Genome wide complex trait analysis

History

Estimation in biology/animal breeding using standard ANOVA/REML methods of variance components such as heritability, shared-environment, maternal effects etc. typically requires individuals of known relatedness such as parent/child; this is often unavailable or the pedigree data unreliable, leading to inability to apply the methods or requiring strict laboratory control of all breeding (which threatens the external validity of all estimates), and several authors have noted that relatedness could be measured directly from genetic markers (and if individuals were reasonably related, economically few markers would have to be obtained for statistical power), leading Kermit Ritland to propose in 1996 that directly measured pairwise relatedness could be compared to pairwise phenotype measurements (Ritland 1996, "A Marker-based Method for Inferences About Quantitative Inheritance in Natural Populations") to combine estimated genetic relatedness with phenotypic measurements to estimate variance components such as heritability or genetic correlations. and subsequently applied to plants/animals

As genome sequencing costs dropped steeply over the 2000s, acquiring enough markers on enough subjects for reliable estimates using very distantly related individuals became possible. An early application of the method to humans came with Visscher et al. 2006/2007, which used SNP markers to estimate the actual relatedness of siblings and estimate heritability from the direct genetics. In humans, unlike the original animal/plant applications, relatedness is usually known with high confidence in the 'wild population', and the benefit of GCTA is connected more to avoiding assumptions of classic behavioral genetics designs and verifying their results, and partitioning heritability by SNP class and chromosomes. The first use of GCTA proper in humans was published in 2010, finding 45% of variance in human height can be explained by the included SNPs. (Large GWASes on height have since confirmed the estimate.) The GCTA algorithm was then described and a software implementation published in 2011. It has since been used to study a wide variety of biological, medical, psychiatric, and psychological traits in humans, and inspired many variant approaches.

Robust heritability

Twin and family studies have long been used to estimate variance explained by particular categories of genetic and environmental causes. Across a wide variety of human traits studied, there is typically minimal shared-environment influence, considerable non-shared environment influence, and a large genetic component (mostly additive), which is on average ~50% and sometimes much higher for some traits such as height or intelligence. However, the twin and family studies have been criticized for their reliance on a number of assumptions that are difficult or impossible to verify, such as the equal environments assumption (that the environments of monozygotic and dizygotic twins are equally similar), that there is no misclassification of zygosity (mistaking identical for fraternal & vice versa), that twins are unrepresentative of the general population, and that there is no assortative mating. Violations of these assumptions can result in both upwards and downwards bias of the parameter estimates. (This debate & criticism have particularly focused on the heritability of IQ.)

The use of SNP or whole-genome data from unrelated subject participants (with participants too related, typically >0.025 or ~fourth cousins levels of similarity, being removed, and several principal components included in the regression to avoid & control for population stratification) bypasses many heritability criticisms: twins are often entirely uninvolved, there are no questions of equal treatment, relatedness is estimated precisely, and the samples are drawn from a broad variety of subjects.

In addition to being more robust to violations of the twin study assumptions, SNP data can be easier to collect since it does not require rare twins and thus also heritability for rare traits can be estimated (with due correction for ascertainment bias).

GWAS power

GCTA estimates can be used to resolve the missing heritability problem and design GWASes which will yield genome-wide statistically-significant hits. This is done by comparing the GCTA estimate with the results of smaller GWASes. If a GWAS of n=10k using SNP data fails to turn up any hits, but the GCTA indicates a high heritability accounted for by SNPs, then that implies that there are a large number of polygenic variants and thus that much larger GWASes will be required to accurately estimate each SNP's effects and directly account for a fraction of the GCTA heritability.

Disadvantages

1. Limited inference: GCTA estimates are inherently limited in that they cannot estimate broadsense heritability like twin/family studies. Hence, while they serve as a critical check on the unbiasedness of the twin/family studies, GCTAs cannot replace them for estimating total genetic contributions to a trait.
2. Substantial data requirements: the number of SNPs sequenced per person should be in the thousands and ideally the hundreds of thousands for reasonable estimates of genetic similarity (although this is no longer such an issue for current commercial chips which default to hundreds of thousands or millions of markers); and the number of persons, for somewhat stable estimates of plausible SNP heritability, should be at least n>1000 and ideally n>10000. In contrast, twin studies can offer precise estimates with a fraction of the sample size.
3. Computational inefficiency: The original GCTA implementation scales poorly with increasing data size ( O ( SNPs ⋅ n 2 ) ), so even if enough data is available for precise GCTA estimates, the computational burden may be unfeasible. GCTA can be meta-analyzed as a standard precision-weighted fixed-effect meta-analysis, so research groups sometimes estimate cohorts or subsets and then pool them meta-analytically (at the cost of additional complexity and some loss of precision). This has motivated the creation of faster implementations and variant algorithms which make different assumptions, such as using moment matching
4. Need for raw data: GCTA requires genetic similarity of all subjects and thus their raw genetic information; due to privacy concerns, individual patient data is rarely shared. GCTA cannot be run on the summary statistics reported publicly by many GWAS projects, and if pooling multiple GCTA estimates, meta-analysis must be done.
   In contrast, there are alternative techniques which operate on summaries reported by GWASes without requiring the raw data e.g. "LD score regression" contrasts linkage disequilibrium statistics (available from public datasets like 1000 Genomes) with the public summary effect-sizes to infer heritability and estimate genetic correlations/overlaps of multiple traits. The Broad Institute runs LD Hub which provides a public web interface to >=177 traits with LD score regression. Another method using summary data is HESS.
5. Confidence intervals may be incorrect, or outside the 0-1 range of heritability, and highly imprecise due to asymptotics

Interpretation

GCTA estimates are often misinterpreted as "the total genetic contribution", and since they are often much less than the twin study estimates, the twin studies are presumed to be biased and the genetic contribution to a particular trait is minor. This is incorrect, as GCTA estimates are lower bounds.

A more correct interpretation would be that: GCTA estimates are the expected amount of variance that could be predicted by an indefinitely large GWAS using a simple additive linear model (without any interactions or higher-order effects) in a particular population at a particular time given the limited selection of SNPs and a trait measured with a particular amount of precision. Hence, there are many ways to exceed GCTA estimates:

1. SNP genotyping data is typically limited to 200k-1m of the most common or scientifically interesting SNPs, though 150 million+ have been documented by genome sequencing; as SNP prices drop and arrays become more comprehensive or whole-genome sequencing replaces SNP genotyping entirely, the expected narrowsense heritability will increase as more genetic variants are included in the analysis. The selection can also be expanded considerably using haplotypes and imputation (SNPs can proxy for unobserved genetic variants which they tend to be inherited with); e.g. Yang et al. 2015 finds that with more aggressive use of imputation to infer unobserved variants, the height GCTA estimate expands to 56% from 45%, and Hill et al. 2017 finds that expanding GCTA to cover rarer variants raises the intelligence estimates from ~30% to ~53% and explains all the heritability in their sample. Additional genetic variants include de novo mutations/mutation load & structural variations such as copy-number variations.
2. narrowsense heritability estimates assume simple additivity of effects, ignoring interactions. As some of trait values will be due to these more complicated effects, the total genetic effect will exceed that of the subset measured by GCTA, and as the additive SNPs are found & measured, it will become possible to find interactions as well using more sophisticated statistical models.
3. all correlation & heritability estimates are biased downwards to zero by the presence of measurement error; the need for adjusting this leads to techniques such as Spearman's correction for measurement error, as the underestimate can be quite severe for traits where large-scale and accurate measurement is difficult and expensive, such as intelligence. For example, an intelligence GCTA estimate of 0.31, based on an intelligence measurement with test-retest reliability r = 0.65 , would after correction ( 0.31 1 ⋅ 0.65 2 = 0.691 2 ), be a true estimate of ~0.48, indicating that common SNPs alone explain half of variance. Hence, a GWAS with a better measurement of intelligence can expect to find more intelligence hits than indicated by a GCTA based on a noisier measurement.

Implementations

The original "GCTA" software package is the most widely used; its primary functionality covers the GREML estimation of SNP heritability, but includes other functionality:

Estimate the genetic relationship from genome-wide SNPs;

Estimate the inbreeding coefficient from genome-wide SNPs;

Estimate the variance explained by all the autosomal SNPs;

Partition the genetic variance onto individual chromosomes;

Estimate the genetic variance associated with the X-chromosome;

Test the effect of dosage compensation on genetic variance on the X-chromosome;

Predict the genome-wide additive genetic effects for individual subjects and for individual SNPs;

Estimate the LD structure encompassing a list of target SNPs;

Simulate GWAS data based upon the observed genotype data;

Convert Illumina raw genotype data into PLINK format;

Conditional & joint analysis of GWAS summary statistics without individual level genotype data

Estimating the genetic correlation between two traits (diseases) using SNP data

Mixed linear model association analysis

Other implementations and variant algorithms include:

FAST-LMM

FAST-LMM-Select: like GCTA in using ridge regression but including feature selection to try to exclude irrelevant SNPs which only add noise to the relatedness estimates

LMM-Lasso

GEMMA

EMMAX

REACTA (formerly ACTA) claims order of magnitude runtime reductions

BOLT-REML/BOLT-LMM (manual), faster & better scaling; with potentially better efficiency in the meta-analysis scenario

MEGHA

PLINK >1.9 (December 2013) supports "the use of genetic relationship matrices in mixed model association analysis and other calculations"

Traits

GCTA estimates frequently find estimates 0.1-0.5, consistent with broadsense heritability estimates (with the exception of personality traits, for which theory & current GWAS results suggest non-additive genetics driven by frequency-dependent selection). Traits univariate GCTA has been used on (excluding SNP heritability estimates computed using other algorithms such as LD score regression, and bivariate GCTAs which are listed in genetic correlation) include (point-estimate format: " h S N P 2 (standard error)"):

Anthropometric

Height: 0.544(0.101), 0.498(0.04), 0.56(0.023), 0.448(0.029), 0.42(0.052), 0.69(0.14), 0.48(0.17) 0.37(0.14): 0.32(0.06), 0.35(0.12), 0.44(0.09), 0.40(0.09)/0.33(0.09), 0.62(0.061), 0.687(0.016), 0.56(0.23), 0.51(0.01), 0.47(0.15)/0.69(0.08)

weight: 0.48(0.14), 0.41(0.12), 0.25(0.09), 0.26(0.061)

Body mass index (BMI): 0.42(0.17) 0.14(0.05), 0.50(0.05) 0.31(0.07), 0.43(0.10), 0.21(0.061), 0.424(0.018), 0.27(0.025), 0.165(0.029), 0.24(0.01), 0.26 (0.08)

in children: 0.37(0.15)

waist-to-hip ratio (WHR): 0.13(0.05) 0.188(0.037)

waist circumference: 0.16(0.061)

Breast size: 0.31(0.16)/0.47(0.25)

Health (self-rated): 0.177(0.089), 0.13(0.006)

Hair color:

Blond: 0.165(0.081)

Brown: 0.095(0.079)

Red: 0.246(0.087)

Black: 0.00(0.083)

Light versus dark: 0.140(0.080)

unibrow: 0.28(0.02)

Male pattern hair loss (balding): autosomal SNPs, 0.473(0.013), X chromosome, 0.046(0.03), 0.519 total

age at menarche: 0.451(0.022)

age at first birth: 0.15(0.04), 0.19(0.039)

age at menopause: 0.409(0.048)

sex ratio of offspring: 0.026(0.017)

number of offspring: 0.073(0.068)/0.102(0.028), 0.10(0.05), 0.22(0.026), 0.21(0.05), 0.20(0.10), 0.19(0.09)

left handedness: 0.004(0.145)

Eye color: 0.59(0.01)

Eye dimensions (axial length & corneal curvature): 0.46(0.16)/0.42(0.16)

Cilantro tasting: 0.087

cry cutting onions: 0.12(0.02)

sweet vs salty: 0.35(0.03)-

Social/behavioral

Education: 0.224(0.042), 0.21(0.06), 0.158(0.061), 0.21(0.05), 0.17(0.07), 0.33 (0.10), 0.23(0.09), 0.156(0.021)

rare/family variants: 0.281(0.03)

test scores: 0.31(0.12)

reading scores: 0.27(0.128)

mathematics scores: 0.52 (0.163)

Socioeconomic status (SES): 0.18(0.05), 0.18(0.12)/0.19(0.12), 0.18(0.12)/0.19(0.12)

social deprivation: 0.21(0.005)

household income: 0.11(0.007)

Exercise:

Moderate to Vigorous Activity: 0.17(0.09)

Sedentary Time: 0.25(0.09)

Total Physical Activity: 0.21(0.10)

Tiredness: 0.084(0.006)

Insomnia: 0.08(0.02)

Chronotype/morningness: 0.25(0.03), 0.194(?), 0.377(?)

Adult antisocial behavior: 0.55(0.41)

trust

trust in people: 0.07(0.17)

trust in friends: 0.06(0.24)

loneliness: 0.27(0.12)

Non-substance related Behavioral Disinhibition: 0.28(0.102), 0.19(0.16)

Stressful life events: 0.3(0.15)

carsickness: 0.2(0.01)

Psychological

Overall brain size: 0.845(0.457)/0.00(0.476)/0.00(0.483)/0.574(0.468), 0.54(0.23)/0.44(0.23)/0.53(0.23)/0.22(0.24)/0.16(0.23)/0.31(0.23)/0.54(0.23)/0.45(0.23)/0.52(0.23)

Volume of neuroanatomical structures:

Accumbens Area: 0.001(0.279)

Amygdala: 0.096(0.279)

Caudate: 0.620(0.279)

Cerebellum: 0.002(0.279)

Corpus Callosum: 0.521(0.279)

Hippocampus: 0.001(0.279)

Lateral Ventricle: 0.266(0.279)

3rd Ventricle: 0.534(0.279)

4th Ventricle: 0.392(0.279)

Pallidum: 0.259(0.279)

Putamen: 0.310(0.279)

Thalamus: 0.227(0.279)

Global:

Intracranial volume: 0.880(0.238)

Overall mean cortical thickness: 0.796(0.244)

Frontal:

Left precentral gyrus thickness: 0.718(0.249)

Left rostral anterior cingulate cortex thickness: 0.737(0.243)

Left superior frontal gyrus thickness: 0.597(0.246)

Right lateral orbital frontal cortex thickness: 0.483(0.240)

Right pars opercularis surface area: 0.545(0.252)

Right paracentral lobule thickness: 0.494(0.252)

Right precentral gyrus thickness: 0.731(0.244)

Occipital:

Left cuneus cortex thickness: 0.550(0.244)

Left lateral occipital cortex thickness: 0.498(0.248)

Right cuneus cortex thickness: 0.723(0.251)

Parietal:

Left inferior parietal cortex thickness: 0.566(0.248)

Left postcentral gyrus thickness: 0.501(0.249)

Left posterior-cingulate cortex thickness: 0.601(0.246)

Left precuneus cortex surface area: 0.555(0.262)

Left precuneus cortex thickness: 0.896(0.245)

Left superior parietal gyrus surface area: 0.558(0.251)

Left superior parietal gyrus thickness: 0.903(0.241)

Right postcentral gyrus thickness: 0.760(0.246)

Right precuneus cortex surface area: 0.547(0.246)

Right precuneus cortex thickness: 0.965(0.243)

Right superior parietal gyrus thickness: 0.941(0.239)

Right supramarginal gyrus thickness: 0.769(0.240)

Temporal:

Left banks superior temporal sulcus thickness: 0.680(0.242)

Left entorhinal cortex thickness: 0.587(0.249)

Left fusiform gyrus surface area: 0.566(0.259)

Left insula cortex surface area: 0.561(0.251)

Left superior temporal gyrus surface area: 0.658(0.244)

Left transverse temporal cortex thickness: 0.555(0.245)

Right entorhinal cortex surface area: 0.651(0.251)

Right insula cortex surface area: 0.878(0.252)

Right middle temporal gyrus surface area: 0.610(0.244)

Right temporal pole surface area: 0.524(0.249)

Right transverse temporal cortex thickness: 0.536(0.254)

Shape of neuroanatomical structures:

Accumbens Area: 0.230(0.134)

Amygdala: 0.036(0.138)

Caudate: 0.497(0.187)

Cerebellum: 0.456(0.190)

Corpus Callosum: 0.243(0.132)

Hippocampus: 0.339(0.168)

Lateral Ventricle: 0.207(0.152)

3rd Ventricle: 0.454(0.156)

4th Ventricle: 0.014(0.206)

Pallidum: 0.074(0.116)

Putamen: 0.365(0.146)

Thalamus Proper: 0.132(0.143)

Intelligence: 0.40(0.11)/0.51(0.11), 0.47(?), 0.24(0.20), 0.29(0.12)/0.26(0.11)/0.20(0.11)/0.35(0.12), 0.47(0.18)/0.26(0.17)/0.23(0.13)/0.15(0.14) 0.29(0.05), 0.35(0.11), 0.60(0.26), 0.32(0.14)/0.28(0.17), 0.40(0.21)/0.46(0.06), 0.56(0.25)/0.52(0.25), 0.29%(0.05)/0.28(0.07), 0.174(0.017), 0.00(?)/0.00(?), 0.31(0.018), 0.360(0.108), 0.23(0.02)

rare/family variants: 0.31(0.03)

reaction time: 0.11(0.06)

memory: 0.05(0.06), 0.00(?)/0.00(?)

working memory: 0.17(?)/0.07(?), 0.108(0.096)

Facial Memory: 0.064(0.093)

Spatial Memory: 0.028(0.090)

Verbal Memory: 0.244(0.097)

Digit Symbol Test: 0.214(0.021)

rare/family variants: 0.147 (0.028)

Logical memory: 0.119 (0.02)

rare/family variants:0.203 (0.028)

Abstraction and Mental Flexibility: 0.064(0.096)

Attention: 0.148(0.097)

Language Reasoning: 0.302(0.098)

vocabulary: 0.256(0.02)

rare/family variants: 0.301(0.028)

TOWRE word reading fluency: 0.74 (0.04)/0.68 (0.04)

Verbal fluency: 0.189(0.021)

rare/family variants: 0.271(0.029)

Wide Range Achievement Test (Reading): 0.433(0.098)

ART written/printed material exposure: 0.39(0.02)

Nonverbal Reasoning: 0.406(0.096)

Spatial Reasoning: 0.357(0.101)

Age Differentiation: 0.039(0.098)

Emotion Differentiation: 0.000(0.092)

Emotion Identification: 0.357(0.093)

Trailing Making test/visual-numeric reasoning

Trail Making test: 0.079(0.024)/0.224(0.026)/0.176(0.025)

Number sense: 0.00(0.29)

Economic preferences

risk aversion: 0.137(0.152)

patience: 0.085(0.148)

trust: 0.242(0.146)

fair-mindedness: 0.00(0.15)

Political preferences

immigration/crime: 0.203(0.147)

economic policy: 0.344(0.150)

environmentalism: 0.00(0.148)

feminism/equality: 0.00(0.147)

foreign policy: 0.354(0.149)

Happiness (self-rated): 0.05–0.10(0.05–0.10)

positive affect: 0.08(0.02)

life satisfaction: 0.13(0.02)

brain region activity response to faces

Big Five personality traits

Neuroticism: 0.06(0.03), 0.147(0.07)/0.157(0.16), 0.15(0.08), 0.156(0.0074), 0.15(0.02), 0.15(0.02), 0.108 (0.02)

rare/family variants: 0.192 (0.025)

Extraversion: 0.12(0.03) 0.00(0.15)/0.05(0.072), 0.08(0.08), 0.130 (0.017)

Openness: 0.21(0.08)

Conscientiousness: 0.01 (0.08), 0.16(0.02)

Agreeableness: 0.001(0.08)

Social Anxiety score: European-Americans: 0.12(0.033); African-Americans: 0.12(0.134); Hispanic: 0.21(0.102)

Cloninger's personality dimensions:

Harm Avoidance: 0.066(0.037)

Novelty Seeking: 0.099(0.036)

Reward Dependence: 0.042(0.036)

Persistence: 0.081(0.037)

optimism: 0.10(0.02)

Psychology endophenotypes:

Total power: ~0.08(?)

Theta power: ~0.04(?)

Delta power: ~0.15(?)

Beta power: ~0.19(?)

CZ alpha power: ~0.21(?)

O1O2 alpha power: ~0.45(?)

Alpha frequency: ~0.49(?)

SCL: ~0.23(?)

SCR amplitude: ~0.25(?)

SCR frequency: ~0.33(?)

EDA factor: ~0.35(?)

P3 amplitude: ~0.29(?)

Antisaccade: ~0.47(?)

Overall startle: ~0.49(?)

Psychiatric

Antisocial Process Screening Devise (APSD; Psychopathic Symptoms); composite:0.00(0.12)/0.15(0.16)

Callous-Unemotional: 0.02(0.12)/0.00(0.16), 0.07(0.12)

Impulsivity: 0.00(0.12)/0.24(0.16)

Narcissism total: 0.00(0.12)/0.50(0.16)

psychopathology in children: 0.38(0.16)

anxiety: 0.16(0.11)

epilepsy: 0.26(0.05)/0.27(0.06)

Depression: 0.21(0.021), 0.32(0.09)/0.32(0.086), 0.19(0.10), 0.15(0.02)

Age at onset: 0.17(0.10),

Episodicity: 0.09(0.14)

Moods and Feelings Questionnaire (MFQ; Depressive Symptoms): 0.00(0.1)/0.00(0.12)

patient response to antidepressive treatment: all response: 0.42(0.18), SSRI response: 0.428(0.23)

Schizophrenia: 0.23 (0.008), 0.23(0.01), 0.32(0.03), 0.39(0.12), 0.24(0.09)/0.28(0.03)/0.27(0.02), 0.274(0.007), 0.20(0.025)

Bipolar disorder: 0.25(0.012), 0.37(0.04) 0.59(0.06), 0.26(0.032), 0.26(0.032)

Borderline Personality: 0.23(0.09)

Tourette syndrome: 0.58(0.09)

Obsessive compulsive disorder: 0.37(0.07)

Empathy Quotient: 0.11(0.014)

Systemizing Quotient-Revised: 0.12(0.012)

Social and Communication Disorders Checklist (SCDC): 0.24(0.07)

Autism spectrum disorders: 0.17(0.025), 0.396(0.082)/0.498(0.118), 0.655(0.139), 0.494(0.096), 0.24(0.07)

Childhood Asperger Syndrome Test (CAST; Autistic-Like Symptoms); composite: 0.09(0.12)/0.00(0.16)

Communication: 0.00(0.12)/0.00(0.15)

Nonsocial: 0.00(0.12)/0.00(0.16)

Social: 0.06(0.12)/0.00(0.16)

male/female differences in autism etiology

ADHD: 0.28(0.023), 0.40(0.14), 0.42(0.13)

DSM-IV–based ADHD scale from the Conners' Parent Rating Scale–Revised (CPRS-R); Conners composite: 0.00(0.12)

Hyperactivity-impulsivity: 0.06(0.12)

Inattention: 0.00(0.12)

Child behavioral problems (ADHD, externalizing problems, total problems): 0.40(0.14)/0.37(0.14)/0.45(0.14)/0.20(0.14)/0.12(0.10)/0.12(0.10)/0.18(0.10)/0.16(0.11)/0.71(0.22)/0.44(0.22)/0.11(0.16)

childhood aggression: 0.10(0.06)/0.54(0.19)/0.46(0.35)/0.08(0.06)

Preschool internalizing problems: 0.26(0.07)/0.18(0.30)/0.13(0.33)

Strengths and Difficulties Questionnaire (SDQ; Behavior Problems); composite: 0.00(0.1)/0.00(0.12)/0.11(0.15)

Anxiety: 0.02(0.12)/0.00(0.12)/0.11(0.15)

Conduct: 0.00(0.12)/0.00(0.12)/0.26(0.15)

Hyperactivity: 0.00(0.12)/0.00(0.12)/0.05(0.15)

Peer problems: 0.00(0.1)/0.16(0.12)/0.00(0.15), 0.04(0.05)/0.06(0.05)/0.11(0.06)/0.02(0.05)

Psychotism:

Paranoia 0.14(0.13)

Hallucinations: 0.00(0.12)

Cognitive Disorganization: 0.19(0.13)

Grandiosity: 0.17(0.13)

Anhedonia: 0.20(0.12)

Negative Symptoms: 0.00(0.12)

Parkinson's Disease: 0.22(0.02), 0.27(0.05), 0.28(0.05)

Early onset: 0.15(0.14)

Late onset: 0.31(0.07)

dementia with Lewy bodies: 0.31(0.03)

Alzheimer's disease: 0.60(0.05)

Drug use

Caffeine use: 0.07(?)

Marijuana ever: 0.06(0.102), 0.25(0.088)

Smoking ever: 0.19(0.087)

Smoking, current: 0.24(0.096), 0.19(0.102), 0.18(0.16), 0.19(0.04)

alcohol

alcohol consumption: 0.14(0.071), 0.16(0.16)0.19 (0.11)

alcohol dependence: 0.08(0.107), 0.12(0.16), 0.235(0.03)0.02(0.10)

maximum drinks: 0.01(0.12)

Illicit Drugs: 0.37(0.102), 0.22(0.16),

DSM-IV drug dependence diagnoses (DD): 0.36(0.13)

factor score based on problem use (PU; i.e. 1+ DSM-IV symptoms): 0.25(0.13)

drug dependence vulnerability (DV; a ratio of DSM-IV symptoms to the number of substances used): 0.33(0.13)

Disease

Allergic rhinitis: 0.074(0.015)

Amyotrophic lateral sclerosis: 0.085(0.005)

Asthma: 0.264(0.067), 0.152(0.018)

Airway hyperresponsiveness (AHR): 0.45(0.29)

Serum total IgE (IGE): 0.53(0.27)

Eosinophil count (EOS): 0.29(0.32)

Pre-bronchodilator FEV1: 0.81(0.22)

Post-bronchodilator FEV: 0.83(0.22)

Bronchodilator response (BDR): 0.67(0.24)

Steroid responsiveness endophenotype (SRE): 0.00(0.42)

Normal lung growth only: 0.47(0.27)

Normal lung growth with early decline: 0.55(0.23)

Reduced lung growth only: 0.49(0.26)

Reduced lung growth with early decline: 0.17(0.27)

Early decline with normal or reduced lung growth: 0.22(0.28)

Reduced lung growth with or without early decline: 0.95(0.19)

hay fever: 0.53(0.05)

Multiple sclerosis: 0.19(0.009), 0.3(0.02), 0.19(0.009)

autoimmune Systemic RA+SLE+SSc+AS (rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, ankylosing spondylitis): 0.2(0.048)

T-cell mediated autoimmune disease: 0.192(0.033)

Crohn's disease: 0.18 0.024), 0.61(0.08), 0.54(0.06), 0.18(0.024), 0.46(0.020)

Ulcerative colitis: 0.17(0.017), 0.17(0.017)

psoriasis: 0.349(0.06)

celiac disease: 0.33(0.042)

Macular degeneration: 0.242(0.029), 0.36(0.016)

Arthritis: 0.11(0.031), 0.57(0.06), 0.098(0.014), 0.11(0.031), 0.126(0.026)/0.261(0.061), 0.32(0.037)

Osteoporosis: 0.195(0.024)

Ankylosing spondylitis: 0.18(0.028)

breast cancer (BC): 0.117(0.051), 0.57(0.11)/0.32(0.17)

prostate cancer (PC): 0.204(0.056), 0.30(0.06)

Hematoma volume: 0.60(0.70)

Intracerebral hemorrhage mortality: 0.40(0.70)

Intracerebral hemorrhage risk: 0.44(0.21)

Dyslipidemia: 0.263(0.014)

HIV viral load: 0.084(0.04)

esophageal adenocarcinoma: 0.0(0.21)

Barrett's esophagus: 0.35(0.06)

Gastroesophageal reflux disease: 0.25(0.05)

Heart-related

Hypertension: 0.42(0.06), 0.255(0.014), 0.37(0.053), 0.60(0.089)

in pregnancy: 0.083(0.043)

fasting triglycerides (TG): 0.16(0.05), 0.31(0.061)

Total cholesterol: 0.15(0.061)

fasting high-density lipoprotein (HDL): 0.12(0.05) 0.24(0.061), 0.45(0.017)

low density lipoprotein cholesterol (LDL): 0.16(0.061), 0.199(0.063)

systolic blood pressure (SBP): 0.24(0.05)

Cardiovascular disease: 0.092(0.015)

Coronary artery disease: 0.30(0.058), 0.39(0.06), 0.31(0.057), 0.146(0.017), 0.41(0.067)

Ischemic stroke:

all: 0.379(0.052)

large-vessel disease: 0.403(0.076)

small-vessel disease: 0.161(0.077)

Cardioembolic stroke: 0.326(0.074)

Diabetes-related

Diabetes Type I: 0.13(0.030), 0.28 (0.04), 0.73(0.06), 0.13 (0.030)

Diabetes type II: 0.35(0.06), 0.297(0.022), 0.37(0.065), 0.254(0.041), 0.36(0.066), 0.286(?), 0.51(0.065)

Fasting glucose: 0.198(0.075), 0.22(0.059), 0.10(0.05), 0.17(0.061)

HbA1c: 0.20(0.061)

fasting insulin (INS): 0.202(0.075), 0.09(0.05)

Biological

QT interval (QTi): 0.209(0.050)

von Willebrand factor (vWF): 0.252(0.051)

Hemoglobin: 0.21(0.061)

Cystatin: 0.27(0.061)

Creatinine: 0.18(0.061)

estimated glomerular filtration rate (eGFR): 0.32(0.061)

Vitamin D blood levels: 0.23(0.147)

Epigenetic age acceleration: 0.41(?)

Amyloid plaque: 0.03(?)

Neuritic plaque: 0.05(?)

Diffuse plaque: 0.38(?)

Neurofibrillary tangles (NFT): 0.00(?)

thyroid hormone levels:

TSH: 0.24(0.255)

FT4: 0.20(0.306)

HOMA-IR: 0.209(0.075)

HOMA-B: 0.187(0.077)

Apolipoprotein A1: 0.17(0.061)

Apolipoprotein B: 0.14(0.071)

C-reactive protein: 0.37(0.061)

Immunoglobulin A: 0.24(0.061)

monocyte white blood cell count: 0.343(0.032)

recombination rate: 0.099(0.023)

telomere length: 0.31(0.14)

Neanderthal admixture

Neanderthal admixture as a risk factor for:

Mood disorders

Depression

Actinic keratosis

Seborrheic keratosis

Obesity

Overweight

Acute upper respiratory infections

Coronary atherosclerosis

Hypercoagulation

Tobacco use

Type 2 diabetes

Crohn's disease

Lupus

Biliary cirrhosis

Infertility

Animal/plant

Boar taint: 0.118(0.064)

Merino sheep body size: ?

Mosquito behavior:

host preference (cattle vs human): 0.94(3.47)

resting behavior (indoors vs outdoors): 0.05(2.34)

Cassava resistance to Cassava mosaic disease: 0.51