeding. Functional assays showed that this SNP is likely located within an enhancer and may affect the binding affinity of transcription factors, thereby regulating BMP2 gene expression.

Conclusions

Taken together, these results suggest that the rs320706814 SNP on pig chromosome 17 is a putative causal mutation for carcass length in the widely used DLY pigs and has great value in breeding for body size in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00689-0.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Genome-wide association study identifies new loci associated with noise-induced tinnitus in Chinese populations

Background

Tinnitus is an auditory phantom sensation in the absence of an acoustic stimulus, which affects nearly 15% of the population. Excessive noise exposure is one of the main causes of tinnitus. To now, the knowledge of the genetic determinants of susceptibility to tinnitus remains limited.

Results

We performed a two-stage genome-wide association study (GWAS) and identified that two single nucleotide polymorphisms (SNPs), rs2846071 located in the intergenic region at 11q13.5 (odds ratio [OR] = 2.14, 95% confidence interval [CI] = 1.96–3.40, combined P = 4.89 × 10^− 6) and rs4149577 located in the intron of TNFRSF1A gene at 12p13.31 (OR = 2.05, 95% CI = 1.89–2.51, combined P = 6.88 × 10^− 6), are significantly associated with the susceptibility to noise-induced tinnitus. Furthermore, the expression quantitative trait loci (eQTL) analyses revealed that rs2846071 is significantly correlated with the expression of WNT11 gene, and rs4149577 with the expression of TNFRSF1A gene in multiple brain tissues (all P < 0.05). The newly identified candidate gene WNT11 is involved in Wnt pathway, and TNFRSF1A in the tumor necrosis factor pathway, respectively. Pathway enrichment analyses also showed that these two pathways are closely relevant to tinnitus.

Conclusions

Our findings highlight two novel loci at 11q13.5 and 12p13.31 conferring susceptibility to noise-induced tinnitus. and suggest that the WNT11 and TNFRSF1A genes might be the candidate causal targets of 11q13.5 and 12p13.31 loci, respectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-00987-y.

Mitochondrial Functions, Cognition, and the Evolution of Intelligence: Reply to Commentaries and Moving Forward

In response to commentaries, I address questions regarding the proposal that general intelligence (g) is a manifestation of the functioning of intramodular and intermodular brain networks undergirded by the efficiency of mitochondrial functioning (Geary 2018). The core issues include the relative contribution of mitochondrial functioning to individual differences in g; studies that can be used to test associated hypotheses; and, the adaptive function of intelligence from an evolutionary perspective. I attempt to address these and related issues, as well as note areas in which other issues remain to be addressed.

Genome-wide association study identifies 7q11.22 and 7q36.3 associated with noise-induced hearing loss among Chinese population

Noise-induced hearing loss (NIHL) seriously affects the life quality of humans and causes huge economic losses to society. To identify novel genetic loci involved in NIHL, we conducted a genome-wide association study (GWAS) for this symptom in Chinese populations. GWAS scan was performed in 89 NIHL subjects (cases) and 209 subjects with normal hearing who have been exposed to a similar noise environment (controls), followed by a replication study consisting of 53 cases and 360 controls. We identified that four candidate pathways were nominally significantly associated with NIHL, including the Erbb, Wnt, hedgehog and intraflagellar transport pathways. In addition, two novel index single-nucleotide polymorphisms, rs35075890 in the intron of AUTS2 gene at 7q11.22 (combined P = 1.3 × 10^-6 ) and rs10081191 in the intron of PTPRN2 gene at 7q36.3 (combined P = 2.1 × 10^-6 ), were significantly associated with NIHL. Furthermore, the expression quantitative trait loci analyses revealed that in brain tissues, the genotypes of rs35075890 are significantly associated with the expression levels of AUTS2, and the genotypes of rs10081191 are significantly associated with the expressions of PTPRN2 and WDR60. In conclusion, our findings highlight two novel loci at 7q11.22 and 7q36.3 conferring susceptibility to NIHL.

Revisiting genome-wide association studies from statistical modelling to machine learning

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures-statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene-gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS.