Exactly what we analyse

No buying blind, and no black box. Here is every gene, condition and trait we examine, by name, and what each kind of file can tell you. Both the counts and the names are generated from the same registries the pipeline runs, so this page can't quietly disagree with your report.

What each file type can tell you

Version-stamped (2026-08-08).

What each file type can tell you, module by module
What you get	Chip	WGS	WGS + Deep Read
Clinical findings	2 examined	39 examined	39 examined
Carrier status	Not available	55 examined	55 examined
Pharmacogenomics	8 examined, see note 1	8 examined	8 examined
Traits	26 examined	26 examined	26 examined
Frontier	38 examined	38 examined	38 examined
The Fringe	20 examined	20 examined	20 examined
Archaic ancestry	9 examined	9 examined	9 examined
Polygenic scores	6 examined	6 examined	6 examined
Ancestry: maternal (mtDNA)	11 examined	11 examined	11 examined
Ancestry: paternal (Y-DNA)	14 examined, see note 2	14 examined	14 examined
Ancestry composition	30 examined	30 examined	30 examined
APOE risk allele (e4)	Included	Included	Included
Lp(a) level	Included	Included	Included
Hemochromatosis (HFE) variants	Included	Included	Included
ABO blood type	Not available	Included	Included
APOL1 risk variants	Included	Included	Included
Celiac HLA types (DQ2.5 / DQ8)	Included, see note 3	Included	Included
Deep Read: 29-target PGx panel + callability	Not available	Not available	29 examined
Recurrent deletion calls (from read depth)	Not available	Not available	2 examined

Show all 19 modulesShow fewer

Note 1: Array-dependent: only the genes whose defining SNPs your chip both typed and could resolve. DPYD needs a sequenced file.
Note 2: Needs your export's genome build, which 23andMe and AncestryDNA files give us. MyHeritage and FamilyTreeDNA exports don't state it, so for those we report the paternal line as unreadable rather than guess at it.
Note 3: Array-dependent: both marker positions must be on your chip and readable from it. When they are not, the result reads as not called rather than as a negative.

A number is how many items we examine; means included; a dash () means that file type can't deliver it. Counts are the most a file of that type can give you, not a promise for every file.

What those counts sit on: the panel is 1,042 regions of GRCh38, and ClinVar 2026-07 lists 482,198 classified variants inside them, 74,729 of them pathogenic or likely pathogenic with review criteria. Every variant in your file that falls inside those regions is annotated against that list before any module decides what is reportable. That is the examined set; the module counts are what we have chosen to report from it.

Medications we cover

The Deep Read pharmacogenomic panel restates CPIC guidance for 74 medications across the 29-target panel. It is the same kind of pharmacogenomic read a test like GeneSight sells, run from aligned-reads (BAM/CRAM) data you already own. Here is every medication it covers, with the gene(s) that inform it.

Named services are described from their public product pages; features change and we don't speak for them.

aminosalicylic acid G6PD
amitriptyline CYP2C19
aspirin G6PD
atazanavir UGT1A1
atorvastatin SLCO1B1
azathioprine NUDT15TPMT
capecitabine DPYD
chloramphenicol G6PD
chloroquine G6PD
ciprofloxacin G6PD
citalopram CYP2C19
clomipramine CYP2C19
clopidogrel CYP2C19
Codeine / tramadol CYP2D6
dapsone G6PD
desflurane CACNA1S
dexlansoprazole CYP2C19
dimercaprol G6PD
doxepin CYP2C19
doxorubicin G6PD
efavirenz CYP2B6
enflurane CACNA1S
escitalopram CYP2C19
fluorouracil DPYD
fluvastatin SLCO1B1
furazolidone G6PD
glyburide G6PD
halothane CACNA1S
hydralazine NAT2
hydroxychloroquine G6PD
imipramine CYP2C19
isoflurane CACNA1S
lansoprazole CYP2C19
lovastatin SLCO1B1
mafenide G6PD
mercaptopurine NUDT15TPMT
methoxyflurane CACNA1S
methylene blue G6PD
nalidixic acid G6PD
nitrofurantoin G6PD
norfloxacin G6PD
NSAIDs (e.g. celecoxib, ibuprofen) CYP2C9
ofloxacin G6PD
omeprazole CYP2C19
pantoprazole CYP2C19
pegloticase G6PD
phenazopyridine G6PD
pitavastatin SLCO1B1
pravastatin SLCO1B1
primaquine G6PD
quinine G6PD
rasburicase G6PD
rosuvastatin ABCG2SLCO1B1
sertraline CYP2B6CYP2C19
sevoflurane CACNA1S
simvastatin SLCO1B1
succinylcholine CACNA1S
sulfadiazine G6PD
sulfadimidine G6PD
sulfamethoxazole / trimethoprim G6PD
sulfanilamide G6PD
sulfasalazine G6PD
sulfisoxazole G6PD
tacrolimus CYP3A5
tafenoquine G6PD
Tamoxifen CYP2D6
thioguanine NUDT15TPMT
tolbutamide G6PD
toluidine blue G6PD
trimipramine CYP2C19
vitamin c G6PD
vitamin k G6PD
voriconazole CYP2C19
Warfarin CYP2C9

Many of these have a plain-language page of their own, from either direction: genes and your medications, carrier conditions, or medications and your DNA.

Coverage means the panel reads the gene that informs this drug; it is decision support for you and a prescriber, never an instruction to start, stop or change a medication. Restated from CPIC, never a diagnosis.

Get the report + Deep Read bundle · €99 (saves €19) Deep Read runs from a BAM/CRAM after your report; the bundle covers both.

Every gene, condition and trait, by name

Grouped by module, clinical first. Everything here is in the €39 whole-genome report, except the Deep Read panel, an add-on for aligned-reads (BAM/CRAM) files. A genotyping-chip file covers a lighter subset (see the table above). The absence of a finding is not a clean result; these are curated, reviewed panels, not a comprehensive genome-wide screen.

Clinical findings

Restated from ClinVar · ACMG SF v3.3

Pathogenic / likely-pathogenic variants in these genes, framed as personal risk. Needs a sequenced genome (WGS); a chip reads only a couple of these.

X-linked adrenoleukodystrophy ABCD1
Arrhythmogenic right ventricular cardiomyopathy PKP2, DSP, DSC2, DSG2, TMEM43
Biotinidase deficiency BTD
Cerebrotendinous xanthomatosis (CTX) CYP27A1
Catecholaminergic polymorphic ventricular tachycardia (CPVT) RYR2, CASQ2, TRDN
Dilated cardiomyopathy (inherited) LMNA, FLNC, TTN, BAG3, DES, RBM20, PLN
Fabry disease GLA
Factor V Leiden thrombophilia F5
Familial hypercholesterolemia LDLR, APOB, PCSK9
Familial adenomatous polyposis APC
Hereditary Breast and Ovarian Cancer BRCA1, BRCA2
Hereditary paraganglioma-pheochromocytoma SDHB, SDHC, SDHD, SDHAF2, MAX, TMEM127
Heritable thoracic aortic disease TGFBR1, TGFBR2, SMAD3, ACTA2, MYH11
Hereditary haemorrhagic telangiectasia (HHT) ACVRL1, ENG
Hypertrophic cardiomyopathy MYH7, MYBPC3, TNNT2, TNNI3, TPM1, MYL3, MYL2, ACTC1, TNNC1, PRKAG2
Juvenile polyposis syndrome BMPR1A, SMAD4
Li-Fraumeni syndrome TP53
Long QT syndrome KCNQ1, KCNH2, SCN5A, CALM1, CALM2, CALM3
Lynch syndrome MLH1, MSH2, MSH6, PMS2, EPCAM
Malignant hyperthermia susceptibility RYR1, CACNA1S
MUTYH-associated polyposis MUTYH
Marfan syndrome FBN1
Multiple endocrine neoplasia type 1 MEN1
Multiple endocrine neoplasia type 2 RET
Maturity-onset diabetes of the young (HNF1A-MODY) HNF1A
NF2-related schwannomatosis (neurofibromatosis type 2) NF2
Ornithine transcarbamylase (OTC) deficiency OTC
Peutz-Jeghers syndrome STK11
Pompe disease GAA
Prothrombin G20210A thrombophilia F2
PTEN hamartoma tumour syndrome PTEN
Hereditary retinoblastoma RB1
RPE65-related retinal dystrophy RPE65
Hereditary transthyretin amyloidosis TTR
Tuberous sclerosis complex TSC1, TSC2
Vascular Ehlers-Danlos syndrome COL3A1
Von Hippel-Lindau syndrome VHL
Wilson disease ATP7B
WT1-related Wilms tumour predisposition WT1

Carrier status

Restated from ACMG Tier 3 · ACOG carrier panel

A recessive-lens read for family planning. Needs WGS.

MCAD deficiency carrier ACADM
Aspartylglucosaminuria (AGU) carrier AGA
APECED (autoimmune polyendocrinopathy) carrier AIRE
Canavan disease carrier ASPA
Citrullinemia type I carrier ASS1
Wilson disease carrier ATP7B
Maple syrup urine disease carrier BCKDHB
GRACILE syndrome carrier BCS1L
Bloom syndrome carrier BLM
Biotinidase deficiency carrier BTD
Cystic fibrosis carrier CFTR
CLN5 disease carrier CLN5
Northern epilepsy (CLN8) carrier CLN8
Usher syndrome type 3A carrier CLRN1
Cystinosis carrier CTNS
Imerslund-Gräsbeck syndrome (B12 malabsorption) carrier CUBN
Cerebrotendinous xanthomatosis (CTX) carrier CYP27A1
Smith-Lemli-Opitz syndrome carrier DHCR7
Familial dysautonomia carrier ELP1
Tyrosinemia type I carrier FAH
Fanconi anemia group C carrier FANCC
Glycogen storage disease type Ia carrier G6PC1
G6PD deficiency G6PD
Pompe disease carrier GAA
Krabbe disease carrier GALC
Galactosemia carrier GALT
Gaucher disease carrier GBA1
Non-syndromic hearing loss carrier (DFNB1) GJB2
Lethal congenital contracture syndrome 1 (LCCS1) carrier GLE1
Sickle cell & β-thalassemia carrier HBB
Tay-Sachs disease carrier HEXA
Hydrolethalus syndrome (HLS) carrier HYLS1
Mucopolysaccharidosis type I carrier IDUA
Cornea plana 2 (CNA2) carrier KERA
Congenital lactase deficiency carrier LCT
Mucolipidosis IV carrier MCOLN1
Meckel syndrome, Finnish type (MKS1) carrier MKS1
Congenital nephrosis, Finnish type (CNF) carrier NPHS1
Gyrate atrophy of the choroid and retina carrier OAT
Phenylketonuria (PKU) carrier PAH
Muscle-eye-brain disease (MEB) carrier POMGNT1
Infantile neuronal ceroid lipofuscinosis (CLN1) carrier PPT1
RAPADILINO syndrome carrier RECQL4
Cartilage-hair hypoplasia (CHH) carrier RMRP
Salla disease carrier SLC17A5
Diastrophic dysplasia (DTD) carrier SLC26A2
Congenital chloride diarrhea (CCD) carrier SLC26A3
Pendred syndrome / hearing-loss carrier SLC26A4
Lysinuric protein intolerance (LPI) carrier SLC7A7
Niemann-Pick disease type A/B carrier SMPD1
Mulibrey nanism carrier TRIM37
Infantile-onset spinocerebellar ataxia (IOSCA) carrier TWNK
Usher syndrome type 2A carrier USH2A
Cohen syndrome carrier VPS13B
PEHO syndrome carrier ZNHIT3

Pharmacogenomics

Restated from CPIC guidelines

How your genotype may affect specific medicines, restated from CPIC. On a chip, only the genes whose defining SNPs your array both typed and could resolve; DPYD needs a sequenced file (VCF/gVCF) or Deep Read.

CYP2C19
CYP2C9
CYP4F2
DPYD
HLA-B
NUDT15
SLCO1B1
VKORC1

Single-gene reads

Restated from ClinVar · peer-reviewed literature

APOE Alzheimer's-risk ε-genotype (hidden until you choose to see it)
Lp(a) Lipoprotein(a) cardiovascular-risk level
HFE Hereditary haemochromatosis (iron overload)
ABO ABO blood group, not a blood test
APOL1 APOL1 kidney-risk variants
Celiac HLA DQ2.5 / DQ8 types: a rule-out read, not a risk score

Traits

Restated from NHGRI-EBI GWAS Catalog

Benign, well-replicated traits that clear a strict evidence gate.

Alcohol flush response
Alcohol metabolism speed (ADH1B)
Asparagus urine smell detection
Bitter taste sensitivity (TAS2R38)
Caffeine metabolism
Chronotype (morning vs evening tendency)
Cilantro (coriander) soapy taste
Coffee consumption tendency (AHR)
Duffy blood-group antigen (malaria resistance & neutrophil baseline)
Earwax type & body odour
Eye colour (blue vs brown)
Freckling tendency (IRF4)
FUT2 secretor status (norovirus resistance, gut microbiome, B12)
Hair colour shade (KITLG)
Hair thickness & shape
Straight vs curly hair (TCHH)
Lactase persistence
Androgenetic hair thinning tendency (TWIST2)
Muscle fibre type (ACTN3)
Omega fatty acid desaturation (FADS1)
Photic sneeze reflex (ACHOO)
Red hair and fair skin tendency (MC1R)
Sleep duration (PAX8)
Tan or burn: tanning response (SLC45A2)
Vitamin B12 tendency (FUT2)
Vitamin D tendency (GC / VDBP)

Polygenic risk scores

Restated from PGS Catalog

A tendency across many variants and a percentile against a reference group, never a personal probability of disease. Hidden until you choose to see it.

Atrial fibrillation
Breast cancer
Coronary artery disease
Prostate cancer
Type 2 diabetes
Venous thromboembolism

Deep ancestry

Restated from PhyloTree

One thread of your tree per line, as global haplogroups, never a “percent ancestry” pie.

Maternal line, mtDNA haplogroup 11 clade families
Paternal line, Y-DNA haplogroup 14 clade families

Frontier

Restated from GWAS Catalog · emerging research

Exploratory; each reading labelled by how much evidence backs it. Walled off from the clinical report, for curiosity, never medical.

Endurance vs power: the ACE 'sport gene'
ACTN3: fast-twitch 'sports gene' (sprint/power vs endurance)
ADORA2A: caffeine sensitivity & sleep
Alcohol flush: acetaldehyde clearance (ALDH2)
Alcohol metabolism speed (ADH1B)
BDNF: Val66Met, learning & neuroplasticity
Beta-carotene converter status (BCO1)
Risk-taking & processing speed (CADM2)
Caffeine metabolism: fast or slow (CYP1A2)
CCR5Δ32: the HIV-resistance deletion (read via a proxy SNP)
CLOCK: chronotype (morningness / eveningness)
Tendon stiffness & flexibility (COL5A1)
COMT: dopamine clearance ('warrior / worrier')
DRD2 / ANKK1: Taq1A, dopamine reward signalling
Novelty-seeking, the contested version (DRD4)
Anandamide tone (FAAH)
Omega-3/6 conversion efficiency (FADS1)
Stress-axis regulation, contested (FKBP5)
FTO: appetite & body-weight set-point
IGF2BP2: a lead type 2 diabetes marker
HDL & longevity curio (CETP I405V)
Longevity-associated FOXO3 variant
Klotho KL-VS: heterozygote-advantage curio
The 'warrior gene' myth (MAOA)
KIBRA memory-performance variant (WWC1)
MTHFR: C677T, the most over-hyped variant in consumer genetics
Glucose & melatonin crossover (MTNR1B)
Nicotine dependence signal (CHRNA5)
Mu-opioid receptor variant (OPRM1 A118G)
OXTR: rs53576, the oxytocin receptor & social behaviour
PPARGC1A: Gly482Ser, endurance & mitochondria
Secretor status: norovirus and B12 (FUT2)
Natural short sleeper: ADRB1
Natural short sleeper: DEC2 (BHLHE41)
Serotonin transporter (5-HTTLPR): the famous null
Glucose handling: the strongest common T2D variant (TCF7L2)
Vitamin-D tendency: activation enzyme (CYP2R1)
Vitamin-D tendency: binding protein (GC)

The Fringe

Restated from GWAS · speculative

The popular, playful gene-quirks, the deepest end of the firewall. For fun only.

Asparagus pee: can you smell it? (OR2M7 cluster)
Bitter taste: PTC taster or not (TAS2R38)
Blond vs darker hair (KITLG)
Cilantro tastes like soap (OR6A2)
Wet or dry earwax (ABCC11)
Blue or brown eyes (HERC2)
Can you taste fat? (CD36)
Freckles & premature greying (IRF4)
Rage at chewing sounds: misophonia (TENM2)
Motion sickness (carsick / seasick tendency)
Sun sneeze: the ACHOO reflex (near ZEB2)
Red hair & freckling (MC1R R151C)
Chili-heat sensitivity (TRPV1)
How sweet is sweet? Sucrose sensitivity (TAS1R3)
Sweet tooth (FGF21 'sugar hormone')
Tongue taste-bud density: gustin (CA6)
Hair thickness and shovel-shaped teeth (EDAR)
Umami (savory) taste sensitivity (TAS1R1)
Eyebrow convergence: the 'unibrow' nudge (PAX3)
Can you smell violets? Beta-ionone (OR5A1)

Deep Read: 29-target pharmacogenomic panel Deep Read add-on

Restated from CPIC · PyPGx (from your aligned reads)

Resolved from aligned reads (BAM/CRAM), including CYP2D6 star-alleles a variant file can't call, with a measured per-gene callability map.

ABCB1
ABCG2
ALDH2
BCHE
CACNA1S
CYP1A2
CYP2B6
CYP2C19
CYP2C8
CYP2C9
CYP2C_rs12777823
CYP2D6
CYP3A4
CYP3A5
CYP4F2
DPYD
F2
F5
G6PD
GRIK4
IFNL3
MTHFR
NAT2
NFIB
NUDT15
SLCO1B1
TPMT
UGT1A1
VKORC1

Recurrent deletion calls Deep Read add-on

Restated from ClinVar · read depth (mosdepth)

Whole-gene and multi-exon deletions, called by comparing read depth against a baseline rather than by reading genotypes. A variant file cannot deliver these: a deletion shows up there as an absence of calls, which is indistinguishable from a region that simply was not read. Needs aligned reads (BAM/CRAM).

TYROBP Nasu-Hakola disease (PLOSL)
CLN3 Juvenile neuronal ceroid lipofuscinosis (CLN3 / Batten disease)

See what your own file can tell you →

The Finnish read

Finland is a genetic isolate, and that changes two separate things about a re-analysis. They are worth keeping apart, because rolling them together would promise more than we do.

Which conditions we screen

26 of the 55 carrier conditions on the panel are Finnish Disease Heritage founder conditions: recessive diseases that are rare worldwide but concentrated here, because a small founding population carried them forward. They are on the panel for everyone, whatever your ancestry. The panel does not change per customer; it was chosen knowing who is likely to be reading.

AGA · AIRE · BCS1L · CLN5 · CLN8 · CLRN1 · CUBN · GLE1 · HYLS1 · KERA · LCT · MKS1 · NPHS1 · OAT · POMGNT1 · PPT1 · RECQL4 · RMRP · SLC17A5 · SLC26A2 · SLC26A3 · SLC7A7 · TRIM37 · TWNK · VPS13B · ZNHIT3

How a frequency gets read

How rare a variant is may be the most useful number on a finding, and "rare" means nothing without saying "rare among whom". You pick the population it is read against: Global, Finnish, European. Findings then carry that population's frequency alongside the global one, so a variant that is unremarkable worldwide and common in Finland reads as what it is.

The lens is display only. It never changes how a variant is classified: classification always uses the global frequency, so switching the lens cannot turn a benign result into a worrying one, or the reverse. We store the figures for 10 populations, so more lenses can be added without re-running your file.

Where a published Finnish population figure exists, the card carries it: 9 conditions have one today, each pinned to the source it came from and labelled with how good that source is. A condition with no defensible Finnish figure gets no figure, rather than a borrowed one.

Finnish prevalence set 2026-07-27. Frequencies are from gnomAD. Population data describes groups, never you: a frequency is context for reading a result, not a result about your ancestry.

What we looked at and decided not to report

A coverage page that only lists what a test includes is half the picture. These 5 traits are reported by other services. We assessed each one against the same evidence bar the traits above clear. None of them held up, so you get the reason instead of the result. The list is published here for the same reason it ships inside the report: the reasoning is the part worth paying for.

Sweet preference Hwang LD et al., Am J Clin Nutr 2019 (PMID:31005972)
The largest study of sweet taste found only one genome-wide-significant signal, and it is not a taste gene. The hit is rs11642841 in FTO (P = 3.8e-8), a variant best known for its association with body weight, and it was found for how much sugar people eat rather than how sweet they find it. The sweet-taste receptor genes themselves (TAS1R2, TAS1R3, GNAT3) had, in the authors' own words, limited support across all three European samples, and every sweet-perception result was below genome-wide significance. Reporting this would mean relabelling a body-weight variant as a taste result.
Motion sickness Hromatka BS et al., Hum Mol Genet 2015 (PMID:25628336)
The evidence is statistically strong but has never been independently repeated. The one study surveyed 80,494 people about car sickness and found 35 variants past the genome-wide threshold, the strongest at P = 4e-44 — but it describes itself as the first such study, it draws on a single customer database, and the trait is self-reported. Our gate asks for at least two independent groups of people before we report a result, and that is the criterion we will not bend: a finding from one cohort that no one else has reproduced is a promising lead, not a result about you.
Hangover response
There is no study to assess. The GWAS Catalog holds no genome-wide association study for hangover under any name, so there is no published, replicated evidence linking specific variants to how badly a person suffers after drinking. What we can say about alcohol from your file we already say: the alcohol-flush response (ALDH2) and alcohol metabolism (ADH1B) are both reported, and they are the parts with real evidence behind them.
HIV resistance (CCR5-delta-32) Samson M et al., Nature 1996 (PMID:8898206); Liu R et al., Cell 1996 (PMID:8756719)
The biology here is real and unusually well understood: people who carry two copies of a 32-base-pair deletion in CCR5 lack the surface receptor that the common strain of HIV uses to enter cells, and are strongly protected against infection by it. We still do not report it as a trait, and the reason is that we cannot read it directly. The deletion is not a single-letter change, and a standard variant file does not call it. What we would actually be reading is a nearby single-letter marker that usually travels with the deletion, and then inferring the deletion from that. The inference is right most of the time and wrong some of the time, and nothing in your file tells you which case you are in. Our gate asks that a trait be callable directly from your data, and for a result this consequential we would rather report nothing than report a guess. The marker remains visible in the Frontier tier, where the inference is labelled as an inference.
Unibrow (eyebrow convergence)
There is no catalogued study to assess. The marker consumer sites cite for this trait, rs2395845, is not in the GWAS Catalog at all — the catalog has no record of it, so there are no published association statistics for us to check it against. The underlying claim traces to a single study of facial features in a Latin American cohort, which is interesting work, but it is one cohort, and our gate asks for at least two independent ones before we describe a result as being about you. With no catalog entry and no replication, there is nothing here we can stand behind.